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Article

Land Use and Land Cover in Tropical Forest: Global Research

by
Andrés Velastegui-Montoya
1,2,3,*,
Néstor Montalván-Burbano
2,4,*,
Gina Peña-Villacreses
5,
Aline de Lima
3 and
Gricelda Herrera-Franco
6,7
1
Facultad de Ingeniería en Ciencias de la Tierra (FICT), ESPOL Polytechnic University, Guayaquil P.O. Box 09-01-5863, Ecuador
2
Centro de Investigación y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), ESPOL Polytechnic University, Guayaquil P.O. Box 09-01-5863, Ecuador
3
Geoscience Institute, Federal University of Pará, Belém 66075-110, Brazil
4
Department of Business and Economics, University of Almería, 04120 Almería, Spain
5
Facultad de Ciencias de la Vida (FCV), ESPOL Polytechnic University, Guayaquil P.O. Box 09-01-5863, Ecuador
6
Facultad de Ciencias de la Ingeniería, Universidad Estatal Península de Santa Elena (UPSE), La Libertad 240204, Ecuador
7
Geo-Recursos y Aplicaciones GIGA, Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador
*
Authors to whom correspondence should be addressed.
Forests 2022, 13(10), 1709; https://doi.org/10.3390/f13101709
Submission received: 10 August 2022 / Revised: 11 October 2022 / Accepted: 12 October 2022 / Published: 17 October 2022
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

Abstract

:
Tropical ecosystems play an important role in the environment. They provide multiple ecosystem services, such as carbon capture and sequestration, food supply, and climate regulation. Studying land use and land cover change makes it possible to understand the land’s alterations associated with deforestation, degradation, erosion, soil desertification, and biodiversity loss. The objective of this study is to evaluate the different approaches to land use and land cover research in tropical forests based on the evolutionary and qualitative analysis of the last 44 years of scientific production. The data were collected using the Scopus database and was based on the PRISMA methodology’s four phases: (i) identification, (ii) screening, (iii) eligibility, and (iv) included. The results showed a significant increase in the study of land use and land cover consolidated in 4557 articles, with contributions from 74 countries, revealing 14 themes and seven lines of research. Core research areas such as biodiversity, land use, and conservation exist due to the ongoing interest in the value of tropical forests and their response to climate change. The present research allowed us to consider future study topics such as the relationship between sustainable development goals and land use and cover in tropical forests, as well as the evaluation of the environmental impact of economic activities in forests.

1. Introduction

Tropical forests differentiate between neotropical (New World) and paleotropical (Old World) [1,2]. They are mainly found between the Tropic of Cancer and the Tropic of Capricorn, but they can extend outside both tropics to Brazil’s southeast and the northeast of Australia [3]. Tropical forests are distributed across four biogeographical areas: the neotropics (South America, Central America, southern Mexico, and the Caribbean islands), which contain 45% of the forests; the Afrotropics (continental Africa, Madagascar, and scattered islands), representing 30%; Asia (India, Sri Lanka, mainland Asia, and southeast Asian islands) with 16%; and Australasia (Australia, New Guinea, and the Pacific Islands) 9% [4]. They are biodiversity centers and serve as home to more than 50% of the planet’s flora and fauna species [5], making them an ecologically unique zone [6]. In addition, they store around a quarter of the world’s terrestrial carbon reserve [7] and play an essential role in mitigating the effects of climate change due to the amount of carbon they store [8].
Tropical forests are classified according to their elevation, annual rainfall, and degree of seasonality. They fall into four main types: ever-humid, semi-green, dry, and montane deciduous forests [9]. Ever-humid forests, also called tropical rainforests, occupy approximately 30% of the tropical forest biome [10], have a high annual rainfall (>2000 mm of rain per year) [11], and are characterized by their unique species and their ecological interactions and specializations [12,13]. On the other hand, semi-green or seasonally humid forests, like rainforests, receive more than 2000 mm of rain per year [14], and their rainy periods are usually longer than their dry periods [15]. This type of forest comprises about 42% of tropical forests [16] and is known for losing its foliage during the dry season [17]. Finally, the main characteristic of montane forests is their variable altitudinal range (800–3500 m), with a low amount of radiation and high persistence of cloudiness in the upper limits [18,19], which allows for constant contact with water, making this environment play an essential role in the hydrological cycle [20,21].
The Amazon houses approximately 30% of the world’s tropical forests [22]. It is the largest tropical forest in the world [23] and extends through Brazil, Colombia, Peru, Venezuela, Ecuador, Bolivia, Guyana, Suriname, and French Guiana [24]. About 60% of the Amazon is located in Brazil, 9% in Peru, and 6% in Colombia; the remaining 25% is in other countries [25]. It is characterized by maintaining a great taxonomic diversity in fauna and flora and being home to several indigenous communities [26].
Tropical forests play a vital role in the conservation of biodiversity, climate change, and the regulation of the water cycle in the region [27]. They are important given the variety of ecosystem services they provide regarding regulation, provision, support, and culture. These ecosystem services include food provision, pest control, and ecotourism [28,29]. In addition, they have global importance in biogeochemical cycles due to their productivity and activity in the water and carbon cycle [30,31]. Locally, they favor the maintenance of soil stability through their roots and inhibit surface washing and erosion [32]. Furthermore, they help regulate floods, act as climate regulators, and help maintain air, water, and soil quality [33,34]. These services result from the interactions between nature and societies; they provide economic, environmental, and social wellbeing and serve as a tool to inform decisions about the use and management of the planet’s resources [35,36]. Moreover, the fewer disturbances tropical forests experience, the greater their capacity to provide varied and quality ecosystem services will be [37].
Land use and land cover (LULC) changes directly or indirectly transform the quality of the tropical forests and the resources they provide [38]. The development of different activities in tropical forests, such as the exploitation of natural resources [39,40], road construction [41], the construction of energy infrastructures [42,43,44], and urban expansion [45] cause forests degradation and generate negative impacts on an environmental, economic, social, and political level [46,47]. All of these activities classify as different land uses that determine the land cover [48] and can influence the water availability in a region [49,50]. They can also alter the richness and biodiversity of the area [51], especially in sectors rich in biodiversity, such as tropical forests [52]. LULC change can also affect soil respiration [53] and influence global food security [54]. In addition, forest loss can degrade their water regulation function, river flow [55], and modulation of precipitation patterns [56].
Land use and land cover is commonly used to describe the Earth’s surface coverage [57]. It serves as one of the parameters for correctly identifying exploitation activities through land management changes [58]. LULC changes can cause negative impacts at the urban level, such as reduced air quality, the generation of heat islands, and decreased landscape quality [59,60]. Likewise, it can compromise the ecosystem services rural areas provide [61].
LULC studies can help manage natural resources sustainably [62]. Moreover, LULC change analysis facilitates the analysis and problem solving of current issues such as policy planning in case of diseases [63], urban planning [64], climate change trends [65], sustainable practices for the development of agriculture [66], and in the proposal of public policies for environmental conservation [67].
Tropical forests have been the subject of studies regarding LULC due to their ecological, economic, and social importance. In these studies, various areas are analyzed; some focus on the relationship between LULC and climate change and its effects on the environment [68,69] or on mapping and monitoring forests using remote sensing to analyze LULC and determine implications [70,71]. Other studies include the impacts of different land use in protected areas as a tool to establish conservation policies [72,73]. Some authors, such as Arantes [74], studied the relationship between plant cover and fish biodiversity to find out how LULC affects their ecology. Others, like Chapman, focus on the impact caused by land use change on the phylogenetic diversity of birds due to the importance of these species in pest control [75]. The complexity of the different approaches associated with LULC in tropical environments shows the progress made and the contribution to the discussion of its future impacts and consequences, justifying a systematic review of the subject to contextualize this evolution through time.
The constant growth of published information in the form of academic texts requires knowledge of the tools to measure the performance of the literature [76]. Therefore, a study that includes a better interpretation of the scientific production of LULC in tropical forests would complement previous research and allow the exchange of knowledge, methods, experiences, ideas, and models, supporting tropical forests’ conservation and sustainable management. Furthermore, bibliometrics has played an essential role in studying the global dynamics of an area of knowledge by evaluating and comparing scientific production [77]. In general, bibliometric methods facilitate the evaluation of central research themes and the impact of scientific publications and researchers, and help organizations with the future prospecting of the field of study [78].
Over time, various studies on land cover and land use change in tropical regions have taken place, but only a few show this area’s growth through bibliometric analyses. Among those studies, there is the study of the causes and effects of fires in tropical forests [79], the remote sensing analysis of wetlands in South America [80], the studies of global deforestation trends [81], the analysis of LULC in the Amazon region [82], and a review on the dynamics of terrestrial and aquatic carbon in tropical peatlands [83]. The present article aims to evaluate the research on LULC in the framework of tropical forests to analyze the scientific production over the last forty-four years.
This study has five sections: the first section, or introduction, discusses the importance of tropical forest land use change, as well as the objective of the study; the second section contains materials and methods that show the database used, as well as the data search and processing; the third section includes results, detailing the main findings and their analysis; the fourth section focuses on the discussion, where the implications of the results are analyzed; and the fifth section includes the conclusions and limitations of the study.

2. Materials and Methods

2.1. Study Area

The study area comprises tropical forests located between the Tropics of Cancer (23.44° N) and Capricorn (23.44° S) (https://pb463.users.earthengine.app/view/fipnee2020, accessed on 5 August 2022) [84,85]. According to the Food and Agriculture Organization of the United Nations (FAO), this tropical area has the highest proportion of forests in the world (45%) [86].

2.2. Methods: Data Processing

Academic literature can be analyzed and explored through systematic and bibliometric analyses. The systematic approach includes rigorous techniques to make the data collection procedure transparent and easily replicated, thus reducing the bias in the information obtained [87,88]. This procedure is similar to bibliometric studies, where the data are verified, thus obtaining a broader vision than the systematic studies [76,78].
Bibliometric analysis can effectively describe a discipline’s state of knowledge, characteristics, and trends [89]. This study includes the quantitative and qualitative analysis of publications on a specific topic based on statistics and computer technology [90,91]. This technique has been widely used to assess various academic disciplines such as business and management [92,93], arts and humanities [94,95], earth sciences [96,97], and environmental sciences [98,99] among others.
Based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology (Figure 1), this study comprises four phases: (i) identification, (ii) screening, (iii) eligibility, and (iv) included. This methodology facilitates an objective, clear, and transparent scientific production analysis [100].

2.2.1. Stage I: Basis Query

This research aims to evaluate the land cover and land use changes in tropical forests worldwide through the bibliometric analysis of scientific publications. The search terms used are related to land use [101], land cover [102], and land use change [82]. These words were combined with the representative names of tropical ecosystems such as tropical forest, rainforest, and Amazon, which are also key terms frequently used to analyze advances and gaps in knowledge regarding restoration, carbon cycle, and knowledge production trends in these areas [103,104,105]. The selection of these criteria aided in defining the study area and compiling documents to evaluate this analysis.
Based on the above, the defined criteria aim to answer the following research questions (RQ):
  • RQ1: What are the scientific production trends regarding LULC in tropical forests?
  • RQ2: What are the most relevant publications and the countries that contributed to the growth of this field of study?
  • RQ3: Which authors and journals have the most influence in the progress of LULC research on tropical forests?
  • RQ4: What are the themes, topics, authors, and journals associated with the intellectual structure in this area?
The data collection occurred exclusively using the Scopus database, with a defined period from 1978 to 2021. The year 1978 was selected because it was when the first articles appeared in the database and the year 2022 was excluded, given that it is in progress at the time of this research.

2.2.2. Stage II: Data Search and Collection

Bibliometric studies generally need appropriate databases with complete and reliable information [106]. Scopus is a suitable database for studies related to bibliometric analysis due to its available bibliographic information [107]. This database has a significant number of unique sources in different research areas not covered by other databases [108], as well as a broad coverage in terms of years, journals, and languages [109]. In addition, Scopus contains links to author affiliation, which facilitates the analysis of researchers’ mobility [110]. It has a stricter review process that involves peer-reviewed scientific documents [111]. In addition, its scientific production quality indices are used by research groups such as CiteScore or Scimago Journal Rank (SJR) [112,113].
The article sample analyzed in this study was selected based on a search carried out in February 2022 in the Scopus database using the topic search: TS = ((TITLE-ABS-KEY (“land use”) OR TITLE-ABS-KEY (“land cover”) OR TITLE-ABS-KEY (“land use change”))) AND ((TITLE-ABS-KEY (“amazon*”) OR TITLE-ABS-KEY (“rain forest*”) OR TITLE-ABS-KEY (“tropical forest*”))) AND (EXCLUDE (PUBYEAR, 2022)). The search obtained a total of 5557 documents.
Documents that went through a general review process, such as editorials, books, and conference papers, were excluded [114]. Generally, articles and reviews are the most widely used documents in the scientific world because they deepen a researched phenomenon, are of greater length in terms of their content, and have been blind peer reviewed [115]. Therefore, excluding certain documents and only including articles/reviews is a commonly accepted approach to performing a literature review [116]. Under these criteria, 699 documents were excluded. In addition, the search only considered articles in English because it is the global language for scientific dissemination [117]. In fact, the vast majority of peer-reviewed scientific journals only publish articles written in English [105]. As a result, the search found 4597 articles.

2.2.3. Stage III Software and Data Selection

Once collected, the data from the Scopus database were exported to both a Microsoft Excel spreadsheet and a text document for its respective treatment [118]. The database includes information on different variables (authors, institutions, journals, language, keywords, abstract, references, among others) [119] that must be reviewed to obtain the greatest accuracy possible. During the review, the data are cleaned by removing duplicate files and incomplete or erroneous records [99,120]. Under these considerations, 20 documents were eliminated, obtaining 4557 papers.
The Microsoft Excel Office 365 MSO software (Version 2209) performed the descriptive statistical analyses of the studied variables [121]. In addition, the ArcGIS Pro software was used to make the maps, which obtained a cartographic representation that allowed the collection, organization, analysis, and visualization of geographic information [122,123].
The bibliometric maps were built with the VOSviewer tool to visually present the statistical results related to the distribution of authors, institutes, countries/territories, and keywords [124]. The software uses a technique called VOS, which minimizes a weighted sum of squared Euclidean distances between all pairs of elements through an optimization process. This mapping approach will enable elements to be placed on the map so that the distance between each pair of elements represents their similarity as accurately as possible [125]. In addition, it has “zooming” features, which facilitate the detailed examination of the map [126]. This software has been used in various bibliometric analyses performed in diverse subject areas [127,128,129].

2.2.4. Stage IV Data Interpretation

During the collected data analysis, two bibliometric approaches were used. The first bibliometric approach focuses on the performance of scientific production. The second bibliometric approach involves studying its intellectual structure through bibliometric mapping [130].
Performance analysis evaluates the impact of the scientific publications that make up this structure by contrasting information related to the number and year of publications, authors, affiliations, and journals [76]. Bibliometric mapping, or science mapping, creates a graphic representation of the fields and subfields of the research, facilitating their visualization and understanding of the relationships between them [131]. These maps show the relationships between variables, such as author keyword co-occurrence, co-citation with cited authors, and source citations [132,133]. This combination reveals the studied topic’s intellectual structure [134].

3. Results

3.1. Performance Analysis

3.1.1. Scientific Production Analysis (RQ1)

The scientific production of LULC in tropical forests comprises 4557 documents and 206,083 citations over 44 years (1978–2021). Initially, the studies on the subject were scarce; however, over time, the academic world seems to have increased its interest in the topic. This interest is evidenced in 2010, when 69.21% of the research on the topic started (see Figure 2). For analysis purposes, the scientific production was divided into four time periods: Period I (1978–1989), Period II (1990–1999), Period III (2000–2009), and Period IV (2010–2021). This division by decades facilitates the understanding of the field of study development [135]. The number of citations per period refers to the total number of citations received by the publications (articles and reviews) in that period.

Period I (1978–1989)

The first twelve years show the beginning of scientific production within the prescribed period of study. In this period, there are 65 publications (1.43% of the documents) that add up to 2942 (1.43%) citations. Before the 1980s (1978 and 1979), articles were published on the causes and consequences of rotational cultivation and population pressure in tropical areas [136,137], as well as regarding the importance of forestry for plant and animal conservation and agrosystems development [138,139]. It is important to note that this period is marked by the advancement of geotechnologies, with the increased use of satellite images (for example, Landsat) in mapping and environment monitoring. Among the outstanding publications of the decade is the use of satellite images and data regarding the type of vegetation and land use as a basis for studying climate change [140], as well as the study of land use change and its effects on the carbon content of vegetation and soil using a computer model [141].

Period II (1990–1999)

In this period, a scientific production growth (329 publications) is observed, especially between 1991 and 1997. The number of citations also increased notably, adding 22,966 (11.14%), indicating a growing interest in LULC research of tropical forests and their implications. Publications highlighted the use of satellite, socioeconomic, and vegetation data to study land cover change [142,143,144]. Other studies focus on the history, structure, diversity, disturbances, maintenance, and recovery of tropical forests [145,146,147], as well as the role of these forests in biogeochemical cycles [148,149] and the modifications of these cycles due to land use changes [150,151,152,153] and land cover change caused by natural and artificial fires in the Amazon [154,155,156].
During this period, there was an advancement in environmental and climate debates, marked by the United Nations Conference on Environment and Development, also known as Eco-92 (held in Rio de Janeiro (RJ), Brazil, in June 1992). This advancement prompted scientific research on “deforestation” and its relationship with the most diverse aspects of land use and the consequences on the climate and biodiversity of the most diverse terrestrial ecosystems, especially the Amazon.

Period III (2000–2009)

This decade shows more significant growth than the previous one, with 1009 publications and 78,912 (38.30%) citations. The documents represent 22.14% of the total number of publications. Publications with many sources that address biodiversity, species conservation, and the characterization of tropical forests stand out. The most-cited article deals with the preservation of the Brazilian Cerrado due to its biological importance and the intensification of land use. The biodiversity in different types of forests is also quantified [157]. Other studies consider how the constant change in land use can impact regional and seasonal climate trends [158,159,160], how the legacy of human beings would modify the structure of tropical forests [161,162], and the environmental and social challenges of soybean, sugarcane, and rubber production [163,164,165].
Historically, it is a consolidation period for environmental research, mainly due to online information, storage platforms, and their public availability for consultation. The “US Geological Survey” [166] site is highlighted during this time because it facilitated an expressive set of information involving the Earth’s surface mapping. That data will become a part of the National Aeronautics and Space Administration (NASA)’s EarthData [167] in the following period, making the set of Landsat (and other satellite) images freely accessible, helping researchers who previously needed to pay to access these platforms.

Period IV (2010–2021)

Finally, the last period contains the most significant number of publications, comprising 3154 articles (69.21% of the production). The substantial increase in citations with 101,263 (49.13%) demonstrates the relevance and interest of the topic in the last 11 years. During this period, related studies on tropical forests’ carbon capture and sequestration capacity [168,169,170] and the magnitude, causes, and consequences of carbon dioxide emissions are highlighted [171,172,173]. The global dynamics of tropical forests and their sustainability is also addressed [174,175,176]. Other studies carry out total estimates of carbon and biomass using satellite images [177,178,179] and establish proposals for managing and conserving tropical forests and future challenges [180,181,182].
In this phase, the Google Earth system was widely used, popularizing the use of geographic information systems in people’s daily lives, through the historical analysis of images, providing a quick view of the changes in vegetation cover and the effects of deforestation. Continuing with the Google platform, the Google Engine tool makes the processing of digital images and land cover change analysis independent from commercial programs. In addition, it marks the current advancement in geotechnologies, including drone imaging, which generates high spatial resolution imagery applicable to environmental surveillance and enforcement.
Although Figure 2 represents the scientific production of the studied topic, its evolution is reviewed using Price’s law. This law evaluates the increase in publications, where exponential growth allows the consideration that the investigated area is a field of study [183]. For example, in Figure 2, the exponential equation Y = 2.6398e0.12228x and its determination coefficient of R2 = 0.961 obtained was considered high, affirming compliance with this law; consequently, we can call the area of study a field of study.

3.1.2. Top 10 Frequently Cited Documents (RQ2)

Citation evaluation in bibliometric analysis is an indicator of the quality and impact of the research [184]. For this reason, analyzing the publications with the highest number of citations offers a perspective on the topics that dominate the study area. Table 1 identifies the ten documents that have received the most attention from the scientific community regarding LULC in tropical forests. Four studies explained the use of satellite images to analyze different land uses, land cover, and carbon balance [156,175,185,186]; three papers showed the importance and conservation of tropical forests [149,157,174]; two studies made estimates of the carbon pool and carbon dioxide emissions [170,172], and one reviewed the characteristics of secondary tropical forests [147]. However, these documents represent only a tiny fraction of scientific production (0.22%) and contribute 8.24% of the total citations. In addition, seven publications come from UK journals, and the remaining three were published in US journals.
The American Journal of Science published the first two most-cited papers. In the first article, the authors use satellite information to map the change in forest cover worldwide. The article verified that tropical forests experienced a significant forest cover loss [175]. The second article presents estimates of the stocks and fluxes of carbon in the world’s forests through data inventories and statistical models. It showed that in the tropics, the change in land use is more intense, and they have greater carbon sequestration; however, future carbon cycle studies should be continued [170]. Finally, the third most-cited article was published by the British journal Nature, presenting a meta-analysis of 138 studies. This article analyses the impact of land use changes on tropical forests’ biodiversity, establishing that primary forests harbor a greater biodiversity than degraded forests [174].
The annual citation index is a parameter that establishes the relationship between the total number of citations per article and their respective publication time [131]. This parameter helps determine whether there is a proportional relationship between the total number of citations and the document’s age. Table 1 shows this index, where the documents published for a longer time do not necessarily obtain a greater number of citations. On the contrary, the most recent document in the table (2013) is the one with the highest index [175].

3.1.3. Top 10 Countries by Number of Documents (RQ2)

The analysis by country is carried out according to the author affiliation, and it reveals the most influential countries and the various relationships between these countries pertaining to knowledge generation [187].
Figure 3 shows the contribution of these countries. Among the top 10 countries, there are four American countries (United States, Brazil, Mexico, and Canada), four European countries (United Kingdom, Germany, France, and the Netherlands), one from Oceania (Australia), and one from Asia (Indonesia). North American and European countries do not have tropical forests in their territory. Regardless, they work with nations from South America, Africa, and Asia to build the intellectual structure of the study area.
The country with the highest number of publications and citations is the United States, with 1893 and 127,366, respectively. Brazil and the United Kingdom rank second and third with 1591 and 610 documents, respectively. In addition, these three countries have published 87 investigations on the subject, with a closer relationship between the United States and Brazil since they have 503 documents together.
From the collaboration between the United States and Brazil, we can highlight publications that study the Brazilian Amazon land cover change through satellite images, as well as the impacts of deforestation, the ecology of tropical forests due to fragmentation by agricultural activities, and the analysis of public policy applications [180,188,189,190]. These three countries, in collaboration with researchers such as John Adams (University of Washington), Carlos Souza (Instituto do Homem e Meio Ambiente da Amazônia), and Edward Mitchard (University of Edinburgh), presented publications representing a model to monitor land use change [191], as well as the quantification of annual deforestation and degradation [192] and carbon stock mapping [178]. The United Kingdom contributes with research on biodiversity in tropical environments, as well as the ecosystem services offered by tropical forests, conservation implications, and on the perceptions of humanity towards forest changes [193,194,195,196].

3.1.4. Top 10 Sources by Number of Documents (RQ3)

Table 2 shows the performance and quality indices of the ten leading journals collaborating to generate knowledge in the area. The top 10 journals contain 920 of the 4557 publications analyzed, representing 20.19% of the scientific production. The table in question shows the performance indicators of journals such as SJR and CiteScore with their 2020 figures. Various areas of knowledge such as agricultural and biological sciences are addressed in Forest Ecology and Management, Land Use Policy and Biotropica. Global Change Biology, Environmental Research Letters and Ecological Applications correspond to the environmental sciences. Earth and planetary science are reviewed in the journals Remote Sensing of Environment, International Journal of Remote Sensing, and Remote Sensing. Meanwhile, the journal PLOS One is multidisciplinary, addressing topics in the natural, social, health, and engineering sciences.
Forest Ecology and Management is a primary source, with 179 documents representing 3.93%. The source presents a CiteScore of 5.8 and has an SJR index of 1.29. It is the journal with the second longest (45 years) coverage period and has 11,068 citations. The most relevant document in the journal has 758 citations. This document studies the results of the Global Forest Resources Assessment 2015 (FRA 2015), concluding that the net loss of tropical forests in the last five years of the study was more accentuated in Brazil, Indonesia, and Nigeria. The publication also suggests policies for developing public forest conservation strategies [176]. Even though its coverage in years (20 years) has decreased in comparison with the previous journal, the second place is occupied by PLOS One with 102 documents and 3531 citations. Its most-cited paper (274 citations) compares protected areas of strict use against those of multiple use using forest fires as an indicator of deforestation and carbon release, finding that protected areas have a lower incidence of forest fires [197]. Both journals are in quartile 1. The following three primary sources are Global Change Biology, Land Use Policy, and Remote Sensing of Environment. The table is generally dominated by journals with greater coverage of years in Scopus. Still, it shows that recent journals could position themselves if they present a greater production of documents in the area.

3.1.5. Top 10 Leading Authors (RQ3)

The authors’ analysis reveals the most productive researchers and facilitates the finding of research collaborations by other academics [198]. Table 3 lists the LULC authors in tropical forests by their number of publications and total citations. In addition, a quality indicator, the H-index, is observed. The author with the most significant number of publications is Yoshio Shimabukuro (National Institute of Space Research), who stands out with publications that include the study of land cover change through remote sensing [199,200,201,202,203], as well as the impacts of LULC [204,205,206]. Eric Davidson (University of Maryland Center for Environmental Science) is the most influential researcher due to his publication of documents in collaboration with other authors. The topics include studying the role of tropical forests in biogeochemical cycles [149,207,208] and the relationship between LULC and biogeochemical cycles [153,209,210,211].
Table 3 shows the top 10 authors. Three are Brazilian, with Yoshio Shimabukuro and Luiz Aragao, who have 11 publications together. They study issues related to LULC in the Brazilian Amazon, e.g., [205,212,213]. On the other hand, Philip Fearnside and Luiz Aragao have a joint publication that aims to analyze fire-affected areas through satellite images [214]. In addition, Fearnside studies land use and climate change [42,215,216,217]. Two authors have affiliations in the United Kingdom: Jos Barlow and Carlos Peres. They have 16 publications studying aspects related to the quantification and importance of biodiversity in tropical forests, e.g., [174,218,219]. Finally, the five authors with the most significant number of documents have American affiliations. Stephen Perz presents publications with Gregory Asner and other pieces with Robert Walker that include the study of the relationship of small farms with land cover change [220,221]. Emilio Morán presents a collaboration with Robert Walker investigating the relationship between humans and LULC [222]. His most influential publications study the Brazilian Amazon through remote sensing [143,223,224]. While Eric Davidson has no collaborations with US-affiliated authors, his work includes studies analyzing tropical forest transition and mortality due to land use [225,226].

3.2. Bibliometric Mapping Analysis

Bibliometric mapping gives access to visual representations in the form of an analysis unit network (authors, keywords, journals, or countries), facilitating the observation of their development and interaction in a network made up of nodes and grouped in clusters [227]. In addition, these maps illustrate a deeper and more detailed understanding of the intellectual structure of an area of research [228].

3.2.1. Co-Occurrence Author Keyword Network (RQ4)

This analysis allows the detection of central research topics and themes and their trends in the field of study [229]. Figure 4 shows the co-occurrence network of author keywords, finding 554 nodes (relevant topics) and 14 clusters. The minimum number of co-occurrences was five. In the figure, the nodes (circles) represent the topics, which, when grouped (clusters of the same color), determine the themes or lines of research [134,230].
Cluster 1 (red), “Tropical forest, biodiversity, and fragmentation”, presents 69 nodes with 1490 occurrences. Relevant topics are tropical forests, biodiversity, and rainforest. In this cluster, there are articles related to the value of tropical forests and their biodiversity [218,219,231], impacts of different land use [38,232,233], biodiversity according to land use [234,235], anthropogenic activity, and biodiversity [51,162,236]. In addition, other authors consider studies referring to deforestation predictors and causes [237,238], history of land use and vegetation [239,240], and biodiversity recovery [241]. The topics addressed in this cluster are of great importance due to tropical forests’ economic, social, ecological, and political interests since they provide multiple benefits, making it necessary to know their value and threats.
Cluster 2 (green), “Land use change”, consists of 69 nodes and 1360 occurrences. The publications in this cluster present research on soil structure changes [242,243,244], soil composition, and tropical forest structure [245,246,247] and management [248,249]. Other publications explain the impacts of land use change on carbon stocks [250,251,252], carbon emissions [253,254,255], and deforestation patterns [199,256]. In this cluster, the most relevant keywords are land use change and tropical rainforest. The cluster considers determining and making visible the impacts of land use change at different levels of the tropical ecosystem.
In cluster 3 (blue), “Remote sensing, land cover, and GIS”, the terms “remote sensing” and “Landsat” predominate. This cluster presents studies using remote sensing to determine forest changes over time [21,200,212], biomass mapping [24,245,257], and biodiversity characterization [258,259]. On the other hand, deforestation [260,261], management [262,263], and associated ecosystem services [54,233] are also evaluated. In this cluster, there are 59 nodes and 1253 occurrences, highlighting the importance of different techniques such as remote sensing to study tropical forests and their ecological and structural dynamics.
Cluster 4 (yellow) is “Land use and indigenous people”. This cluster has 54 nodes and 1019 occurrences. The predominant themes are land use and agriculture. In addition, it includes articles referring to the economic value of non-timber products and the risk they face due to deforestation [264,265], land use in protected or rural areas [266,267,268], and the observation of agroforestry system benefits [269,270]. In this case, the study of these topics aims to investigate and highlight the importance and impacts of different land uses and their opportunities and threats to indigenous communities.
Cluster 5 (purple), “Amazon, deforestation, and public policy”, shows land use dynamics in the Amazon [238,271,272] and the bases for conserving tropical forests [249,273,274], and explains the relationship between degradation and palm oil plantations [275,276]. The cluster has 48 nodes with 2141 occurrences. The main topics are studied due to the taxonomic importance and natural regulation of the Amazon, where the patterns and causes of deforestation are analyzed. In addition, it includes public policies for the management, restoration, and remediation of tropical forests.
Cluster 6 (sky-blue) is “Ecosystem service, logging, and forest management”. The main terms in this cluster are pasture and ecosystem service. In this group, 45 nodes with 641 occurrences are presented and include research on the importance of ecosystem services [193,277] and the impacts at different organizational levels of forest exploitation [75,278]. These issues particularly highlight the benefits that tropical forests provide to humans and the planet and the effects on ecosystem services due to the conversion of forests to pasture.
Cluster 7 (orange) is “Fire, human disturbance, and climate”. In this cluster, there are 39 nodes and 430 occurrences. These topics are mainly related to articles that study the change of forest cover due to fires [279,280] and the impact of anthropogenic activities [281,282]. In addition, the topics focus on explaining how natural and arson fires, as well as disturbances due to the intensification of human activity, directly influence the climate in the short and long term.
Cluster 8 (brown) is “Secondary forest and carbon cycle”. The main characteristics of this cluster are research on biodiversity [188,283] and the carbon cycle in secondary forests [148,186]. It presents 32 nodes and 520 occurrences, which address issues related to the taxonomic value and structural variations that secondary forests contribute due to their different disturbances.
Cluster 9 (violet) is “Climate change, land cover change, and water cycle”. In this cluster, the central terms are climate change and land cover change, with 32 nodes and 603 occurrences. The effects of climate change and its impacts [160,284] are one of the main topics in this cluster. In addition, it studies the modeling of biogeochemical cycles in tropical forests [285,286] and biodiversity dynamics [287,288]. Climate change is related to the different dynamics in tropical forests, so its study is of interest due to the value of forests for the planet.
Cluster 10 (pink), “Land use and land cover change, disturbance”, includes 32 nodes with 510 occurrences. Among the most common topics are carbon and nitrogen dynamics due to land use [210,289], biomass estimates, carbon and nitrogen in tropical forests [290,291], and composition and microbial dynamics [292,293]. The central nodes of this cluster are carbon, land use and land cover change, and disturbance. These topics are studied due to the importance of the biogeochemical cycles on the planet. These cycles maintain a balance in the biotic and abiotic components and help to understand their role in climate change.
Clusters 11 to 14 (light green, mustard, light blue, and lilac) are small clusters with familiar topics, which is why they have been grouped under the name “Global change, protected area, and Amazon basin”. This cluster contains 74 nodes with 162 occurrences. The central topics include publications referring to the general analysis of the Amazon basin, its different land uses and changes in vegetation structure [43,209] and disturbances, and the management and conservation of protected areas in tropical forests [294,295].

3.2.2. Co-Citation Analysis (RQ4)

This analysis makes it easier to determine the papers that are more likely to be related or belong to a similar study area [296]. It establishes the relationship between the co-cited articles and determines their influence [297]. This analysis forms clusters where it identifies thematic areas [298]. Large clusters include a more significant number of publications, and the distance between two clusters indicates the relationship between them. If they are close, they tend to be strongly related [299]. Co-citation can be used as a unit of analysis for authors and journals.

Author Co-Citation Analysis (ACA)

This analysis aims to show the structure and connections of the most frequently cited authors [300]. In addition, it makes it possible to understand the various schools of thought or topics that comprise the field’s base knowledge [301]. In Figure 5, the nodes represent the authors, who are grouped in a cluster of the same color to represent collective knowledge [302]. The figure shows a structure of eight clusters and 3833 authors with more than 30 co-citations. The clusters and their most relevant authors will be examined next.
Cluster 1 (red), “Deforestation and value of tropical forests”, includes 1120 authors. This cluster comprises different topics that refer to deforestation for other land uses, the importance of tropical forests structure, and their biodiversity and conservation. William Laurance, with 1854 citations, leads the cluster, presenting publications on the analysis of deforestation [237,273], and tropical forests’ response to global changes and their conservation [303,304]. Carlos Peres (1351) and Jos Barlow (1224) present 15 joint studies that analyze the value of diversity in different tropical forest environments, whether disturbed or not [219,236]. The three primary authors share a meta-analysis that studies the value of biodiversity in tropical forests and highlights its importance [174].
Cluster 2 (green) is the “Carbon and nitrogen cycle”. This cluster features 733 authors who discuss topics relating to carbon and nitrogen reserves, emissions, and sequestration. For example, Sandra Brown (1283) makes estimates of carbon density, biomass, and carbon pools [178,305,306]. Eric Davidson (1264) focuses on carbon and nitrogen dynamics in the Amazon [211,307,308], and nutrient activity in tropical forests [309,310]. On the other hand, Carlos Cerri (1056) studies the biochemical dynamics associated with land conversion in different land uses [152,189,254].
Cluster 3 (blue), “Wildfires and land use dynamics”, presents 720 authors who focus on studying the effect of fires in tropical forests and the impacts of land use change. The most relevant publication by Daniel Nepstad (2640) focuses on the positive and negative effects of forest fires [154,156,225]. Another author, Philip Fearnside (2421), studies the threats posed by land use change [42,163,215]. Finally, Ruth DeFries (1722) focuses on deforestation impacts and the conservation opportunities that this activity entails [199,201,256,273].
Cluster 4 (yellow), “Rainforest”, includes 694 authors mainly studying tropical forests and their dynamics and interactions. Among the most relevant authors is Carlos Nobre (1369), who presents studies on changes in the Amazon due to logging and fires [156,311], and the risks of climate change in the Amazon due to anthropogenic activities [312,313]. In addition, Yadvinder Malhi (1200) and Oliver Phillips (946) present nine joint publications that mainly focus on carbon balance, biodiversity, and environmental changes in the Amazon region, e.g., [304,314,315].
Cluster 5 (purple), “Remote sensing and monitoring”, which contains 468 authors, deals with issues related to estimating carbon cycle products and changes in coverage visualized through satellite images. Gregory Asner (1580) studies estimates of biomass and greenhouse gas emissions [171,316], as well as land cover and its different uses, using satellite images [317,318]. For his part, Matthew Hansen (1352) focuses on cover change detection to determine an area’s vulnerability [319,320]. Finally, Richard Houghton (1266) performs multi-temporal analyses of carbon stock and estimates emissions [168,170,172].
Cluster 6 (sky-blue), “Mining and historical LULC”, has 96 authors, and the publications are related to mining activity in tropical forests and LULC multi-temporal analysis. Timothy Killeen (398) studies land use biodiversity evolution [304,321]. Mark Bush (339) explores LULC multi-temporal changes [322,323]. At the same time, Miles Silman (224) covers the study of deforestation, degradation, and mercury impact on soils as a result of mining activity [324,325].
Cluster 7 (orange) and Cluster 8 (brown), “Logging, fire, and evaporation”, have an author in each of their clusters. Jeffrey Gerwing (42) presents papers on forest degradation caused by logging and fire [326], while Tatiana Sá (35) studies the evaporation process of secondary tropical forests [327].

Journal Co-Citation Analysis (JCA)

This analysis considers the similarity of the journals according to the received reference patterns, where two journals are cited by several documents that are related to one another [328,329]. This analysis allows us to understand the theme structures of the various areas of knowledge [330].
Figure 6 illustrates this journal co-citation network, visualizing the different journals and their connections. This structure has seven clusters, including 541 journals (nodes) with at least 30 citations.
Cluster 1 (red), “Biology”, represents 164 journals totaling 48,030 citations. In this group, the main journals are: Conservation Biology (the United Kingdom, 3554 citations), Ecology (the United States, 3045 citations), Biological Conservation (the Netherlands, 2913 citations), Biotropica (the United States, 2853 citations), and Ecological Applications (the United States, 2436 citations). This group’s journals mainly study different areas of biology such as ecology, conservation, biogeography, and evolution.
Cluster 2 (green), “Environment, land use, and development”, includes 144 journals totaling 28,462 citations. The journals of this cluster stand out for their topics encompassing various aspects of the environment, and land use and its ecological, economic, social, and political implications. Among the journals, we find: Agriculture, Ecosystems and Environment (the Netherlands, 1645 citations), Bioscience (the United States, 1532 citations), World Development (the United Kingdom, 1249), Ecological Economics (the Netherlands, 1138 citations), and Land Use Policy (the United Kingdom, 1038 citations).
Cluster 3 (blue), “Soil and biogeochemistry”, contains 140 journals, totaling 22,566 citations. Usually, the journals that belong to this group present publications on the study of soil structure, soil dynamics, and biogeochemical cycles, their function, and interactions. These journals include: Soil Biology and Biochemistry (the United Kingdom, 1588 citations), Journal of Hydrology (the Netherlands, 1193 citations), Geoderma (the United Kingdom, 1081 citations), Soil Science Society of America Journal (the United States, 996 citations), and Biogeochemistry (the Netherlands, 873 citations).
Cluster 4 (yellow), “Multidisciplinary sciences”, groups 58 journals and 39,796 citations. These journals address various academic disciplines such as environmental, biological, agricultural, planetary, and earth sciences. Journal examples include: Science (the United States, 7251 citations), Nature (the United Kingdom, 4810 citations), Proceedings of the National Academy of Sciences of the United States of America (the United States, 4320 citations), Global Change Biology (the United Kingdom, 3771 citations), and Journal of Geophysical Research (the United States, 1949 citations).
Cluster 5 (purple), “Remote sensing”, consists of 26 journals with 12,186 citations. In this cluster, the journals that stand out are: Remote Sensing of Environment (the United States, 4687 citations), International Journal of Remote Sensing (the United Kingdom, 2857 citations), IEEE Transactions on Geoscience and Remote Sensing (the United States, 1071 citations), Remote Sensing (Switzerland, 1002 citations), and ISPRS Journal of Photogrammetry and Remote Sensing (the Netherlands, 372 citations). The journals of this group focus on publications related to remote sensing in different ecosystems, covering aspects such as ecology, environmental sciences, and engineering.
Cluster 6 (sky blue) and Cluster 7 (orange), “Forest”, includes nine journals with 6110 citations. The journals of this group refer mainly to the study of forest sciences, ecology, management, and conservation. Some examples include the journals Forest Ecology and Management (the Netherlands, 5053 citations), Frontiers in Ecology and Environment (the United States, 384 citations), Canadian Journal of Forest Research (Canada, 287 citations), Forest Science (the United States, 171 citations), and New Forests (Holland, 58 citations).
All of the topics mentioned in each cluster are related to tropical forests. They are made up of different biological structures, as well as topics ranging from the biodiversity, ecology, and conservation of tropical forests to the dynamics of the different environmental matrices.

4. Discussion

This paper investigates the scientific trends and evolution of LULC research in tropical forests. Regarding environmental sciences, this bibliometric research adds a new perspective to the current forest situation by identifying the countries that contribute the most to LULC studies. At the same time, the management, conservation, and implementation of public policies that help control environmental problems such as global warming, deforestation, and forest degradation are increasingly crucial for researchers, world leaders, and organizations. For this reason, a bibliometric analysis can serve as a starting point to determine the topics of most significant interest in this field of study.
Tropical forests play an important ecological, social, and economic role. In addition to having rich biodiversity, they are home to multiple human communities and providers of various ecosystem services [29,331]. Despite their value, these ecosystems are subjected to deforestation, degradation, and forest fragmentation caused by forest fires during drought, in addition to the replacement and loss of forests caused by human activities such as housing, food, and infrastructure [38,43,332]. These tropical forest alterations are identified over time with the help of satellite images, which has made it possible to study changes in land cover and land use. This research examined the intellectual structure of this study field through 4557 documents and the contribution of 74 countries, most of which are developed countries (Figure 2). The first study concerning LULC in tropical forests analyzed the different applications of Landsat images in various tropical forest studies [333]. At the same time, the most recent publication merged data from Sentinel-1 and -2 to model the characteristics of the vegetation structure in GEE for the Paraguayan Chaco [334].
On the other hand, Brazil has a considerable extension of tropical forests. Their participation is important due to their interest in Amazon conservation, which represents approximately 60% of the Brazilian territory [335]. Guyana, Bolivia, Cambodia, Malaysia, Nigeria, and other regions with tropical forests have an important collaboration with developed countries. This collaboration may be related to the funding provided by international organizations and the large volume of partnerships from the United States, Brazil, the United Kingdom, and the European Union [81,336]. Research usually requires economic, technological, and material resources, which are not readily available in less developed countries; as a result, collaboration is necessary. Some biodiversity hotspots are found in Southeast Asia, Madagascar, Liberia, Central America, and the Amazon rainforest. The same regions tend to present a high deforestation rate [337,338], a known environmental, social, and economic problem. For these reasons, LULC studies with close collaboration with the United States have been necessary.
The importance and conservation of tropical forests and their carbon reserves and emissions belong to topics analyzed through satellite images. These images also facilitate the exploration of different land uses, land cover, and the carbon balance [170,174,175,185]. The research interest in these topics can be linked to current climate change concerns and the importance of the conservation of different ecosystems for the maintenance and reduction of greenhouse gases, not to mention the current interest in the sustainable management of these forests’ resources, so their study is kept regular and uninterrupted.
The Sustainable Development Goals (SDGs) established in 2015 would also benefit the development and conservation of tropical forests. Several researchers analyzed the relationship of LULC with the SDGs in these ecosystems. These studies include public policy proposals to allow the sustainable management of tropical forests, coverage mapping [339], and land use change and biomass as a baseline to achieve the objectives [340]. The SDGs related to activities within tropical forests and their conservation are: the end of poverty (SDG 1), zero hunger (SDG 2), health and wellbeing (SDG 3), affordable and non-polluting energy (SDG 7), responsible production and consumption (SDG 12), action for the climate (SDG 13), and life in terrestrial ecosystems (SDG 15). Tropical forests are the most biodiverse, so their conservation, restoration, and sustainable management are essential. In this sense, goals 13 and 15 are directly linked to this challenge. Similarly, tropical forests are important carbon reservoirs that serve as a local and global buffer against the effects of climate change. However, deforestation contributes to the emission of greenhouse gases, so it serves as a matter requiring urgent attention.
Remote sensing has been a tool used since the beginning of LULC research in tropical forests. Since 1972, Landsat satellite data has made it possible to analyze changes in global coverage, such as deforestation and the expansion of agricultural and urban areas. Starting in 2008, corrections were made regarding the errors presented in the datasets to improve their coverage [341]. Therefore, the evolution and appearance of different sensor networks have offered opportunities to the scientific community in biology, ecology, and the conservation of the tropics to develop a complete analysis of how the various tropical ecosystems respond to global environmental and climate changes [342,343].
Despite continuous advances in LULC research techniques in tropical forests, forest degradation retains various unknows in many human impact analyses [344,345]. Nevertheless, a positive trend is maintained in the LULC impact study since these activities have long-term effects on biodiversity, the structure of terrestrial communities, climate, and landscapes, which are affected by ever-rising deforestation rates. According to the World Resources Institute portal, there has been a loss of around 3 to 4 million hectares of primary tropical forests in the last two decades. By 2020, the losses had increased compared to the previous year [346].
The three bibliometric maps and intellectual structure analyses allowed us to analyze the relationships between the different topics and schools of thought:
  • First, the keyword co-occurrence analysis made it possible to analyze the most prominent themes (Figure 4). For this case, the clusters are superimposed, demonstrating their complementarity between the clusters and the various topics (keywords). The issues are closely related to multiple areas of biology, land cover and use, and remote sensing. For example, biodiversity (red cluster) and land use change (green cluster) were among the most-studied topics in tropical forests due to the interest in understanding and analyzing the response of various taxa to cover change, and different land uses [347,348].
  • The author co-citation analysis reveals the interconnections between different authors (Figure 5). At the same time, the journal’s co-citation analysis (Figure 6) shows clusters where a specific school of thought is studied. For example, authors such as Daniel Nepstad and Robert Walker (blue cluster) have been pioneers in studies focused on the activity and importance of tropical forests. These topics are studied mainly in the red and green (most significant) clusters of the JCA, which include the most-analyzed general areas in tropical forests, referring to the different aspects of biology and the environment. In the analysis of the author co-citations, cluster 5 (purple cluster) related to clusters 3 (blue cluster) and 4 (yellow cluster), which cover research structures such as sciences and environmental studies, ecology, and agricultural and biological sciences. The studies of these clusters deal with issues related to remote sensing and monitoring and its usefulness in analyzing topics such as fires and deforestation in tropical forests and their impact on carbon and nitrogen values.
This research facilitates the analysis of the interrelationships between researchers, countries, and schools of thought where the different areas of LULC in tropical forests are studied. The development of this theme aims at the continuous understanding of the LULC impacts using newer mathematical, computer, and technological models, satellite missions [349,350], and the constant advance in conservation and sustainability [351,352].
An integrated analysis indicates that the scientific research on tropical forests has a greater focus on the Amazon region, which may be related to the fact that this area represents the largest tropical forest in the world, as well as to its fundamental role in conservation and in the fight against climate change. It also concerns land use and land cover alterations, which South American institutions and other countries are widely investigating, particularly in the United States and Europe. There is an inequality between Brazil and the other Amazonian countries regarding scientific production, since the Andean countries have a much lower percentage of research. This inequality creates a differentiated understanding of Amazonian problems and decreases the use of these studies in the development of public policies to control deforestation throughout the region.
Another important region for tropical forest assessment is Africa, where research production is reduced. The United States appears as a producer of knowledge about other areas of the planet, which reflects its dominance of information storage platforms, which have made satellite and radar images of the Earth accessible to the public, becoming a global generator of geospatial information. This situation implies that many countries with no surveillance systems use these external databases as the primary source of information, thus generating a direct intervention in their geopolitics.
Finally, although LULC research in tropical forests is not recent, and its growth in the last decade has been prominent, it is necessary to consider what other topics within the area could be analyzed. The following are some gaps in the research that future studies could address:
  • Studies linking compliance with SDGs concerning LULC in tropical forests are required. Few studies address this issue, and the ones that do focus mainly on how deforestation and degradation monitoring can be used to manage these objectives.
  • Studies of specific activities such as oil exploitation and mining focus solely on the effects on human health, and there are few publications on its impact on the environment.
  • LULC studies in the Andean–Amazonian region should be expanded. A large part of the analyses use optical satellite images, where the presence of cloud cover is a problem that limits the generation of maps regarding land use and land coverage in these regions.
This study has some limitations, such as i) the exclusive consideration of scientific articles in English, and ii) the fact that the data analysis was limited to data from the Scopus database. As a result, the present study may have ignored some contributions in this field. Subsequent studies could consider these limitations to broaden the subject of study.

5. Conclusions

This study demonstrates that in the last four decades, there has been an increase in the scientific production of tropical forests’ LULC due to the collaboration of 74 countries. The intellectual structure in this field of study records collaborations of greater scientific production concerning (i) researchers: Shimabukuro Yosie and Barlow Jos; (ii) countries: the United States and Brazil; and (iii) journals: Forest Ecology and Management and PLOS One, as well as the collaborations with the highest number of citations: (i) author: Eric Davidson; (ii) country: the United States; and (iii) journal: Science.
The different approaches associated with LULC tend to focus on research topics related to the analysis of multiple anthropogenic activities that cause forest degradation and changes in their ecological dynamics. In addition, a growing interest in conserving these ecosystems due to their high biological, economic, and social value is revealed. The use and evolution of different technological tools that allow a multi-temporal analysis of the dynamics and structure of tropical forests were also evidenced.
This research serves as a contribution to future studies that explore the areas of knowledge focused on LULC in tropical forests because (i) it facilitates access to information on the most studied topics and the most representative authors, (ii) it guides researchers to form networks of collaboration when developing projects according to their area of study, (iii) it is a management tool for researchers to understand, in a general way, the intellectual structure of the area, and (iv) it is a complement for the identification of tendencies in the area that allow the generation of knowledge in favor of the sustainable management of forests.

Author Contributions

Conceptualization, A.V.-M. and G.P.-V.; methodology, A.V.-M., N.M.-B., G.P.-V. and G.H.-F.; software, N.M.-B. and G.P.-V.; validation, A.V.-M., N.M.-B., G.P.-V. and G.H.-F.; formal analysis, A.V.-M., N.M.-B., G.P.-V., A.d.L. and G.H.-F.; investigation, A.V.-M., N.M.-B., G.P.-V. and G.H.-F.; data curation, A.V.-M., N.M.-B. and G.P.-V.; writing—original draft preparation, A.V.-M., N.M.-B., G.P.-V. and G.H.-F.; writing—review and editing, A.V.-M., N.M.-B., G.P.-V., A.d.L. and G.H.-F.; visualization A.V.-M., N.M.-B. and G.P.-V.; supervision, A.V.-M.; project administration, A.V.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This study was supported by the research projects of the ESPOL University (Escuela Superior Politécnica del Litoral): (a) “Estudios de impacto ambiental de grandes obras de ingeniería en la Amazonía ecuatoriana” (Studies of the environmental impact of major engineering works in the Ecuadorian Amazon) with code no. FICT-53-2020; (b) “Registro del Patrimonio Geológico y Minero y su incidencia en la defensa y preservación de la geodiversidad en Ecuador” (Registry of Geological and Mining Heritage and its impact on the defense and preservation of geodiversity in Ecuador) with the code no. CIPAT-01-2018; and (c) “Proyecto de Gestión y Evaluación de la Investigación Científica en Ciencias de la Tierra, Economía, Administración y sus vínculos con la Sociedad” (Project on Management and Evaluation of Scientific Research in Earth Sciences, Economics, Administration and their links with Society) with the code no. CIPAT-7-2022.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hartshorn, G.S. Tropical Forest Ecosystems. In Encyclopedia of Biodiversity; Levin, S., Ed.; Elsevier: Amsterdam, The Netherlands, 2013; pp. 269–276. ISBN 9780123847195. [Google Scholar]
  2. Phillips, O.L.; Gentry, A.H. Increasing Turnover Through Time in Tropical Forests. Science 1994, 263, 954–958. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Adler, G.H. Rainforest Ecosystems, Animal Diversity. In Encyclopedia of Biodiversity; Elsevier: Amsterdam, The Netherlands, 2013; pp. 304–312. [Google Scholar]
  4. Deikumah, J.P.; Mcalpine, C.A.; Maron, M. Biogeographical and Taxonomic Biases in Tropical Forest Fragmentation Research. Conserv. Biol. 2014, 28, 1522–1531. [Google Scholar] [CrossRef] [PubMed]
  5. Reed, S.C.; Reibold, R.; Cavaleri, M.A.; Alonso-Rodríguez, A.M.; Berberich, M.E.; Wood, T.E. Soil Biogeochemical Responses of a Tropical Forest to Warming and Hurricane Disturbance. In Advances in Ecological Research; Dumbrell, A., Turner, E., Fayle, T., Eds.; Academic Press: Cambridge, MA, USA, 2020; pp. 225–252. [Google Scholar]
  6. Ahlheim, M.; Börger, T.; Frör, O. Replacing Rubber Plantations by Rain Forest in Southwest China—Who Would Gain and How Much? Environ. Monit. Assess. 2015, 187, 3. [Google Scholar] [CrossRef] [PubMed]
  7. Corlett, R.T. Tropical Rainforests and Climate Change. In Encyclopedia of the Anthropocene; Dellasala, D., Goldstein, M., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; pp. 25–29. ISBN 9780128135761. [Google Scholar]
  8. Funk, J.M.; Aguilar-Amuchastegui, N.; Baldwin-Cantello, W.; Busch, J.; Chuvasov, E.; Evans, T.; Griffin, B.; Harris, N.; Ferreira, M.N.; Petersen, K.; et al. Securing the Climate Benefits of Stable Forests. Clim. Policy 2019, 19, 845–860. [Google Scholar] [CrossRef] [Green Version]
  9. Meister, K.; Ashton, M.S.; Craven, D.; Griscom, H. Carbon Dynamics of Tropical Forests. In Managing Forest Carbon in a Changing Climate; Springer: Dordrecht, The Netherlands, 2012; pp. 51–75. ISBN 9789400722323. [Google Scholar]
  10. Underwood, E.C.; Olson, D.; Hollander, A.D.; Quinn, J.F. Ever-Wet Tropical Forests as Biodiversity Refuges. Nat. Clim. Chang. 2014, 4, 740–741. [Google Scholar] [CrossRef]
  11. Bonal, D.; Burban, B.; Stahl, C.; Wagner, F.; Hérault, B. The Response of Tropical Rainforests to Drought—Lessons from Recent Research and Future Prospects. Ann. For. Sci. 2016, 73, 27–44. [Google Scholar] [CrossRef] [Green Version]
  12. Primack, R.B.; Morrison, R.A. Causes of Extinction. In Encyclopedia of Biodiversity; Elsevier: Amsterdam, The Netherlands, 2013; pp. 401–412. ISBN 9780123847195. [Google Scholar]
  13. Ustjuzhanin, P.; Kovtunovich, V.; Maicher, V.; Sáfián, S.; Delabye, S.; Streltzov, A.; Tropek, R. Even Hotter Hotspot: Description of Seven New Species of Many-Plumed Moths (Lepidoptera, Alucitidae) from Mount Cameroon. Zookeys 2020, 935, 103–119. [Google Scholar] [CrossRef] [PubMed]
  14. Mishra, G.; Das, P.K.; Borah, R.; Dutta, A. Investigation of Phytosociological Parameters and Physico-Chemical Properties of Soil in Tropical Semi-Evergreen Forests of Eastern Himalaya. J. For. Res. 2017, 28, 513–520. [Google Scholar] [CrossRef]
  15. Neha, S.A.; Khatun, U.H.; Ul Hasan, M.A. Resource Partitioning and Niche Overlap between Hoolock Gibbon (Hoolock Hoolock) and Other Frugivorous Vertebrates in a Tropical Semi-Evergreen Forest. Primates 2021, 62, 331–342. [Google Scholar] [CrossRef] [PubMed]
  16. Van Bloem, S.J.; Murphy, P.G.; Lugo, A.E. Tropical Forest | Tropical Dry Forests. In Encyclopedia of Forest Sciences; Burley, J., Ed.; Elsevier: Amsterdam, The Netherlands, 2004; pp. 1767–1775. ISBN 9780121451608. [Google Scholar]
  17. Ishida, A.; Diloksumpun, S.; Ladpala, P.; Staporn, D.; Panuthai, S.; Gamo, M.; Yazaki, K.; Ishizuka, M.; Puangchit, L. Contrasting Seasonal Leaf Habits of Canopy Trees between Tropical Dry-Deciduous and Evergreen Forests in Thailand. Tree Physiol. 2006, 26, 643–656. [Google Scholar] [CrossRef]
  18. Ray, D.K. Tropical Montane Cloud Forests. In Climate Vulnerability; Elsevier: Amsterdam, The Netherlands, 2013; Vol. 5, pp. 79–85. ISBN 9780123847041. [Google Scholar]
  19. Salinas, N.; Cosio, E.G.; Silman, M.; Meir, P.; Nottingham, A.T.; Roman-Cuesta, R.M.; Malhi, Y. Editorial: Tropical Montane Forests in a Changing Environment. Front. Plant Sci. 2021, 12. [Google Scholar] [CrossRef]
  20. Campos Pinto, L.; de Mello, C.R.; Norton, L.D.; Owens, P.R.; Curi, N. Spatial Prediction of Soil-Water Transmissivity Based on Fuzzy Logic in a Brazilian Headwater Watershed. Catena 2016, 143, 26–34. [Google Scholar] [CrossRef]
  21. Pellikka, P.K.E.; Lötjönen, M.; Siljander, M.; Lens, L. Airborne Remote Sensing of Spatiotemporal Change (1955–2004) in Indigenous and Exotic Forest Cover in the Taita Hills, Kenya. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 221–232. [Google Scholar] [CrossRef]
  22. Otavo, S.; Echeverría, C. Fragmentación Progresiva y Pérdida de Hábitat de Bosques Naturales En Uno de Los Hotspot Mundiales de Biodiversidad. Rev. Mex. Biodivers. 2017, 88, 924–935. [Google Scholar] [CrossRef] [Green Version]
  23. Gloor, E. The Fate of Amazonia. Nat. Clim. Chang. 2019, 9, 355–356. [Google Scholar] [CrossRef]
  24. Saatchi, S.S.; Houghton, R.A.; Dos Santos Alvalá, R.C.; Soares, J.V.; Yu, Y. Distribution of Aboveground Live Biomass in the Amazon Basin. Glob. Chang. Biol. 2007, 13, 816–837. [Google Scholar] [CrossRef]
  25. Schulman, L.; Ruokolainen, K.; Junikka, L.; Sääksjärvi, I.E.; Salo, M.; Juvonen, S.-K.; Salo, J.; Higgins, M. Amazonian Biodiversity and Protected Areas: Do They Meet? Biodivers. Conserv. 2007, 16, 3011–3051. [Google Scholar] [CrossRef]
  26. Le Tourneau, F.M. The Sustainability Challenges of Indigenous Territories in Brazil’s Amazonia. Curr. Opin. Environ. Sustain. 2015, 14, 213–220. [Google Scholar] [CrossRef]
  27. Edwards, D.P.; Tobias, J.A.; Sheil, D.; Meijaard, E.; Laurance, W.F. Maintaining Ecosystem Function and Services in Logged Tropical Forests. Trends Ecol. Evol. 2014, 29, 511–520. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Valle Nunes, A.; Guariento, R.D.; Santos, B.A.; Fischer, E. Wild Meat Sharing among Non-Indigenous People in the Southwestern Amazon. Behav. Ecol. Sociobiol. 2019, 73, 26. [Google Scholar] [CrossRef]
  29. Holzner, A.; Ruppert, N.; Swat, F.; Schmidt, M.; Weiß, B.M.; Villa, G.; Mansor, A.; Mohd Sah, S.A.; Engelhardt, A.; Kühl, H.; et al. Macaques Can Contribute to Greener Practices in Oil Palm Plantations When Used as Biological Pest Control. Curr. Biol. 2019, 29, R1066–R1067. [Google Scholar] [CrossRef]
  30. Malhi, Y.; Doughty, C.E.; Goldsmith, G.R.; Metcalfe, D.B.; Girardin, C.A.J.; Marthews, T.R.; del Aguila-Pasquel, J.; Aragão, L.E.O.C.; Araujo-Murakami, A.; Brando, P.; et al. The Linkages between Photosynthesis, Productivity, Growth and Biomass in Lowland Amazonian Forests. Glob. Chang. Biol. 2015, 21, 2283–2295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Powers, J.S.; Marín-Spiotta, E. Ecosystem Processes and Biogeochemical Cycles in Secondary Tropical Forest Succession. Annu. Rev. Ecol. Evol. Syst. 2017, 48, 497–519. [Google Scholar] [CrossRef] [Green Version]
  32. Pawlik, Ł. The Role of Trees in the Geomorphic System of Forested Hillslopes — A Review. Earth-Science Rev. 2013, 126, 250–265. [Google Scholar] [CrossRef]
  33. Dainese, M.; Martin, E.A.; Aizen, M.A.; Albrecht, M.; Bartomeus, I.; Bommarco, R.; Carvalheiro, L.G.; Chaplin-Kramer, R.; Gagic, V.; Garibaldi, L.A.; et al. A Global Synthesis Reveals Biodiversity-Mediated Benefits for Crop Production. Sci. Adv. 2019, 5, 1–14. [Google Scholar] [CrossRef] [Green Version]
  34. Lhoest, S.; Dufrêne, M.; Vermeulen, C.; Oszwald, J.; Doucet, J.L.; Fayolle, A. Perceptions of Ecosystem Services Provided by Tropical Forests to Local Populations in Cameroon. Ecosyst. Serv. 2019, 38, 100956. [Google Scholar] [CrossRef]
  35. Pandeya, B.; Buytaert, W.; Zulkafli, Z.; Karpouzoglou, T.; Mao, F.; Hannah, D.M. A Comparative Analysis of Ecosystem Services Valuation Approaches for Application at the Local Scale and in Data Scarce Regions. Ecosyst. Serv. 2016, 22, 250–259. [Google Scholar] [CrossRef] [Green Version]
  36. Balvanera, P.; Quijas, S.; Karp, D.S.; Ash, N.; Bennett, E.M.; Boumans, R.; Brown, C.; Chan, K.M.A.; Chaplin-Kramer, R.; Halpern, B.S.; et al. Ecosystem Services. In The GEO Handbook on Biodiversity Observation Networks; Walters, M., Scholes, R.J., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 39–78. ISBN 978-3-319-27288-7. [Google Scholar]
  37. Alamgir, M.; Turton, S.M.; Campbell, M.J.; Macgregor, C.J.; Pert, P.L. Spatial Congruence and Divergence between Ecosystem Services and Biodiversity in a Tropical Forested Landscape. Ecol. Indic. 2018, 93, 173–182. [Google Scholar] [CrossRef]
  38. Llerena-Montoya, S.; Velastegui-Montoya, A.; Zhirzhan-Azanza, B.; Herrera-Matamoros, V.; Adami, M.; De Lima, A.; Moscoso-Silva, F.; Encalada, L. Multitemporal Analysis of Land Use and Land Cover within an Oil Block in the Ecuadorian Amazon. ISPRS Int. J. Geo-Information 2021, 10, 191. [Google Scholar] [CrossRef]
  39. Turubanova, S.; Potapov, P.V.; Tyukavina, A.; Hansen, M.C. Ongoing Primary Forest Loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 2018, 13, 074028. [Google Scholar] [CrossRef]
  40. Alvarez-Berríos, N.L.; Mitchell Aide, T. Global Demand for Gold Is Another Threat for Tropical Forests. Environ. Res. Lett. 2015, 10, 014006. [Google Scholar] [CrossRef] [Green Version]
  41. Laurance, W.F.; Goosem, M.; Laurance, S.G.W. Impacts of Roads and Linear Clearings on Tropical Forests. Trends Ecol. Evol. 2009, 24, 659–669. [Google Scholar] [CrossRef] [PubMed]
  42. Fearnside, P.M. Greenhouse Gas Emissions from Brazil’s Amazonian Hydroelectric Dams. Environ. Res. Lett. 2016, 11, 7–10. [Google Scholar] [CrossRef]
  43. Velastegui-Montoya, A.; De Lima, A.; Adami, M. Multitemporal Analysis of Deforestation in Response to the Construction of the Tucuruí Dam. ISPRS Int. J. Geo-Information 2020, 9, 583. [Google Scholar] [CrossRef]
  44. Montoya, A.D.V.; De Lima, A.M.M.; Adami, M. Analysis of the Land Cover around a Hydroelectric Power Plant in the Brazilian Amazon. Anu. do Inst. Geociencias 2019, 42, 74–86. [Google Scholar] [CrossRef]
  45. Richards, P.; VanWey, L. Where Deforestation Leads to Urbanization: How Resource Extraction Is Leading to Urban Growth in the Brazilian Amazon. Ann. Assoc. Am. Geogr. 2015, 105, 806–823. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Qaim, M.; Sibhatu, K.T.; Siregar, H.; Grass, I. Environmental, Economic, and Social Consequences of the Oil Palm Boom. Annu. Rev. Resour. Econ. 2020, 12, 321–344. [Google Scholar] [CrossRef]
  47. Velastegui-Montoya, A.; de Lima, A.; Herrera-Matamoros, V. What Is the Socioeconomic Impact of the Tucuruí Dam on Its Surrounding Municipalities? Sustainability 2022, 14, 1630. [Google Scholar] [CrossRef]
  48. Wulder, M.A.; Coops, N.C.; Roy, D.P.; White, J.C.; Hermosilla, T. Land Cover 2.0. Int. J. Remote Sens. 2018, 39, 4254–4284. [Google Scholar] [CrossRef] [Green Version]
  49. Patra, S.; Sahoo, S.; Mishra, P.; Mahapatra, S.C. Impacts of Urbanization on Land Use /Cover Changes and Its Probable Implications on Local Climate and Groundwater Level. J. Urban Manag. 2018, 7, 70–84. [Google Scholar] [CrossRef]
  50. Sajikumar, N.; Remya, R.S. Impact of Land Cover and Land Use Change on Runoff Characteristics. J. Environ. Manag. 2015, 161, 460–468. [Google Scholar] [CrossRef]
  51. Newbold, T.; Hudson, L.N.; Phillips, H.R.P.; Hill, S.L.L.; Contu, S.; Lysenko, I.; Blandon, A.; Butchart, S.H.M.; Booth, H.L.; Day, J.; et al. A Global Model of the Response of Tropical and Sub-Tropical Forest Biodiversity to Anthropogenic Pressures. Proc. R. Soc. B Biol. Sci. 2014, 281. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Bailey, K.M.; McCleery, R.A.; Binford, M.W.; Zweig, C. Land-Cover Change within and around Protected Areas in a Biodiversity Hotspot. J. Land Use Sci. 2016, 11, 154–176. [Google Scholar] [CrossRef]
  53. Rodtassana, C.; Unawong, W.; Yaemphum, S.; Chanthorn, W.; Chawchai, S.; Nathalang, A.; Brockelman, W.Y.; Tor-ngern, P. Different Responses of Soil Respiration to Environmental Factors across Forest Stages in a Southeast Asian Forest. Ecol. Evol. 2021, 11, 15430–15443. [Google Scholar] [CrossRef]
  54. Sharma, R.; Rimal, B.; Baral, H.; Nehren, U.; Paudyal, K.; Sharma, S.; Rijal, S.; Ranpal, S.; Acharya, R.; Alenazy, A.; et al. Impact of Land Cover Change on Ecosystem Services in a Tropical Forested Landscape. Resources 2019, 8, 18. [Google Scholar] [CrossRef] [Green Version]
  55. Bonilla-Bedoya, S.; Estrella-Bastidas, A.; Molina, J.R.; Herrera, M.Á. Socioecological System and Potential Deforestation in Western Amazon Forest Landscapes. Sci. Total Environ. 2018, 644, 1044–1055. [Google Scholar] [CrossRef] [PubMed]
  56. Schielein, J.; Ponzoni Frey, G.; Miranda, J.; de Souza, R.A.; Boerner, J.; Henderson, J. The Role of Accessibility for Land Use and Land Cover Change in the Brazilian Amazon. Appl. Geogr. 2021, 132, 102419. [Google Scholar] [CrossRef]
  57. Anandkumar, A.; Vijith, H.; Nagarajan, R.; Jonathan, M.P. Evaluation of Decadal Shoreline Changes in the Coastal Region of Miri, Sarawak, Malaysia. In Coastal Management; Elsevier: Amsterdam, The Netherlands, 2019; pp. 95–119. [Google Scholar]
  58. Rousta, I.; Sarif, M.O.; Gupta, R.D.; Olafsson, H.; Ranagalage, M.; Murayama, Y.; Zhang, H.; Mushore, T.D. Spatiotemporal Analysis of Land Use/Land Cover and Its Effects on Surface Urban Heat Island Using Landsat Data: A Case Study of Metropolitan City Tehran (1988-2018). Sustainability 2018, 10, 4433. [Google Scholar] [CrossRef] [Green Version]
  59. Gogoi, P.P.; Vinoj, V.; Swain, D.; Roberts, G.; Dash, J.; Tripathy, S. Land Use and Land Cover Change Effect on Surface Temperature over Eastern India. Sci. Rep. 2019, 9, 8859. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Sun, L.; Wei, J.; Duan, D.H.; Guo, Y.M.; Yang, D.X.; Jia, C.; Mi, X.T. Impact of Land-Use and Land-Cover Change on Urban Air Quality in Representative Cities of China. J. Atmos. Solar-Terrestrial Phys. 2016, 142, 43–54. [Google Scholar] [CrossRef]
  61. Talukdar, S.; Singha, P.; Shahfahad; Mahato, S.; Praveen, B.; Rahman, A. Dynamics of Ecosystem Services (ESs) in Response to Land Use Land Cover (LU/LC) Changes in the Lower Gangetic Plain of India. Ecol. Indic. 2020, 112, 106121. [Google Scholar] [CrossRef]
  62. Lai, S.-K. Effects of Land Use Plans on Urban Development: A Property Rights Approach. J. Urban Manag. 2020, 9, 1–5. [Google Scholar] [CrossRef]
  63. Gibb, R.; Redding, D.W.; Chin, K.Q.; Donnelly, C.A.; Blackburn, T.M.; Newbold, T.; Jones, K.E. Zoonotic Host Diversity Increases in Human-Dominated Ecosystems. Nature 2020, 584, 398–402. [Google Scholar] [CrossRef] [PubMed]
  64. Turkelboom, F.; Leone, M.; Jacobs, S.; Kelemen, E.; García-Llorente, M.; Baró, F.; Termansen, M.; Barton, D.N.; Berry, P.; Stange, E.; et al. When We Cannot Have It All: Ecosystem Services Trade-Offs in the Context of Spatial Planning. Ecosyst. Serv. 2018, 29, 566–578. [Google Scholar] [CrossRef]
  65. Marengo, J.A.; Souza, C.M.; Thonicke, K.; Burton, C.; Halladay, K.; Betts, R.A.; Alves, L.M.; Soares, W.R. Changes in Climate and Land Use Over the Amazon Region: Current and Future Variability and Trends. Front. Earth Sci. 2018, 6, 1–21. [Google Scholar] [CrossRef]
  66. Weiss, M.; Jacob, F.; Duveiller, G. Remote Sensing for Agricultural Applications: A Meta-Review. Remote Sens. Environ. 2020, 236, 111402. [Google Scholar] [CrossRef]
  67. Anderson, C.B. Biodiversity Monitoring, Earth Observations and the Ecology of Scale. Ecol. Lett. 2018, 21, 1572–1585. [Google Scholar] [CrossRef] [PubMed]
  68. Chishugi, D.U.; Sonwa, D.J.; Kahindo, J.-M.; Itunda, D.; Chishugi, J.B.; Félix, F.L.; Sahani, M. How Climate Change and Land Use/Land Cover Change Affect Domestic Water Vulnerability in Yangambi Watersheds (D. R. Congo). Land 2021, 10, 165. [Google Scholar] [CrossRef]
  69. Pang, S.E.H.; De Alban, J.D.T.; Webb, E.L. Effects of Climate Change and Land Cover on the Distributions of a Critical Tree Family in the Philippines. Sci. Rep. 2021, 11, 276. [Google Scholar] [CrossRef] [PubMed]
  70. Brovelli, M.A.; Sun, Y.; Yordanov, V. Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine. ISPRS Int. J. Geo-Information 2020, 9, 580. [Google Scholar] [CrossRef]
  71. Bullock, E.L.; Woodcock, C.E.; Olofsson, P. Monitoring Tropical Forest Degradation Using Spectral Unmixing and Landsat Time Series Analysis. Remote Sens. Environ. 2020, 238, 110968. [Google Scholar] [CrossRef]
  72. Fonseca Morello, T.; Marchetti Ramos, R.; Anderson, L.O.; Owen, N.; Rosan, T.M.; Steil, L. Predicting Fires for Policy Making: Improving Accuracy of Fire Brigade Allocation in the Brazilian Amazon. Ecol. Econ. 2020, 169, 106501. [Google Scholar] [CrossRef]
  73. Rudke, A.P.; Sikora de Souza, V.A.; dos Santos, A.M.; Freitas Xavier, A.C.; Rotunno Filho, O.C.; Martins, J.A. Impact of Mining Activities on Areas of Environmental Protection in the Southwest of the Amazon: A GIS- and Remote Sensing-Based Assessment. J. Environ. Manag. 2020, 263, 110392. [Google Scholar] [CrossRef]
  74. Arantes, C.C.; Winemiller, K.O.; Petrere, M.; Castello, L.; Hess, L.L.; Freitas, C.E.C. Relationships between Forest Cover and Fish Diversity in the Amazon River Floodplain. J. Appl. Ecol. 2018, 55, 386–395. [Google Scholar] [CrossRef]
  75. Chapman, P.M.; Tobias, J.A.; Edwards, D.P.; Davies, R.G. Contrasting Impacts of Land-Use Change on Phylogenetic and Functional Diversity of Tropical Forest Birds. J. Appl. Ecol. 2018, 55, 1604–1614. [Google Scholar] [CrossRef] [Green Version]
  76. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to Conduct a Bibliometric Analysis: An Overview and Guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
  77. Mortazavi, S.; Eslami, M.H.; Hajikhani, A.; Väätänen, J. Mapping Inclusive Innovation: A Bibliometric Study and Literature Review. J. Bus. Res. 2021, 122, 736–750. [Google Scholar] [CrossRef]
  78. Zupic, I.; Čater, T. Bibliometric Methods in Management and Organization. Organ. Res. Methods 2015, 18, 429–472. [Google Scholar] [CrossRef]
  79. Juárez-Orozco, S.M.; Siebe, C.; Fernández y Fernández, D. Causes and Effects of Forest Fires in Tropical Rainforests: A Bibliometric Approach. Trop. Conserv. Sci. 2017, 10, 1–4. [Google Scholar] [CrossRef]
  80. Kandus, P.; Minotti, P.G.; Morandeira, N.S.; Grimson, R.; González Trilla, G.; González, E.B.; San Martín, L.; Gayol, M.P. Remote Sensing of Wetlands in South America: Status and Challenges. Int. J. Remote Sens. 2018, 39, 993–1016. [Google Scholar] [CrossRef]
  81. Aleixandre-Benavent, R.; Aleixandre-Tudó, J.L.; Castelló-Cogollos, L.; Aleixandre, J.L. Trends in Global Research in Deforestation. A Bibliometric Analysis. Land use policy 2018, 72, 293–302. [Google Scholar] [CrossRef]
  82. Montalván-Burbano, N.; Velastegui-Montoya, A.; Gurumendi-Noriega, M.; Morante-Carballo, F.; Adami, M. Worldwide Research on Land Use and Land Cover in the Amazon Region. Sustainability 2021, 13, 6039. [Google Scholar] [CrossRef]
  83. Sasmito, S.D.; Taillardat, P.; Fong, L.S.; Ren, J.W.F.; Sundahl, H.; Wijedasa, L.; Bandla, A.; Arifin-Wong, N.; Sudarshan, A.S.; Tarigan, S.; et al. Terrestrial and Aquatic Carbon Dynamics in Tropical Peatlands under Different Land Use Types: A Systematic Review Protocol. Forests 2021, 12, 1298. [Google Scholar] [CrossRef]
  84. Corlett, R.T. Tropical Forests. In eLS; Wiley: Hoboken, NJ, USA, 2014. [Google Scholar]
  85. Hansen, A.J.; Burns, P.; Ervin, J.; Goetz, S.J.; Hansen, M.; Venter, O.; Watson, J.E.M.; Jantz, P.A.; Virnig, A.L.S.; Barnett, K.; et al. A Policy-Driven Framework for Conserving the Best of Earth’s Remaining Moist Tropical Forests. Nat. Ecol. Evol. 2020, 4, 1377–1384. [Google Scholar] [CrossRef] [PubMed]
  86. FAO. Global Forest Resources Assessment 2020; FAO: Rome, Italy, 2020; ISBN 978-92-5-132974-0. [Google Scholar]
  87. Linnenluecke, M.K.; Marrone, M.; Singh, A.K. Conducting Systematic Literature Reviews and Bibliometric Analyses. Aust. J. Manag. 2020, 45, 175–194. [Google Scholar] [CrossRef]
  88. Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. [Google Scholar] [CrossRef]
  89. Szomszor, M.; Adams, J.; Fry, R.; Gebert, C.; Pendlebury, D.A.; Potter, R.W.K.; Rogers, G. Interpreting Bibliometric Data. Front. Res. Metrics Anal. 2021, 5. [Google Scholar] [CrossRef]
  90. Liu, H.; Liu, Y.; Wang, Y.; Pan, C. Hot Topics and Emerging Trends in Tourism Forecasting Research: A Scientometric Review. Tour. Econ. 2019, 25, 448–468. [Google Scholar] [CrossRef]
  91. Moral-Muñoz, J.A.; Herrera-Viedma, E.; Santisteban-Espejo, A.; Cobo, M.J. Software Tools for Conducting Bibliometric Analysis in Science: An up-to-Date Review. El Prof. la Inf. 2020, 29, 1–20. [Google Scholar] [CrossRef] [Green Version]
  92. Geissdoerfer, M.; Savaget, P.; Bocken, N.M.P.; Hultink, E.J. The Circular Economy – A New Sustainability Paradigm? J. Clean. Prod. 2017, 143, 757–768. [Google Scholar] [CrossRef]
  93. Payán-Sánchez, B.; Belmonte-Ureña, L.J.; Plaza-úbeda, J.A.; Vazquez-Brust, D.; Yakovleva, N.; Pérez-Valls, M. Open Innovation for Sustainability or Not: Literature Reviews of Global Research Trends. Sustainability 2021, 13, 1136. [Google Scholar] [CrossRef]
  94. Su, X.; Li, X.; Kang, Y. A Bibliometric Analysis of Research on Intangible Cultural Heritage Using CiteSpace. SAGE Open 2019, 9, 215824401984011. [Google Scholar] [CrossRef] [Green Version]
  95. Álvarez-García, J.; Maldonado-Erazo, C.P.; Río-Rama, M. de la C. Del; Castellano-Álvarez, F.J. Cultural Heritage and Tourism Basis for Regional Development: Mapping of Scientific Coverage. Sustainability 2019, 11, 6034. [Google Scholar] [CrossRef] [Green Version]
  96. Pinos, J.; Quesada-Román, A. Flood Risk-Related Research Trends in Latin America and the Caribbean. Water 2022, 14, 10. [Google Scholar] [CrossRef]
  97. Herrera-Franco, G.; Montalván-Burbano, N.; Carrión-Mero, P.; Jaya-Montalvo, M.; Gurumendi-Noriega, M. Worldwide Research on Geoparks through Bibliometric Analysis. Sustainability 2021, 13, 1175. [Google Scholar] [CrossRef]
  98. Blettler, M.C.M.; Abrial, E.; Khan, F.R.; Sivri, N.; Espinola, L.A. Freshwater Plastic Pollution: Recognizing Research Biases and Identifying Knowledge Gaps. Water Res. 2018, 143, 416–424. [Google Scholar] [CrossRef] [Green Version]
  99. Velasco-Muñoz, J.F.; Aznar-Sánchez, J.A.; Belmonte-Ureña, L.J.; López-Serrano, M.J. Advances in Water Use Efficiency in Agriculture: A Bibliometric Analysis. Water 2018, 10, 377. [Google Scholar] [CrossRef] [Green Version]
  100. Punnakitikashem, P.; Hallinger, P. Bibliometric Review of the Knowledge Base on Healthcare Management for Sustainability, 1994-2018. Sustainability 2020, 12, 205. [Google Scholar] [CrossRef] [Green Version]
  101. Newell, J.P.; Cousins, J.J. The Boundaries of Urban Metabolism. Prog. Hum. Geogr. 2015, 39, 702–728. [Google Scholar] [CrossRef] [Green Version]
  102. Van der Meer, F. Remote-Sensing Image Analysis and Geostatistics. Int. J. Remote Sens. 2012, 33, 5644–5676. [Google Scholar] [CrossRef]
  103. Guerra, A.; Reis, L.K.; Borges, F.L.G.; Ojeda, P.T.A.; Pineda, D.A.M.; Miranda, C.O.; de Maidana, D.P.F.L.; dos Santos, T.M.R.; Shibuya, P.S.; Marques, M.C.M.; et al. Ecological Restoration in Brazilian Biomes: Identifying Advances and Gaps. For. Ecol. Manag. 2020, 458, 117802. [Google Scholar] [CrossRef]
  104. Huang, L.; Zhou, M.; Lv, J.; Chen, K. Trends in Global Research in Forest Carbon Sequestration: A Bibliometric Analysis. J. Clean. Prod. 2020, 252, 119908. [Google Scholar] [CrossRef]
  105. Malhado, A.C.M.; de Azevedo, R.S.D.; Todd, P.A.; Santos, A.M.C.; Fabré, N.N.; Batista, V.S.; Aguiar, L.J.G.; Ladle, R.J. Geographic and Temporal Trends in Amazonian Knowledge Production. Biotropica 2014, 46, 6–13. [Google Scholar] [CrossRef]
  106. Andrés, A. Introduction. In Measuring Academic Research; Elsevier: Amsterdam, The Netherlands, 2009; pp. 1–12. [Google Scholar]
  107. Baminiwatta, A.; Solangaarachchi, I. Trends and Developments in Mindfulness Research over 55 Years: A Bibliometric Analysis of Publications Indexed in Web of Science. Mindfulness (N. Y). 2021, 12, 2099–2116. [Google Scholar] [CrossRef] [PubMed]
  108. Caputo, A.; Kargina, M. A User-Friendly Method to Merge Scopus and Web of Science Data during Bibliometric Analysis. J. Mark. Anal. 2021. [Google Scholar] [CrossRef]
  109. Vera-Baceta, M.-A.; Thelwall, M.; Kousha, K. Web of Science and Scopus Language Coverage. Scientometrics 2019, 121, 1803–1813. [Google Scholar] [CrossRef]
  110. Baas, J.; Schotten, M.; Plume, A.; Côté, G.; Karimi, R. Scopus as a Curated, High-Quality Bibliometric Data Source for Academic Research in Quantitative Science Studies. Quant. Sci. Stud. 2020, 1, 377–386. [Google Scholar] [CrossRef]
  111. Bozkurt, A.; Koseoglu, S.; Singh, L. An Analysis of Peer Reviewed Publications on Openness in Education in Half a Century: Trends and Patterns in the Open Hemisphere. Australas. J. Educ. Technol. 2019, 35, 78–97. [Google Scholar] [CrossRef]
  112. Pranckutė, R. Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications 2021, 9, 12. [Google Scholar] [CrossRef]
  113. Carrión-Mero, P.; Montalván-Burbano, N.; Herrera-Narváez, G.; Morante-Carballo, F. Geodiversity and Mining towards the Development of Geotourism: A Global Perspective. Int. J. Des. Nat. Ecodynamics 2021, 16, 191–201. [Google Scholar] [CrossRef]
  114. Si, H.; Shi, J.; Tang, D.; Wen, S.; Miao, W.; Duan, K. Application of the Theory of Planned Behavior in Environmental Science: A Comprehensive Bibliometric Analysis. Int. J. Environ. Res. Public Health 2019, 16, 2788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Jin, R.; Gao, S.; Cheshmehzangi, A.; Aboagye-Nimo, E. A Holistic Review of Off-Site Construction Literature Published between 2008 and 2018. J. Clean. Prod. 2018, 202, 1202–1219. [Google Scholar] [CrossRef] [Green Version]
  116. Oraee, M.; Hosseini, M.R.; Papadonikolaki, E.; Palliyaguru, R.; Arashpour, M. Collaboration in BIM-Based Construction Networks: A Bibliometric-Qualitative Literature Review. Int. J. Proj. Manag. 2017, 35, 1288–1301. [Google Scholar] [CrossRef]
  117. Martín-Martín, A.; Orduna-Malea, E.; Thelwall, M.; Delgado López-Cózar, E. Google Scholar, Web of Science, and Scopus: A Systematic Comparison of Citations in 252 Subject Categories. J. Informetr. 2018, 12, 1160–1177. [Google Scholar] [CrossRef] [Green Version]
  118. Calma, A.; Davies, M. Academy of Management Journal, 1958–2014: A Citation Analysis. Scientometrics 2016, 108, 959–975. [Google Scholar] [CrossRef]
  119. Muhuri, P.K.; Shukla, A.K.; Abraham, A. Industry 4.0: A Bibliometric Analysis and Detailed Overview. Eng. Appl. Artif. Intell. 2019, 78, 218–235. [Google Scholar] [CrossRef]
  120. Najmi, A.; Rashidi, T.H.; Abbasi, A.; Travis Waller, S. Reviewing the Transport Domain: An Evolutionary Bibliometrics and Network Analysis. Scientometrics 2017, 110, 843–865. [Google Scholar] [CrossRef]
  121. Hallinger, P.; Kovačević, J. A Bibliometric Review of Research on Educational Administration: Science Mapping the Literature, 1960 to 2018. Rev. Educ. Res. 2019, 89, 335–369. [Google Scholar] [CrossRef]
  122. Sweileh, W.M.; Al-Jabi, S.W.; AbuTaha, A.S.; Zyoud, S.H.; Anayah, F.M.A.; Sawalha, A.F. Bibliometric Analysis of Worldwide Scientific Literature in Mobile - Health: 2006–2016. BMC Med. Inform. Decis. Mak. 2017, 17, 72. [Google Scholar] [CrossRef] [Green Version]
  123. Carrión-Mero, P.; Montalván-Burbano, N.; Morante-Carballo, F.; Quesada-Román, A.; Apolo-Masache, B. Worldwide Research Trends in Landslide Science. Int. J. Environ. Res. Public Health 2021, 18, 9445. [Google Scholar] [CrossRef]
  124. van Eck, N.J.; Waltman, L. Visualizing Bibliometric Networks BT - Measuring Scholarly Impact: Methods and Practice; Ding, Y., Rousseau, R., Wolfram, D., Eds.; Springer International Publishing: Cham, Switzerland, 2014; pp. 285–320. ISBN 978-3-319-10377-8. [Google Scholar]
  125. Waltman, L.; van Eck, N.J.; Noyons, E.C.M. A Unified Approach to Mapping and Clustering of Bibliometric Networks. J. Informetr. 2010, 4, 629–635. [Google Scholar] [CrossRef] [Green Version]
  126. van Eck, N.J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Duque-Acevedo, M.; Belmonte-Ureña, L.J.; Cortés-García, F.J.; Camacho-Ferre, F. Agricultural Waste: Review of the Evolution, Approaches and Perspectives on Alternative Uses. Glob. Ecol. Conserv. 2020, 22. [Google Scholar] [CrossRef]
  128. Kavle, R.R.; Pritchard, E.T.M.; Bekhit, A.E.-D.A.; Carne, A.; Agyei, D. Edible Insects: A Bibliometric Analysis and Current Trends of Published Studies (1953–2021). Int. J. Trop. Insect Sci. 2022. [Google Scholar] [CrossRef]
  129. Mishra, H.G.; Pandita, S.; Bhat, A.A.; Mishra, R.K.; Sharma, S. Tourism and Carbon Emissions: A Bibliometric Review of the Last Three Decades: 1990–2021. Tour. Rev. 2022, 77, 636–658. [Google Scholar] [CrossRef]
  130. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field. J. Informetr. 2011, 5, 146–166. [Google Scholar] [CrossRef]
  131. Morante-Carballo, F.; Montalván-Burbano, N.; Carrión-Mero, P.; Jácome-Francis, K. Worldwide Research Analysis on Natural Zeolites as Environmental Remediation Materials. Sustainability 2021, 13, 6378. [Google Scholar] [CrossRef]
  132. Liu, C.; Li, K. Mapping the Field: A Bibliometric Analysis of Land Use and Carbon Emissions (LUCE) Research from 1987 to 2018. Libr. Hi Tech 2021, 39, 396–411. [Google Scholar] [CrossRef]
  133. Ye, C. Bibliometrical Analysis of International Big Data Research: Based on Citespace and VOSviewer. In Proceedings of the 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Huangshan, China, 28–30 July 2018; pp. 927–932. [Google Scholar]
  134. Morante-Carballo, F.; Montalván-Burbano, N.; Carrión-Mero, P.; Espinoza-Santos, N. Cation Exchange of Natural Zeolites: Worldwide Research. Sustainability 2021, 13, 7751. [Google Scholar] [CrossRef]
  135. Batistič, S.; Kaše, R. The Organizational Socialization Field Fragmentation: A Bibliometric Review. Scientometrics 2015, 104, 121–146. [Google Scholar] [CrossRef]
  136. Denevan, W. The Causes and Consequences of Shifting Cultivation in Relation to Tropical Forest Survival. Publ. Ser. Conf. Lat. Am. Geogr. 1978, 7, 67–82. [Google Scholar]
  137. Grenzebach, K. Population Pressure and Areas of Potential Rural Development in Southern Nigeria. Geo J. 1978, 2, 215–224. [Google Scholar] [CrossRef] [PubMed]
  138. Roche, l. Forestry and the Conservation of Plants and Animals in the Tropics. For. Ecol. Manag. 1979, 2, 103–122. [Google Scholar] [CrossRef]
  139. Steinlin, H. Development of New Agro- Forestry Land Use Systems in the Humid Tropics. Plant Res. Dev. 1979, 10, 7–17. [Google Scholar]
  140. Matthews, E. Global Vegetation and Land Use: New High-Resolution Data Bases for Climate Studies. J. Clim. Appl. Meteorol. 1983, 22, 474–487. [Google Scholar] [CrossRef]
  141. Detwiler, R.P.; Hall, C.A.S. Tropical Forests and the Global Carbon Cycle. Science 1988, 239, 42–47. [Google Scholar] [CrossRef]
  142. Kasischke, E.S.; Melack, J.M.; Dobson, M.C. The Use of Imaging Radars for Applications A Review Ecological; ©Elsevier Science Inc.: New York, NY, USA, 1996; Volume 59. [Google Scholar]
  143. Moran, E.F.; Brondizio, E.; Mausel, P.; Wu, Y. Integrating Amazonian Vegetation, Land-Use, and Satellite Data. Bioscience 1994, 44, 329–338. [Google Scholar] [CrossRef]
  144. Pfaff, A.S.P. What Drives Deforestation in the Brazilian Amazon? J. Environ. Econ. Manag. 1999, 37, 26–43. [Google Scholar] [CrossRef]
  145. Aide, T.M.; Zimmerman, J.K.; Herrera, L.; Rosario, M.; Serrano, M. Forest Recovery in Abandoned Tropical Pastures in Puerto Rico. For. Ecol. Manag. 1995, 77, 77–86. [Google Scholar] [CrossRef]
  146. Brown, K. Diversity, Disturbance, and Sustainable Use of Neotropical Forests: Insects as Indicators for Conservation Monitoring. J. Insect Conserv. 1997, 1, 25–42. [Google Scholar] [CrossRef]
  147. Brown, S.; Lugo, A.E. Tropical Secondary Forests. J. Trop. Ecol. 1990, 6, 1–32. [Google Scholar] [CrossRef]
  148. Hughes, R.F.; Kauffman, J.B.; Jaramillo, V.J. Biomass, Carbon, and Nutrient Dynamics of Secondary Forests in a Humid Tropical Region of Mexico. Ecology 1999, 80, 1892–1907. [Google Scholar]
  149. Nepstad, D.C.; de Carvalho, C.R.; Davidson, E.A.; Jipp, P.H.; Lefebvre, P.A.; Negreiros, G.H.; da Silva, E.D.; Stone, T.A.; Trumbore, S.E.; Vieira, S. The Role of Deep Roots in the Hydrological and Carbon Cycles of Amazonian Forests and Pastures. Nature 1994, 372, 666–669. [Google Scholar] [CrossRef]
  150. Fearnside, P.M.; Imbrozio Barbosa, R. Soil Carbon Changes from Conversion of Forest to Pasture in Brazilian Amazonia. For. Ecol. Manag. 1998, 108, 147–166. [Google Scholar] [CrossRef]
  151. Houghton, R.A. The Annual Net Flux of Carbon to the Atmosphere from Changes in Land Use 1850–1990. Tellus B Chem. Phys. Meteorol. 1999, 51, 298–313. [Google Scholar] [CrossRef] [Green Version]
  152. Neill, C.; Melillo, J.M.; Steudler, P.A.; Cerri, C.C.; de Moraes, J.F.L.; Piccolo, M.C.; Brito, M. Soil Carbon and Nitrogen Stocks Following Forest Clearing for Pasture in the Southwestern Brazilian Amazon. Ecol. Appl. 1997, 7, 1216. [Google Scholar] [CrossRef]
  153. Verchot, L.V.; Davidson, E.A.; Cattânio, H.; Ackerman, I.L.; Erickson, H.E.; Keller, M. Land Use Change and Biogeochemical Controls of Nitrogen Oxide Emissions from Soils in Eastern Amazonia. Global Biogeochem. Cycles 1999, 13, 31–46. [Google Scholar] [CrossRef]
  154. Cochrane, M.A.; Alencar, A.; Schulze, M.D.; Souza, C.M.; Nepstad, D.C.; Lefebvre, P.; Davidson, E.A. Positive Feedbacks in the Fire Dynamic of Closed Canopy Tropical Forests. Science 1999, 284, 1832–1835. [Google Scholar] [CrossRef]
  155. De Castro, E.; Kauffman, J. Ecosystem Structure in the Brazilian Cerrado: A Vegetation Gradient of Aboveground Biomass, Root Mass and Consumption by Fire. J. Trop. Ecol. 1998, 14, 263–283. [Google Scholar] [CrossRef]
  156. Nepstad, D.C.; Verssimo, A.; Alencar, A.; Nobre, C.; Lima, E.; Lefebvre, P.; Schlesinger, P.; Potter, C.; Moutinho, P.; Mendoza, E.; et al. Large-Scale Impoverishment of Amazonian Forests by Logging and Fire. Nature 1999, 398, 505–508. [Google Scholar] [CrossRef]
  157. Klink, C.A.; Machado, R.B. Conservation of the Brazilian Cerrado. Conserv. Biol. 2005, 19, 707–713. [Google Scholar] [CrossRef]
  158. Feddema, J.J.; Oleson, K.W.; Bonan, G.B.; Mearns, L.O.; Buja, L.E.; Meehl, G.A.; Washington, W.M. The Importance of Land-Cover Change in Simulating Future Climates. Science 2005, 310, 1674–1678. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  159. Lawton, R.O.; Nair, U.S.; Pielke, R.A.; Welch, R.M. Climatic Impact of Tropical Lowland Deforestation on Nearby Montane Cloud Forests. Science 2001, 294, 584–587. [Google Scholar] [CrossRef] [PubMed]
  160. Scholze, M.; Knorr, W.; Arnell, N.W.; Prentice, I.C. A Climate-Change Risk Analysis for World Ecosystems. Proc. Natl. Acad. Sci. USA 2006, 103, 13116–13120. [Google Scholar] [CrossRef] [Green Version]
  161. Chazdon, R.L. Tropical Forest Recovery: Legacies of Human Impact and Natural Disturbances. Perspect. Plant Ecol. Evol. Syst. 2003, 6, 51–71. [Google Scholar] [CrossRef] [Green Version]
  162. Gardner, T.A.; Barlow, J.; Chazdon, R.; Ewers, R.M.; Harvey, C.A.; Peres, C.A.; Sodhi, N.S. Prospects for Tropical Forest Biodiversity in a Human-Modified World. Ecol. Lett. 2009, 12, 561–582. [Google Scholar] [CrossRef] [Green Version]
  163. Fearnside, P.M. Soybean Cultivation as a Threat to the Environment in Brazil. Environ. Conserv. 2001, 28, 23–38. [Google Scholar] [CrossRef] [Green Version]
  164. Li, H.; Aide, T.M.; Ma, Y.; Liu, W.; Cao, M. Demand for Rubber Is Causing the Loss of High Diversity Rain Forest in SW China. Biodivers. Conserv. 2007, 16, 1731–1745. [Google Scholar] [CrossRef]
  165. Martinelli, L.A.; Filoso, S. Expansion of Sugarcane Ethanol Production In Brazil: Environmental and Social Challenges. Ecol. Appl. 2008, 18, 885–898. [Google Scholar] [CrossRef] [Green Version]
  166. U.S. Geological Survey. Available online: https://www.usgs.gov/ (accessed on 12 July 2022).
  167. NASA Earth Data. Available online: https://www.earthdata.nasa.gov/ (accessed on 11 July 2022).
  168. Baccini, A.; Walker, W.; Carvalho, L.; Farina, M.; Sulla-Menashe, D.; Houghton, R.A. Tropical Forests Are a Net Carbon Source Based on Aboveground Measurements of Gain and Loss. Science 2017, 358, 230–234. [Google Scholar] [CrossRef] [Green Version]
  169. Dargie, G.C.; Lewis, S.L.; Lawson, I.T.; Mitchard, E.T.A.; Page, S.E.; Bocko, Y.E.; Ifo, S.A. Age, Extent and Carbon Storage of the Central Congo Basin Peatland Complex. Nature 2017, 542, 86–90. [Google Scholar] [CrossRef] [PubMed]
  170. Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, P.E.; Kurz, W.A.; Phillips, O.L.; Shvidenko, A.; Lewis, S.L.; Canadell, J.G.; et al. A Large and Persistent Carbon Sink in the World’s Forests. Science 2011, 333, 988–993. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  171. Asner, G.P.; Powell, G.V.N.; Mascaro, J.; Knapp, D.E.; Clark, J.K.; Jacobson, J.; Kennedy-Bowdoin, T.; Balaji, A.; Paez-Acosta, G.; Victoria, E.; et al. High-Resolution Forest Carbon Stocks and Emissions in the Amazon. Proc. Natl. Acad. Sci. USA 2010, 107, 16738–16742. [Google Scholar] [CrossRef] [PubMed]
  172. Baccini, A.; Goetz, S.J.; Walker, W.S.; Laporte, N.T.; Sun, M.; Sulla-Menashe, D.; Hackler, J.; Beck, P.S.A.; Dubayah, R.; Friedl, M.A.; et al. Estimated Carbon Dioxide Emissions from Tropical Deforestation Improved by Carbon-Density Maps. Nat. Clim. Chang. 2012, 2, 182–185. [Google Scholar] [CrossRef]
  173. Schimel, D.; Stephens, B.B.; Fisher, J.B. Effect of Increasing CO 2 on the Terrestrial Carbon Cycle. Proc. Natl. Acad. Sci. USA 2015, 112, 436–441. [Google Scholar] [CrossRef] [Green Version]
  174. Gibson, L.; Lee, T.M.; Koh, L.P.; Brook, B.W.; Gardner, T.A.; Barlow, J.; Peres, C.A.; Bradshaw, C.J.A.; Laurance, W.F.; Lovejoy, T.E.; et al. Primary Forests Are Irreplaceable for Sustaining Tropical Biodiversity. Nature 2011, 478, 378–381. [Google Scholar] [CrossRef]
  175. Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  176. Keenan, R.J.; Reams, G.A.; Achard, F.; de Freitas, J.V.; Grainger, A.; Lindquist, E. Dynamics of Global Forest Area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manag. 2015, 352, 9–20. [Google Scholar] [CrossRef]
  177. Dubayah, R.O.; Sheldon, S.L.; Clark, D.B.; Hofton, M.A.; Blair, J.B.; Hurtt, G.C.; Chazdon, R.L. Estimation of Tropical Forest Height and Biomass Dynamics Using Lidar Remote Sensing at La Selva, Costa Rica. J. Geophys. Res. Biogeosciences 2010, 115, 1–17. [Google Scholar] [CrossRef]
  178. Mitchard, E.T.A.; Feldpausch, T.R.; Brienen, R.J.W.; Lopez-Gonzalez, G.; Monteagudo, A.; Baker, T.R.; Lewis, S.L.; Lloyd, J.; Quesada, C.A.; Gloor, M.; et al. Markedly Divergent Estimates of Amazon Forest Carbon Density from Ground Plots and Satellites. Glob. Ecol. Biogeogr. 2014, 23, 935–946. [Google Scholar] [CrossRef] [Green Version]
  179. Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.-M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global Estimates of Evapotranspiration and Gross Primary Production Based on MODIS and Global Meteorology Data. Remote Sens. Environ. 2010, 114, 1416–1431. [Google Scholar] [CrossRef] [Green Version]
  180. Arima, E.Y.; Barreto, P.; Araújo, E.; Soares-Filho, B. Public Policies Can Reduce Tropical Deforestation: Lessons and Challenges from Brazil. Land use policy 2014, 41, 465–473. [Google Scholar] [CrossRef]
  181. Holl, K.D.; Aide, T.M. When and Where to Actively Restore Ecosystems? For. Ecol. Manag. 2011, 261, 1558–1563. [Google Scholar] [CrossRef]
  182. Porter-Bolland, L.; Ellis, E.A.; Guariguata, M.R.; Ruiz-Mallén, I.; Negrete-Yankelevich, S.; Reyes-García, V. Community Managed Forests and Forest Protected Areas: An Assessment of Their Conservation Effectiveness across the Tropics. For. Ecol. Manag. 2012, 268, 6–17. [Google Scholar] [CrossRef]
  183. Price, D.; de Solla Price, D. Little Science, Big Science-- and Beyond; Columbia University Press: New York, NY, USA, 1986; ISBN 9780231049566. [Google Scholar]
  184. Shan, W.; Wang, J. Mapping the Landscape and Evolutions of Green Supply Chain Management. Sustainability 2018, 10, 597. [Google Scholar] [CrossRef] [Green Version]
  185. Gibbs, H.K.; Ruesch, A.S.; Achard, F.; Clayton, M.K.; Holmgren, P.; Ramankutty, N.; Foley, J.A. Tropical Forests Were the Primary Sources of New Agricultural Land in the 1980s and 1990s. Proc. Natl. Acad. Sci. USA 2010, 107, 16732–16737. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Houghton, R.A. Aboveground Forest Biomass and the Global Carbon Balance. Glob. Chang. Biol. 2005, 11, 945–958. [Google Scholar] [CrossRef]
  187. Gu, Z.; Meng, F.; Farrukh, M. Mapping the Research on Knowledge Transfer: A Scientometrics Approach. IEEE Access 2021, 9, 34647–34659. [Google Scholar] [CrossRef]
  188. Vieira, I.C.G.; De Almeida, A.S.; Davidson, E.A.; Stone, T.A.; Reis De Carvalho, C.J.; Guerrero, J.B. Classifying Successional Forests Using Landsat Spectral Properties and Ecological Characteristics in Eastern Amazônia. Remote Sens. Environ. 2003, 87, 470–481. [Google Scholar] [CrossRef]
  189. Neill, C.; Deegan, L.A.; Thomas, S.M.; Cerri, C.C. Deforestation for Pasture Alters Nitrogen and Phosphorus in Small Amazonian Streams. Ecol. Appl. 2001, 11, 1817–1828. [Google Scholar] [CrossRef]
  190. Nepstad, D.; McGrath, D.; Stickler, C.; Alencar, A.; Azevedo, A.; Swette, B.; Bezerra, T.; DiGiano, M.; Shimada, J.; Da Motta, R.S.; et al. Slowing Amazon Deforestation through Public Policy and Interventions in Beef and Soy Supply Chains. Science 2014, 344, 1118–1123. [Google Scholar] [CrossRef] [PubMed]
  191. Adams, J.; Sabol, D.; Kapos, V.; Almeida, R.; Roberts, D.; Smith, M.; Gillespie, A. Classification of Multispectral Images Based on Fractions of Endmembers: Application to Land-Cover Change in the Brazilian Amazon. Remote Sens. Environ. 1995, 52, 137–154. [Google Scholar] [CrossRef]
  192. Souza, C.M.; Siqueira, J.V.; Sales, M.H.; Fonseca, A.V.; Ribeiro, J.G.; Numata, I.; Cochrane, M.A.; Barber, C.P.; Roberts, D.A.; Barlow, J. Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon. Remote Sens. 2013, 5, 5493–5513. [Google Scholar] [CrossRef]
  193. Dawson, N.; Martin, A. Assessing the Contribution of Ecosystem Services to Human Wellbeing: A Disaggregated Study in Western Rwanda. Ecol. Econ. 2015, 117, 62–72. [Google Scholar] [CrossRef] [Green Version]
  194. Scales, B.R.; Marsden, S.J. Biodiversity in Small-Scale Tropical Agroforests: A Review of Species Richness and Abundance Shifts and the Factors Influencing Them. Environ. Conserv. 2008, 35, 160–172. [Google Scholar] [CrossRef]
  195. Mayle, F.E.; Langstroth, R.P.; Fisher, R.A.; Meir, P. Long-Term Forest-Savannah Dynamics in the Bolivian Amazon: Implications for Conservation. Philos. Trans. R. Soc. B Biol. Sci. 2007, 362, 291–307. [Google Scholar] [CrossRef] [Green Version]
  196. Pellier, A.S.; Wells, J.A.; Abram, N.K.; Gaveau, D.; Meijaard, E. Through the Eyes of Children: Perceptions of Environmental Change in Tropical Forests. PLoS ONE 2014, 9, e22722. [Google Scholar] [CrossRef]
  197. Nelson, A.; Chomitz, K.M. Effectiveness of Strict vs. Multiple Use Protected Areas in Reducing Tropical Forest Fires: A Global Analysis Using Matching Methods. PLoS ONE 2011, 6, e103005. [Google Scholar] [CrossRef]
  198. Donthu, N.; Kumar, S.; Pattnaik, D. Forty-Five Years of Journal of Business Research: A Bibliometric Analysis. J. Bus. Res. 2020, 109, 1–14. [Google Scholar] [CrossRef]
  199. Morton, D.C.; DeFries, R.S.; Shimabukuro, Y.E.; Anderson, L.O.; Arai, E.; Del Bon Espirito-Santo, F.; Freitas, R.; Morisette, J. Cropland Expansion Changes Deforestation Dynamics in the Southern Brazilian Amazon. Proc. Natl. Acad. Sci. USA 2006, 103, 14637–14641. [Google Scholar] [CrossRef] [Green Version]
  200. Beuchle, R.; Grecchi, R.C.; Shimabukuro, Y.E.; Seliger, R.; Eva, H.D.; Sano, E.; Achard, F. Land Cover Changes in the Brazilian Cerrado and Caatinga Biomes from 1990 to 2010 Based on a Systematic Remote Sensing Sampling Approach. Appl. Geogr. 2015, 58, 116–127. [Google Scholar] [CrossRef]
  201. Macedo, M.N.; DeFries, R.S.; Morton, D.C.; Stickler, C.M.; Galford, G.L.; Shimabukuro, Y.E. Decoupling of Deforestation and Soy Production in the Southern Amazon during the Late 2000s. Proc. Natl. Acad. Sci. USA 2012, 109, 1341–1346. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  202. Shimabukuro, Y.E.; Duarte, V.; Arai, E.; Freitas, R.M.; Martini, P.R.; Lima, A. Monitoring Land Cover in Acre State, Western Brazilian Amazonia, Using Multitemporal Remote Sensing Data. Int. J. Image Data Fusion 2010, 1, 325–335. [Google Scholar] [CrossRef]
  203. Almeida-Filho, R.; Shimabukuro, Y.E. Digital Processing of a Landsat-TM Time Series for Mapping and Monitoring Degraded Areas Caused by Independent Gold Miners, Roraima State, Brazilian Amazon. Remote Sens. Environ. 2002, 79, 42–50. [Google Scholar] [CrossRef]
  204. Anderson, L.O.; Malhi, Y.; Ladle, R.J.; Aragão, L.E.O.C.; Shimabukuro, Y.; Phillips, O.L.; Baker, T.; Costa, A.C.L.; Espejo, J.S.; Higuchi, N.; et al. Influence of Landscape Heterogeneity on Spatial Patterns of Wood Productivity, Wood Specific Density and above Ground Biomass in Amazonia. Biogeosciences 2009, 6, 1883–1902. [Google Scholar] [CrossRef] [Green Version]
  205. Mataveli, G.A.V.; Pereira, G.; Chaves, M.E.D.; Cardozo, F. da S.; Stark, S.C.; Shimabukuro, Y.E.; Aragão, L.E.O.C.; de Oliveira, G.; Chen, J.M. Deforestation and Land Use and Land Cover Changes in Protected Areas of the Brazilian Cerrado: Impacts on the Fire-Driven Emissions of Fine Particulate Aerosols Pollutants. Remote Sens. Lett. 2021, 12, 79–92. [Google Scholar] [CrossRef]
  206. Maeda, E.E.; Formaggio, A.R.; Shimabukuro, Y.E. Impacts of Land Use and Land Cover Changes on Sediment Yield in a Brazilian Amazon Drainage Basin. GIScience Remote Sens. 2008, 45, 443–453. [Google Scholar] [CrossRef]
  207. Davidson, E.A.; Vitousek, P.M.; Matson, P.A.; Riley, R.; Garcia- Mendez, G.; Maass, J.M. Soil Emissions of Nitric Oxide in a Seasonally Dry Tropical Forest of Mexico. J. Geophys. Res. 1991, 96, 439–445. [Google Scholar] [CrossRef]
  208. Davidson, E.A.; Bustamante, M.M.; de Siqueira Pinto, A. Emissions of Nitrous Oxide and Nitric Oxide from Soils of Native and Exotic Ecosystems of the Amazon and Cerrado Regions of Brazil. Sci. World J. 2001, 1, 312–319. [Google Scholar] [CrossRef] [Green Version]
  209. Verchot, L.V.; Davidson, E.A.; Cattânio, J.H.; Ackerman, I.L. Land-Use Change and Biogeochemical Controls of Methane Fluxes in Soils of Eastern Amazonia. Ecosystems 2000, 3, 41–56. [Google Scholar] [CrossRef]
  210. Erickson, H.; Keller, M.; Davidson, E.A. Nitrogen Oxide Fluxes and Nitrogen Cycling during Postagricultural Succession and Forest Fertilization in the Humid Tropics. Ecosystems 2001, 4, 67–84. [Google Scholar] [CrossRef]
  211. Davidson, E.A.; De Carvalho, C.J.R.; Figueira, A.M.; Ishida, F.Y.; Ometto, J.P.H.B.; Nardoto, G.B.; Sabá, R.T.; Hayashi, S.N.; Leal, E.C.; Vieira, I.C.G.; et al. Recuperation of Nitrogen Cycling in Amazonian Forests Following Agricultural Abandonment. Nature 2007, 447, 995–998. [Google Scholar] [CrossRef] [PubMed]
  212. Aragão, L.E.O.C.; Shimabukuro, Y.E.; Espírito Santo, F.D.B.; Williams, M. Landscape Pattern and Spatial Variability of Leaf Area Index in Eastern Amazonia. For. Ecol. Manag. 2005, 211, 240–256. [Google Scholar] [CrossRef]
  213. Anderson, L.O.; Aragão, L.E.O.C.; Gloor, M.; Arai, E.; Adami, M.; Saatchi, S.S.; Malhi, Y.; Shimabukuro, Y.E.; Barlow, J.; Berenguer, E.; et al. Disentangling the Contribution of Multiple Land Covers to Fire-Mediated Carbon Emissions in Amazonia during the 2010 Drought. Global Biogeochem. Cycles 2015, 29, 1739–1753. [Google Scholar] [CrossRef] [Green Version]
  214. da Silva, S.S.; Oliveira, I.; Morello, T.F.; Anderson, L.O.; Karlokoski, A.; Brando, P.M.; de Melo, A.W.F.; da Costa, J.G.; de Souza, F.S.C.; da Silva, I.S.; et al. Burning in Southwestern Brazilian Amazonia, 2016–2019. J. Environ. Manag. 2021, 286. [Google Scholar] [CrossRef]
  215. Fearnside, P.M. Global Warming and Tropical Land-Use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation. Clim. Change 2000, 46, 115–158. [Google Scholar] [CrossRef]
  216. Fearnside, P.M. Forests and Global Warming Mitigation in Brazil: Opportunities in the Brazilian Forest Sector for Responses to Global Warming under the “Clean Development Mechanism”. Biomass and Bioenergy 1999, 16, 171–189. [Google Scholar] [CrossRef]
  217. Fearnside, P.M.; Righi, C.A.; Graça, P.M.L. de A.; Keizer, E.W.H.; Cerri, C.C.; Nogueira, E.M.; Barbosa, R.I. Biomass and Greenhouse-Gas Emissions from Land-Use Change in Brazil’s Amazonian “Arc of Deforestation”: The States of Mato Grosso and Rondônia. For. Ecol. Manag. 2009, 258, 1968–1978. [Google Scholar] [CrossRef]
  218. Barlow, J.; Mestre, L.A.M.; Gardner, T.A.; Peres, C.A. The Value of Primary, Secondary and Plantation Forests for Amazonian Birds. Biol. Conserv. 2007, 136, 212–231. [Google Scholar] [CrossRef]
  219. Barlow, J.; Gardner, T.A.; Araujo, I.S.; Ávila-Pires, T.C.; Bonaldo, A.B.; Costa, J.E.; Esposito, M.C.; Ferreira, L.V.; Hawes, J.; Hernandez, M.I.M.; et al. Quantifying the Biodiversity Value of Tropical Primary, Secondary, and Plantation Forests. Proc. Natl. Acad. Sci. USA 2007, 104, 18555–18560. [Google Scholar] [CrossRef] [Green Version]
  220. Zambrano, A.M.A.; Broadbent, E.N.; Schmink, M.; Perz, S.G.; Asner, G.P. Deforestation Drivers in Southwest Amazonia: Comparing Smallholder Farmers in Iñapari, Peru, and Assis Brasil, Brazil. Conserv. Soc. 2010, 8, 157–170. [Google Scholar] [CrossRef]
  221. Perz Robert, T.; Stephen, G. Household Life Cycles and Secondary Forest Cover among Small Farm Colonists in the Amazon. World Dev. 2002, 30, 1009–1027. [Google Scholar] [CrossRef]
  222. Batistella, M.; Alves, D.S.; Moran, E.F.; Souza, C.; Walker, R.; Walsh, S.J. People and Environment in Amazonia: The LBA Experience and Other Perspectives. In Amazonia and Global Change; Keller, M., Bustamante, M., Gash, J., Silva, P., Eds.; Geophysical Monograph Series: Washington, DC, USA, 2013; pp. 1–9. ISBN 9781118670347. [Google Scholar]
  223. McCracken, S.D.; Brondizio, E.S.; Nelson, D.; Moran, E.F.; Siqueira, A.D.; Rodriguez-Pedraza, C. Remote Sensing and GIS at Farm Property Level: Demography and Deforestation in the Brazilian Amazon. Photogramm. Eng. Remote Sensing 1999, 65, 1311–1320. [Google Scholar]
  224. Moran, E.F.; Brondizio, E.S.; Tucker, J.M.; da Silva-Forsberg, M.C.; McCracken, S.; Falesi, I. Effects of Soil Fertility and Land-Use on Forest Succession in Amazônia. For. Ecol. Manag. 2000, 139, 93–108. [Google Scholar] [CrossRef]
  225. Brando, P.M.; Balch, J.K.; Nepstad, D.C.; Morton, D.C.; Putz, F.E.; Coe, M.T.; Silvério, D.; Macedo, M.N.; Davidson, E.A.; Nóbrega, C.C.; et al. Abrupt Increases in Amazonian Tree Mortality Due to Drought-Fire Interactions. Proc. Natl. Acad. Sci. USA 2014, 111, 6347–6352. [Google Scholar] [CrossRef] [Green Version]
  226. Davidson, E.A.; De Araüjo, A.C.; Artaxo, P.; Balch, J.K.; Brown, I.F.; Mercedes, M.M.; Coe, M.T.; Defries, R.S.; Keller, M.; Longo, M.; et al. The Amazon Basin in Transition. Nature 2012, 481, 321–328. [Google Scholar] [CrossRef]
  227. Kokol, P.; Završnik, J.; Železnik, D.; Vošner, H.B. Creating a Self-Plagiarism Research Topic Typology through Bibliometric Visualisation. J. Acad. Ethics 2016, 14, 221–230. [Google Scholar] [CrossRef]
  228. Costa, C.; Schurr, U.; Loreto, F.; Menesatti, P.; Carpentier, S. Plant Phenotyping Research Trends, a Science Mapping Approach. Front. Plant Sci. 2019, 9, 1–11. [Google Scholar] [CrossRef] [Green Version]
  229. Garrido-Cardenas, J.A.; Esteban-García, B.; Agüera, A.; Sánchez-Pérez, J.A.; Manzano-Agugliaro, F. Wastewater Treatment by Advanced Oxidation Process and Their Worldwide Research Trends. Int. J. Environ. Res. Public Health 2020, 17, 170. [Google Scholar] [CrossRef] [Green Version]
  230. Perea-Moreno, M.A.; Samerón-Manzano, E.; Perea-Moreno, A.J. Biomass as Renewable Energy: Worldwide Research Trends. Sustainability 2019, 11, 863. [Google Scholar] [CrossRef] [Green Version]
  231. Berry, N.J.; Phillips, O.L.; Lewis, S.L.; Hill, J.K.; Edwards, D.P.; Tawatao, N.B.; Ahmad, N.; Magintan, D.; Khen, C.V.; Maryati, M.; et al. The High Value of Logged Tropical Forests: Lessons from Northern Borneo. Biodivers. Conserv. 2010, 19, 985–997. [Google Scholar] [CrossRef]
  232. Echeverría, C.; Newton, A.C.; Lara, A.; Benayas, J.M.R.; Coomes, D.A. Impacts of Forest Fragmentation on Species Composition and Forest Structure in the Temperate Landscape of Southern Chile. Glob. Ecol. Biogeogr. 2007, 16, 426–439. [Google Scholar] [CrossRef] [Green Version]
  233. Hu, H.; Liu, W.; Cao, M. Impact of Land Use and Land Cover Changes on Ecosystem Services in Menglun, Xishuangbanna, Southwest China. Environ. Monit. Assess. 2008, 146, 147–156. [Google Scholar] [CrossRef] [PubMed]
  234. Kessler, M.; Abrahamczyk, S.; Bos, M.; Buchori, D.; Putra, D.D.; Gradstein, S.R.; Höhn, P.; Kluge, J.; Orend, F.; Pitopang, R.; et al. Alpha and Beta Diversity of Plants and Animals along a Tropical Land-Use Gradient. Ecol. Appl. 2009, 19, 2142–2156. [Google Scholar] [CrossRef]
  235. Norris, K.; Asase, A.; Collen, B.; Gockowksi, J.; Mason, J.; Phalan, B.; Wade, A. Biodiversity in a Forest-Agriculture Mosaic—The Changing Face of West African Rainforests. Biol. Conserv. 2010, 143, 2341–2350. [Google Scholar] [CrossRef]
  236. Gardner, T.A.; Barlow, J.; Sodhi, N.S.; Peres, C.A. A Multi-Region Assessment of Tropical Forest Biodiversity in a Human-Modified World. Biol. Conserv. 2010, 143, 2293–2300. [Google Scholar] [CrossRef]
  237. Laurance, W.F.; Albernaz, A.K.M.; Schroth, G.; Fearnside, P.M.; Bergen, S.; Venticinque, E.M.; Da Costa, C. Predictors of Deforestation in the Brazilian Amazon. J. Biogeogr. 2002, 29, 737–748. [Google Scholar] [CrossRef] [Green Version]
  238. Armenteras, D.; Rudas, G.; Rodriguez, N.; Sua, S.; Romero, M. Patterns and Causes of Deforestation in the Colombian Amazon. Ecol. Indic. 2006, 6, 353–368. [Google Scholar] [CrossRef]
  239. Thompson, J.; Brokaw, N.; Zimmerman, J.K.; Waide, R.B.; Everham, E.M.; Lodge, D.J.; Taylor, C.M.; García-Montiel, D.; Fluet, M. Land Use History, Environment, and Tree Composition in a Tropical Forest. Ecol. Appl. 2002, 12, 1344–1363. [Google Scholar] [CrossRef]
  240. Pascarella, J.B.; Aide, T.M.; Serrano, M.I.; Zimmerman, J.K. Land-Use History and Forest Regeneration in the Cayey Mountains, Puerto Rico. Ecosystems 2000, 3, 217–228. [Google Scholar] [CrossRef]
  241. Klanderud, K.; Mbolatiana, H.Z.H.; Vololomboahangy, M.N.; Radimbison, M.A.; Roger, E.; Totland, Ø.; Rajeriarison, C. Recovery of Plant Species Richness and Composition after Slash-and-Burn Agriculture in a Tropical Rainforest in Madagascar. Biodivers. Conserv. 2010, 19, 187–204. [Google Scholar] [CrossRef]
  242. Van Dam, D.; Veldkamp, E.; Van Breemen, N. Soil Organic Carbon Dynamics: Variability with Depth in Forested and Deforested Soils under Pasture in Costa Rica. Biogeochemistry 1997, 39, 343–375. [Google Scholar] [CrossRef]
  243. Kaschuk, G.; Alberton, O.; Hungria, M. Quantifying Effects of Different Agricultural Land Uses on Soil Microbial Biomass and Activity in Brazilian Biomes: Inferences to Improve Soil Quality. Plant Soil 2011, 338, 467–481. [Google Scholar] [CrossRef]
  244. Davidson, E.A.; Verchot, L.V.; Henrique Cattânio, J.; Ackerman, I.L.; Carvalho, J.E.M. Effects of Soil Water Content on Soil Respiration in Forests and Cattle Pastures of Eastern Amazonia. Biogeochemistry 2000, 48, 53–69. [Google Scholar] [CrossRef]
  245. Cairns, M.A.; Olmsted, I.; Granados, J.; Argaez, J. Composition and Aboveground Tree Biomass of a Dry Semi-Evergreen Forest on Mexico’s Yucatan Peninsula. For. Ecol. Manag. 2003, 186, 125–132. [Google Scholar] [CrossRef]
  246. Kalacska, M.; Sanchez-Azofeifa, G.A.; Calvo-Alvarado, J.C.; Quesada, M.; Rivard, B.; Janzen, D.H. Species Composition, Similarity and Diversity in Three Successional Stages of a Seasonally Dry Tropical Forest. For. Ecol. Manag. 2004, 200, 227–247. [Google Scholar] [CrossRef]
  247. Powers, J.S.; Becknell, J.M.; Irving, J.; Pèrez-Aviles, D. Diversity and Structure of Regenerating Tropical Dry Forests in Costa Rica: Geographic Patterns and Environmental Drivers. For. Ecol. Manag. 2009, 258, 959–970. [Google Scholar] [CrossRef]
  248. Lugo, A.E.; Brown, S. Management of Tropical Soils as Sinks or Sources of Atmospheric Carbon. Plant Soil 1993, 149, 27–41. [Google Scholar] [CrossRef]
  249. Quesada, M.; Sanchez-Azofeifa, G.A.; Alvarez-Añorve, M.; Stoner, K.E.; Avila-Cabadilla, L.; Calvo-Alvarado, J.; Castillo, A.; Espírito-Santo, M.M.; Fagundes, M.; Fernandes, G.W.; et al. Succession and Management of Tropical Dry Forests in the Americas: Review and New Perspectives. For. Ecol. Manag. 2009, 258, 1014–1024. [Google Scholar] [CrossRef]
  250. Schroth, G.; D’Angelo, S.A.; Teixeira, W.G.; Haag, D.; Lieberei, R. Conversion of Secondary Forest into Agroforestry and Monoculture Plantations in Amazonia: Consequences for Biomass, Litter and Soil Carbon Stocks after 7 Years. For. Ecol. Manag. 2002, 163, 131–150. [Google Scholar] [CrossRef]
  251. Zinn, Y.L.; Lal, R.; Resck, D.V.S. Changes in Soil Organic Carbon Stocks under Agriculture in Brazil. Soil Tillage Res. 2005, 84, 28–40. [Google Scholar] [CrossRef]
  252. Cerri, C.E.P.; Easter, M.; Paustian, K.; Killian, K.; Coleman, K.; Bernoux, M.; Falloon, P.; Powlson, D.S.; Batjes, N.H.; Milne, E.; et al. Predicted Soil Organic Carbon Stocks and Changes in the Brazilian Amazon between 2000 and 2030. Agric. Ecosyst. Environ. 2007, 122, 58–72. [Google Scholar] [CrossRef]
  253. Baumann, M.; Gasparri, I.; Piquer-Rodríguez, M.; Gavier Pizarro, G.; Griffiths, P.; Hostert, P.; Kuemmerle, T. Carbon Emissions from Agricultural Expansion and Intensification in the Chaco. Glob. Chang. Biol. 2017, 23, 1902–1916. [Google Scholar] [CrossRef] [PubMed]
  254. Galford, G.L.; Melillo, J.M.; Kicklighter, D.W.; Mustard, J.F.; Cronin, T.W.; Cerri, C.E.P.; Cerri, C.C. Historical Carbon Emissions and Uptake from the Agricultural Frontier of the Brazilian Amazon. Ecol. Appl. 2011, 21, 750–763. [Google Scholar] [CrossRef]
  255. Muylaert de Araújo, M.S.; Silva, C.; Campos, C.P. de Land Use Change Sector Contribution to the Carbon Historical Emissions and the Sustainability-Case Study of the Brazilian Legal Amazon. Renew. Sustain. Energy Rev. 2009, 13, 696–702. [Google Scholar] [CrossRef]
  256. Morton, D.C.; Defries, R.S.; Randerson, J.T.; Giglio, L.; Schroeder, W.; van der Werf, G.R. Agricultural Intensification Increases Deforestation Fire Activity in Amazonia. Glob. Chang. Biol. 2008, 14, 2262–2275. [Google Scholar] [CrossRef] [Green Version]
  257. Foody, G.M.; Cutler, M.E.; Mcmorrow, J.; Pelz, D.; Tangki, H.; Boyd, D.S.; Douglas, I. Mapping the Biomass of Bornean Tropical Rain Forest from Remotely Sensed Data Published by: Blackwell Publishing Stable URL: Http://Www.Jstor.Org/Stable/2665383. Glob. Ecol. anf Biogeogr. 2001, 10, 379–387. [Google Scholar] [CrossRef]
  258. Lindell, C.A.; Chomentowski, W.H.; Zook, J.R. Characteristics of Bird Species Using Forest and Agricultural Land Covers in Southern Costa Rica. Biodivers. Conserv. 2004, 13, 2419–2441. [Google Scholar] [CrossRef]
  259. Fajardo, L.; González, V.; Nassar, J.M.; Lacabana, P.; Portillo Q, C.A.; Carrasquel, F.; Rodríguez, J.P. Tropical Dry Forests of Venezuela: Characterization and Current Conservation Status. Biotropica 2005, 37, 531–546. [Google Scholar] [CrossRef]
  260. Duveiller, G.; Defourny, P.; Desclée, B.; Mayaux, P. Deforestation in Central Africa: Estimates at Regional, National and Landscape Levels by Advanced Processing of Systematically-Distributed Landsat Extracts. Remote Sens. Environ. 2008, 112, 1969–1981. [Google Scholar] [CrossRef]
  261. Müller, R.; Müller, D.; Schierhorn, F.; Gerold, G.; Pacheco, P. Proximate Causes of Deforestation in the Bolivian Lowlands: An Analysis of Spatial Dynamics. Reg. Environ. Chang. 2012, 12, 445–459. [Google Scholar] [CrossRef] [Green Version]
  262. Zekeng, J.C.; Sebego, R.; Mphinyane, W.N.; Mpalo, M.; Nayak, D.; Fobane, J.L.; Onana, J.M.; Funwi, F.P.; Mbolo, M.M.A. Land Use and Land Cover Changes in Doume Communal Forest in Eastern Cameroon: Implications for Conservation and Sustainable Management. Model. Earth Syst. Environ. 2019, 5, 1801–1814. [Google Scholar] [CrossRef]
  263. Jakimow, B.; Griffiths, P.; van der Linden, S.; Hostert, P. Mapping Pasture Management in the Brazilian Amazon from Dense Landsat Time Series. Remote Sens. Environ. 2018, 205, 453–468. [Google Scholar] [CrossRef]
  264. Gunatilake, H.M.; Senaratne, D.M.A.H.; Abeygunawardena, P. Role of Non-Timber Forest Products in the Economy of Peripheral Communities of Knuckles National Wilderness Area of Sri Lanka: A Farming Systems Approach. Econ. Bot. 1993, 47, 275–281. [Google Scholar] [CrossRef]
  265. Brandão, D.O.; Barata, L.E.S.; Nobre, I.; Nobre, C.A. The Effects of Amazon Deforestation on Non-Timber Forest Products. Reg. Environ. Chang. 2021, 21, 122. [Google Scholar] [CrossRef]
  266. McClain, M.E.; Cossío, R.E. The Use of Riparian Environments in the Rural Peruvian Amazon. Environ. Conserv. 2003, 30, 242–248. [Google Scholar] [CrossRef]
  267. Ohl, J.; Wezel, A.; Shepard, G.H.; Yu, D.W. Swidden Agriculture in a Protected Area: The Matsigenka Native Communities of Manu National Park, Peru. Environ. Dev. Sustain. 2008, 10, 827–843. [Google Scholar] [CrossRef]
  268. Heredia-R, M.; Torres, B.; Cabrera-Torres, F.; Torres, E.; Díaz-Ambrona, C.G.H.; Pappalardo, S.E. Land Use and Land Cover Changes in the Diversity and Life Zone for Uncontacted Indigenous People: Deforestation Hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon. Forests 2021, 12, 1539. [Google Scholar] [CrossRef]
  269. Harvey, C.A.; González Villalobos, J.A. Agroforestry Systems Conserve Species-Rich but Modified Assemblages of Tropical Birds and Bats. Biodivers. Conserv. 2007, 16, 2257–2292. [Google Scholar] [CrossRef]
  270. Asase, A.; Tetteh, D.A. The Role of Complex Agroforestry Systems in the Conservation of Forest Tree Diversity and Structure in Southeastern Ghana. Agrofor. Syst. 2010, 79, 355–368. [Google Scholar] [CrossRef]
  271. Walker, R.; Kingo, A.; Homma, O. Analysis Land Use and Land Cover Dynamics in the Brazilian Amazon: An Overview. Ecol. Econ. 1996, 18, 67–75. [Google Scholar] [CrossRef]
  272. de Espindola, G.M.; de Aguiar, A.P.D.; Pebesma, E.; Câmara, G.; Fonseca, L. Agricultural Land Use Dynamics in the Brazilian Amazon Based on Remote Sensing and Census Data. Appl. Geogr. 2012, 32, 240–252. [Google Scholar] [CrossRef]
  273. Rudel, T.K.; Defries, R.; Asner, G.P.; Laurance, W.F. Changing Drivers of Deforestation and New Opportunities for Conservation. Conserv. Biol. 2009, 23, 1396–1405. [Google Scholar] [CrossRef] [PubMed]
  274. Struhsaker, T.T.; Struhsaker, P.J.; Siex, K.S. Conserving Africa’s Rain Forests: Problems in Protected Areas and Possible Solutions. Biol. Conserv. 2005, 123, 45–54. [Google Scholar] [CrossRef]
  275. Edwards, D.P.; Magrach, A.; Woodcock, P.; Ji, Y.; Lim, N.T.-L.; Edwards, F.A.; Larsen, T.H.; Hsu, W.W.; Benedick, S.; Khen, C.V.; et al. Selective-Logging and Oil Palm: Multitaxon Impacts, Biodiversity Indicators, and Trade-Offs for Conservation Planning. Ecol. Appl. 2014, 24, 2029–2049. [Google Scholar] [CrossRef]
  276. Edwards, F.A.; Edwards, D.P.; Larsen, T.H.; Hsu, W.W.; Benedick, S.; Chung, A.; Vun Khen, C.; Wilcove, D.S.; Hamer, K.C. Does Logging and Forest Conversion to Oil Palm Agriculture Alter Functional Diversity in a Biodiversity Hotspot? Anim. Conserv. 2014, 17, 163–173. [Google Scholar] [CrossRef] [PubMed]
  277. Montoya, A.V.G.; Vizuete, D.D.C.; Toaza, J.M.M.; Marcu, M.V.; Borz, S.A. Importance and Use of Ecosystem Services Provided by the Amazonian Landscapes in Ecuador-Evaluation and Spatial Scaling of a Representative Area. Bull. Transilv. Univ. Brasov, Ser. II For. Wood Ind. Agric. Food Eng. 2019, 12, 1–26. [Google Scholar] [CrossRef]
  278. Tripathi, B.M.; Edwards, D.P.; Mendes, L.W.; Kim, M.; Dong, K.; Kim, H.; Adams, J.M. The Impact of Tropical Forest Logging and Oil Palm Agriculture on the Soil Microbiome. Mol. Ecol. 2016, 25, 2244–2257. [Google Scholar] [CrossRef]
  279. Bucini, G.; Lambin, E.F. Fire Impacts on Vegetation in Central Africa: A Remote-Sensing-Based Statistical Analysis. Appl. Geogr. 2002, 22, 27–48. [Google Scholar] [CrossRef]
  280. Belenguer-Plomer, M.A.; Tanase, M.A.; Fernandez-Carrillo, A.; Chuvieco, E. Burned Area Detection and Mapping Using Sentinel-1 Backscatter Coefficient and Thermal Anomalies. Remote Sens. Environ. 2019, 233, 111345. [Google Scholar] [CrossRef]
  281. Salazar, A.; Baldi, G.; Hirota, M.; Syktus, J.; McAlpine, C. Land Use and Land Cover Change Impacts on the Regional Climate of Non-Amazonian South America: A Review. Glob. Planet. Change 2015, 128, 103–119. [Google Scholar] [CrossRef]
  282. Swann, A.L.S.; Longo, M.; Knox, R.G.; Lee, E.; Moorcroft, P.R. Future Deforestation in the Amazon and Consequences for South American Climate. Agric. For. Meteorol. 2015, 214–215, 12–24. [Google Scholar] [CrossRef] [Green Version]
  283. Gardner, T.A.; Hernández, M.I.M.; Barlow, J.; Peres, C.A. Understanding the Biodiversity Consequences of Habitat Change: The Value of Secondary and Plantation Forests for Neotropical Dung Beetles. J. Appl. Ecol. 2008, 45, 883–893. [Google Scholar] [CrossRef]
  284. Falloon, P.; Jones, C.D.; Cerri, C.E.; Al-Adamat, R.; Kamoni, P.; Bhattacharyya, T.; Easter, M.; Paustian, K.; Killian, K.; Coleman, K.; et al. Climate Change and Its Impact on Soil and Vegetation Carbon Storage in Kenya, Jordan, India and Brazil. Agric. Ecosyst. Environ. 2007, 122, 114–124. [Google Scholar] [CrossRef]
  285. Potter, C.S.; Davidson, E.A.; Klooster, S.A.; Nepstad, D.C.; De Negreiros, G.H.; Brooks, V. Regional Application of an Ecosystem Production Model for Studies of Biogeochemistry in Brazilian Amazonia. Glob. Chang. Biol. 1998, 4, 315–333. [Google Scholar] [CrossRef]
  286. Abe, C.A.; Lobo, F. de L.; Dibike, Y.B.; Costa, M.P. de F.; Dos Santos, V.; Novo, E.M.L.M. Modelling the Effects of Historical and Future Land Cover Changes on the Hydrology of an Amazonian Basin. Water 2018, 10, 932. [Google Scholar] [CrossRef] [Green Version]
  287. Papeş, M.; Gaubert, P. Modelling Ecological Niches from Low Numbers of Occurrences: Assessment of the Conservation Status of Poorly Known Viverrids (Mammalia, Carnivora) across Two Continents. Divers. Distrib. 2007, 13, 890–902. [Google Scholar] [CrossRef]
  288. Hilker, T.; Lyapustin, A.I.; Tucker, C.J.; Hall, F.G.; Myneni, R.B.; Wang, Y.; Bi, J.; De Moura, Y.M.; Sellers, P.J. Vegetation Dynamics and Rainfall Sensitivity of the Amazon. Proc. Natl. Acad. Sci. USA 2014, 111, 16041–16046. [Google Scholar] [CrossRef] [Green Version]
  289. Saynes, V.; Hidalgo, C.; Etchevers, J.D.; Campo, J.E. Soil C and N Dynamics in Primary and Secondary Seasonally Dry Tropical Forests in Mexico. Appl. Soil Ecol. 2005, 29, 282–289. [Google Scholar] [CrossRef]
  290. Batjes, N.H.; Dijkshoorn, J.A. Carbon and Nitrogen Stocks in the Soils of the Amazon Region. Geoderma 1999, 89, 273–286. [Google Scholar] [CrossRef]
  291. Jaramillo, V.J.; Kauffman, J.B.; Rentería-Rodríguez, L.; Cummings, D.L.; Ellingson, L.J. Biomass, Carbon, and Nitrogen Pools in Mexican Tropical Dry Forest Landscapes. Ecosystems 2003, 6, 609–629. [Google Scholar] [CrossRef]
  292. Cleveland, C.C.; Townsend, A.R.; Schmidt, S.K.; Constance, B.C. Soil Microbial Dynamics and Biogeochemistry in Tropical Forests and Pastures, Southwestern Costa Rica. Ecol. Appl. 2003, 13, 314–326. [Google Scholar] [CrossRef]
  293. Navarrete, A.A.; Cannavan, F.S.; Taketani, R.G.; Tsai, S.M. A Molecular Survey of the Diversity of Microbial Communities in Different Amazonian Agricultural Model Systems. Diversity 2010, 2, 787–809. [Google Scholar] [CrossRef] [Green Version]
  294. Wadt, L.H.O.; Kainer, K.A.; Staudhammer, C.L.; Serrano, R.O.P. Sustainable Forest Use in Brazilian Extractive Reserves: Natural Regeneration of Brazil Nut in Exploited Populations. Biol. Conserv. 2008, 141, 332–346. [Google Scholar] [CrossRef]
  295. Schwartzman, S.; Alencar, A.; Zarin, H.; Souza, A.P.S. Social Movements and Large-Scale Tropical Forest Protection on the Amazon Frontier: Conservation from Chaos. J. Environ. Dev. 2010, 19, 274–299. [Google Scholar] [CrossRef]
  296. Musigmann, B.; Von Der Gracht, H.; Hartmann, E. Blockchain Technology in Logistics and Supply Chain Management—A Bibliometric Literature Review from 2016 to January 2020. IEEE Trans. Eng. Manag. 2020, 67, 988–1007. [Google Scholar] [CrossRef]
  297. Gao, H.; Ding, X.H.; Wu, S. Exploring the Domain of Open Innovation: Bibliometric and Content Analyses. J. Clean. Prod. 2020, 275, 122580. [Google Scholar] [CrossRef]
  298. Kovács, A.; Van Looy, B.; Cassiman, B. Exploring the Scope of Open Innovation: A Bibliometric Review of a Decade of Research. Scientometrics 2015, 104, 951–983. [Google Scholar] [CrossRef]
  299. van Eck, N.J.; Waltman, L. Citation-Based Clustering of Publications Using CitNetExplorer and VOSviewer. Scientometrics 2017, 111, 1053–1070. [Google Scholar] [CrossRef] [Green Version]
  300. Gaviria-Marin, M.; Merigó, J.M.; Baier-Fuentes, H. Knowledge Management: A Global Examination Based on Bibliometric Analysis. Technol. Forecast. Soc. Change 2019, 140, 194–220. [Google Scholar] [CrossRef]
  301. Ding, X.; Yang, Z. Knowledge Mapping of Platform Research: A Visual Analysis Using VOSviewer and CiteSpace. Electron. Commer. Res. 2020. [Google Scholar] [CrossRef]
  302. Liu, Z.; Yin, Y.; Liu, W.; Dunford, M. Visualizing the Intellectual Structure and Evolution of Innovation Systems Research: A Bibliometric Analysis. Scientometrics 2015, 103, 135–158. [Google Scholar] [CrossRef]
  303. Laurance, W.F. A Crisis in the Making: Responses of Amazonian Forests to Land Use and Climate Change. Trends Ecol. Evol. 1998, 13, 411–415. [Google Scholar] [CrossRef]
  304. Ter Steege, H.; Pitman, N.C.A.; Killeen, T.J.; Laurance, W.F.; Peres, C.A.; Guevara, J.E.; Salomão, R.P.; Castilho, C.V.; Amaral, I.L.; De Almeida Matos, F.D.; et al. Estimating the Global Conservation Status of More than 15,000 Amazonian Tree Species. Sci. Adv. 2015, 1, 9–11. [Google Scholar] [CrossRef]
  305. Houghton, R.A.; Lawrence, K.T.; Hackler, J.L.; Brown, S. The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates. Glob. Chang. Biol. 2001, 7, 731–746. [Google Scholar] [CrossRef]
  306. Lugo, A.E.; Sanchez, M.J.; Brown, S. Land Use and Organic Carbon Content of Some Subtropical Soils. Plant Soil 1986, 96, 185–196. [Google Scholar] [CrossRef]
  307. De Camargo, P.B.; Trumbore, S.E.; Martinelli, L.A.; Davidson, E.A.; Nepstad, D.C.; Victoria, R.L. Soil Carbon Dynamics in Regrowing Forest of Eastern Amazonia. Glob. Chang. Biol. 1999, 5, 693–702. [Google Scholar] [CrossRef]
  308. Davidson, E.A.; Markewitz, D.; de, O. Figueiredo, R.; de Camargo, P.B. Nitrogen Fixation Inputs in Pasture and Early Successional Forest in the Brazilian Amazon Region: Evidence From a Claybox Mesocosm Study. J. Geophys. Res. Biogeosciences 2018, 123, 712–721. [Google Scholar] [CrossRef]
  309. Markewitz, D.; Davidson, E.; Moutinho, P.; Nepstad, D. Nutrient Loss and Redistribution after Forest Clearing on a Highly Weathered Soil in Amazonia. Ecol. Appl. 2004, 14, 177–199. [Google Scholar] [CrossRef] [Green Version]
  310. Davidson, E.A.; Martinelli, L.A. Nutrient Limitations to Secondary Forest Regrowth. Amaz. Glob. Chang. 2013, 299–309. [Google Scholar] [CrossRef]
  311. Cardoso, M.; Nobre, C.; Sampaio, G.; Hirota, M.; Valeriano, D.; Câmara, G. Long-Term Potential for Tropical-Forest Degradation Due to Deforestation and Fires in the Brazilian Amazon. Biologia 2009, 64, 433–437. [Google Scholar] [CrossRef]
  312. Nobre, C.A.; Sampaio, G.; Borma, L.S.; Castilla-Rubio, J.C.; Silva, J.S.; Cardoso, M. Land-Use and Climate Change Risks in the Amazon and the Need of a Novel Sustainable Development Paradigm. Proc. Natl. Acad. Sci. USA 2016, 113, 10759–10768. [Google Scholar] [CrossRef] [Green Version]
  313. Hirota, M.; Oyama, M.D.; Nobre, C. Concurrent Climate Impacts of Tropical South America Land-Cover Change. Atmos. Sci. Lett. 2011, 12, 261–267. [Google Scholar] [CrossRef]
  314. Phillips, O.L.; Brienen, R.J.W.; Gloor, E.; Baker, T.R.; Lloyd, J.; Lopez-Gonzalez, G.; Monteagudo-Mendoza, A.; Malhi, Y.; Lewis, S.L.; Vásquez Martinez, R.; et al. Carbon Uptake by Mature Amazon Forests Has Mitigated Amazon Nations’ Carbon Emissions. Carbon Balance Manag. 2017, 12, 1–9. [Google Scholar] [CrossRef] [PubMed]
  315. Aragão, L.E.O.C.; Poulter, B.; Barlow, J.B.; Anderson, L.O.; Malhi, Y.; Saatchi, S.; Phillips, O.L.; Gloor, E. Environmental Change and the Carbon Balance of Amazonian Forests. Biol. Rev. 2014, 89, 913–931. [Google Scholar] [CrossRef] [PubMed]
  316. Hughes, R.F.; Asner, G.P.; Baldwin, J.A.; Mascaro, J.; Bufil, L.K.K.; Knapp, D.E. Estimating Aboveground Carbon Density across Forest Landscapes of Hawaii: Combining FIA Plot-Derived Estimates and Airborne LiDAR. For. Ecol. Manag. 2018, 424, 323–337. [Google Scholar] [CrossRef]
  317. Asner, G.P.; Martin, R.E.; Knapp, D.E.; Tupayachi, R.; Anderson, C.B.; Sinca, F.; Vaughn, N.R.; Llactayo, W. Airborne Laser-Guided Imaging Spectroscopy to Map Forest Trait Diversity and Guide Conservation. Science 2017, 355, 385–389. [Google Scholar] [CrossRef]
  318. Oliveira, P.J.C.; Asner, G.P.; Knapp, D.E.; Almeyda, A.; Galván-Gildemeister, R.; Keene, S.; Raybin, R.F.; Smith, R.C. Land-Use Allocation Protects the Peruvian Amazon. Science 2007, 317, 1233–1236. [Google Scholar] [CrossRef] [Green Version]
  319. Saatchi, S.; Longo, M.; Xu, L.; Yang, Y.; Abe, H.; André, M.; Aukema, J.E.; Carvalhais, N.; Cadillo-Quiroz, H.; Cerbu, G.A.; et al. Detecting Vulnerability of Humid Tropical Forests to Multiple Stressors. One Earth 2021, 4, 988–1003. [Google Scholar] [CrossRef]
  320. Hansen, M.C.; Krylov, A.; Tyukavina, A.; Potapov, P.V.; Turubanova, S.; Zutta, B.; Ifo, S.; Margono, B.; Stolle, F.; Moore, R. Humid Tropical Forest Disturbance Alerts Using Landsat Data. Environ. Res. Lett. 2016, 11, 034008. [Google Scholar] [CrossRef]
  321. Draper, F.C.; Costa, F.R.C.; Arellano, G.; Phillips, O.L.; Duque, A.; Macía, M.J.; ter Steege, H.; Asner, G.P.; Berenguer, E.; Schietti, J.; et al. Amazon Tree Dominance across Forest Strata. Nat. Ecol. Evol. 2021, 5, 757–767. [Google Scholar] [CrossRef]
  322. Bush, M.B.; Silman, M.R.; De Toledo, M.B.; Listopad, C.; Gosling, W.D.; Williams, C.; De Oliveira, P.E.; Krisel, C. Holocene Fire and Occupation in Amazonia: Records from Two Lake Districts. Philos. Trans. R. Soc. B Biol. Sci. 2007, 362, 209–218. [Google Scholar] [CrossRef]
  323. Mcmichael, C.N.H.; Bush, M.B. Spatiotemporal Patterns of Pre-Columbian People in Amazonia. Quat. Res. 2019, 92, 53–69. [Google Scholar] [CrossRef]
  324. Espejo, J.C.; Messinger, M.; Román-Dañobeytia, F.; Ascorra, C.; Fernandez, L.E.; Silman, M. Deforestation and Forest Degradation Due to Gold Mining in the Peruvian Amazon: A 34-Year Perspective. Remote Sens. 2018, 10, 1903. [Google Scholar] [CrossRef]
  325. Velásquez Ramírez, M.G.; Vega Ruiz, C.M.; Gomringer, R.C.; Pillaca, M.; Thomas, E.; Stewart, P.M.; Gamarra Miranda, L.A.; Dañobeytia, F.R.; Guerrero Barrantes, J.A.; Gushiken, M.C.; et al. Mercury in Soils Impacted by Alluvial Gold Mining in the Peruvian Amazon. J. Environ. Manag. 2021, 288, 112364. [Google Scholar] [CrossRef] [PubMed]
  326. Gerwing, J.J. Degradation of Forests through Logging and Fire in the Eastern Brazilian Amazon. For. Ecol. Manag. 2002, 157, 131–141. [Google Scholar] [CrossRef]
  327. Hölscher, D.; Sá, T.D.D.A.; Bastos, T.X.; Denich, M.; Fölster, H. Evaporation from Young Secondary Vegetation in Eastern Amazonia. J. Hydrol. 1997, 193, 293–305. [Google Scholar] [CrossRef]
  328. Liu, D.; Che, S.; Zhu, W. Visualizing the Knowledge Domain of Academic Mobility Research from 2010 to 2020: A Bibliometric Analysis Using CiteSpace. SAGE Open 2022, 12, 1–15. [Google Scholar] [CrossRef]
  329. Carrión-Mero, P.; Montalván-Burbano, N.; Paz-Salas, N.; Morante-Carballo, F. Volcanic Geomorphology: A Review of Worldwide Research. Geosciences 2020, 10, 347. [Google Scholar] [CrossRef]
  330. Yang, L.; Han, L.; Liu, N. A New Approach to Journal Co-Citation Matrix Construction Based on the Number of Co-Cited Articles in Journals. Scientometrics 2019, 120, 507–517. [Google Scholar] [CrossRef]
  331. Mahmoud, M.I.; Campbell, M.J.; Sloan, S.; Alamgir, M.; Laurance, W.F. Land-Cover Change Threatens Tropical Forests and Biodiversity in the Littoral Region, Cameroon. Oryx 2020, 54, 882–891. [Google Scholar] [CrossRef]
  332. Yu, J.; Li, F.; Wang, Y.; Lin, Y.; Peng, Z.; Cheng, K. Spatiotemporal Evolution of Tropical Forest Degradation and Its Impact on Ecological Sensitivity: A Case Study in Jinghong, Xishuangbanna, China. Sci. Total Environ. 2020, 727, 138678. [Google Scholar] [CrossRef] [PubMed]
  333. Baltaxe, R. The Application of Landsat Data to Tropical Forest Surveys, 1st ed.; FAO: Rome, Italy, 1980. [Google Scholar]
  334. Kacic, P.; Hirner, A.; Da Ponte, E. Fusing Sentinel-1 and-2 to Model Gedi-Derived Vegetation Structure Characteristics in Gee for the Paraguayan Chaco. Remote Sens. 2021, 13, 5105. [Google Scholar] [CrossRef]
  335. Padula, R.; Brozoski, F. La Amazonía En El Pensamiento Geopolítico Brasileño. Rev. tempo do mundo 2021, 27, 47–70. [Google Scholar]
  336. Song, Y.; Zhao, T. A Bibliometric Analysis of Global Forest Ecology Research during 2002-2011. Springerplus 2013, 2, 1–9. [Google Scholar] [CrossRef] [PubMed]
  337. Taubert, F.; Fischer, R.; Groeneveld, J.; Lehmann, S.; Müller, M.S.; Rödig, E.; Wiegand, T.; Huth, A. Global Patterns of Tropical Forest Fragmentation. Nature 2018, 554, 519–522. [Google Scholar] [CrossRef]
  338. Hoang, N.T.; Kanemoto, K. Mapping the Deforestation Footprint of Nations Reveals Growing Threat to Tropical Forests. Nat. Ecol. Evol. 2021, 5, 845–853. [Google Scholar] [CrossRef] [PubMed]
  339. Wan Mahari, W.A.; Azwar, E.; Li, Y.; Wang, Y.; Peng, W.; Ma, N.L.; Yang, H.; Rinklebe, J.; Lam, S.S.; Sonne, C. Deforestation of Rainforests Requires Active Use of UN’s Sustainable Development Goals. Sci. Total Environ. 2020, 742, 140681. [Google Scholar] [CrossRef] [PubMed]
  340. Liu, H.; Gong, P.; Wang, J.; Wang, X.; Ning, G.; Xu, B. Production of Global Daily Seamless Data Cubes and Quantification of Global Land Cover Change from 1985 to 2020 - IMap World 1.0. Remote Sens. Environ. 2021, 258, 112364. [Google Scholar] [CrossRef]
  341. Gutman, G.; Huang, C.; Chander, G.; Noojipady, P.; Masek, J.G. Assessment of the NASA-USGS Global Land Survey (GLS) Datasets. Remote Sens. Environ. 2013, 134, 249–265. [Google Scholar] [CrossRef]
  342. Sanchez-Azofeifa, A.; Antonio Guzmán, J.; Campos, C.A.; Castro, S.; Garcia-Millan, V.; Nightingale, J.; Rankine, C. Twenty-First Century Remote Sensing Technologies Are Revolutionizing the Study of Tropical Forests. Biotropica 2017, 49, 604–619. [Google Scholar] [CrossRef]
  343. Velastegui-Montoya, A.; Rivera-Torres, H.; Herrera-Matamoros, V.; Sadeck, L.; Quevedo, R.P. Application of Google Earth Engine for Land Cover Classification in Yasuni National Park, Ecuador. In Proceedings of the IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; IEEE: New York, NY, USA, 2022; pp. 6376–6379. [Google Scholar]
  344. Dupuis, C.; Lejeune, P.; Michez, A.; Fayolle, A. How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review. Remote Sens. 2020, 12, 1087. [Google Scholar] [CrossRef] [Green Version]
  345. Jackson, C.M.; Adam, E. Remote Sensing of Selective Logging in Tropical Forests: Current State and Future Directions. IForest 2020, 13, 286–300. [Google Scholar] [CrossRef]
  346. Seymour, F. Why Are Tropical Forests Being Lost, and How to Protect Them. Available online: https://research.wri.org/gfr/tropical-forests-loss-deforestation-protection (accessed on 21 June 2022).
  347. Cannon, P.G.; Gilroy, J.J.; Tobias, J.A.; Anderson, A.; Haugaasen, T.; Edwards, D.P. Land-Sparing Agriculture Sustains Higher Levels of Avian Functional Diversity than Land Sharing. Glob. Chang. Biol. 2019, 25, 1576–1590. [Google Scholar] [CrossRef]
  348. Song, H.; Singh, D.; Tomlinson, K.W.; Yang, X.; Ogwu, M.C.; Slik, J.W.F.; Adams, J.M. Tropical Forest Conversion to Rubber Plantation in Southwest China Results in Lower Fungal Beta Diversity and Reduced Network Complexity. FEMS Microbiol. Ecol. 2019, 95, 1–13. [Google Scholar] [CrossRef]
  349. da Silva Cruz, J.; Blanco, C.J.C.; de Oliveira Júnior, J.F. Modeling of Land Use and Land Cover Change Dynamics for Future Projection of the Amazon Number Curve. Sci. Total Environ. 2022, 811, 152348. [Google Scholar] [CrossRef]
  350. Yatoo, S.A.; Sahu, P.; Kalubarme, M.H.; Kansara, B.B. Monitoring Land Use Changes and Its Future Prospects Using Cellular Automata Simulation and Artificial Neural Network for Ahmedabad City, India. GeoJournal 2022, 87, 765–786. [Google Scholar] [CrossRef]
  351. Boulton, C.A.; Lenton, T.M.; Boers, N. Pronounced Loss of Amazon Rainforest Resilience since the Early 2000s. Nat. Clim. Chang. 2022, 12, 271–278. [Google Scholar] [CrossRef]
  352. Kleemann, J.; Koo, H.; Hensen, I.; Mendieta-Leiva, G.; Kahnt, B.; Kurze, C.; Inclan, D.J.; Cuenca, P.; Noh, J.K.; Hoffmann, M.H.; et al. Priorities of Action and Research for the Protection of Biodiversity and Ecosystem Services in Continental Ecuador. Biol. Conserv. 2022, 265, 109404. [Google Scholar] [CrossRef]
Figure 1. Diagram inspired by the PRISMA statement detailing the four phases of the bibliometric research methodology.
Figure 1. Diagram inspired by the PRISMA statement detailing the four phases of the bibliometric research methodology.
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Figure 2. Scientific production in research (1978–2021) on LULC in tropical forests divided into four periods. The data are the annual number of papers (articles and reviews) indexed in Scopus and the number of citations per year.
Figure 2. Scientific production in research (1978–2021) on LULC in tropical forests divided into four periods. The data are the annual number of papers (articles and reviews) indexed in Scopus and the number of citations per year.
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Figure 3. World map showing number of studies about LULC in tropical forest per country.
Figure 3. World map showing number of studies about LULC in tropical forest per country.
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Figure 4. Visualization of themes of research (clusters of the same colour) based on the analysis of keyword co-occurrence.
Figure 4. Visualization of themes of research (clusters of the same colour) based on the analysis of keyword co-occurrence.
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Figure 5. Network and grouping of co-cited authors. Clusters of the same color represent authors with collective knowledge.
Figure 5. Network and grouping of co-cited authors. Clusters of the same color represent authors with collective knowledge.
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Figure 6. Network and grouping of co-cited journals. Clusters of the same color represent areas of knowledge.
Figure 6. Network and grouping of co-cited journals. Clusters of the same color represent areas of knowledge.
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Table 1. Top 10 frequently cited documents during 1978–2021 on LULC in tropical forests. R = rank; TC = total number of citations received for document; ACI = annual citation index.
Table 1. Top 10 frequently cited documents during 1978–2021 on LULC in tropical forests. R = rank; TC = total number of citations received for document; ACI = annual citation index.
RAuthorsTitleYearTCACI
1Hansen et al. [175]High-resolution global maps of 21st century forest cover change20135372596.9
2Pan et al. [170]A large and persistent carbon sink in the world’s forests20113788344.4
3Gibson et al. [174]Primary forests are irreplaceable for sustaining tropical biodiversity20111187107.9
4Gibbs et al. [185]Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s2010103486.16
5Nepstad et al. [149]The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures1994100635.9
6Baccini et al. [172]Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps201299299.2
7Nepstad et al. [156]Large-scale impoverishment of Amazonian forests by logging and fire199996241.8
8Klink and Machado [157]Conservation of the Brazilian Cerrado200596156.5
9Brown and Lugo [147]Tropical secondary forests199086327
10Houghton [186]Aboveground forest biomass and the global carbon balance200580847.5
Table 2. Top 10 sources with the highest number of documents. R = rank; CY = coverage; ND = number of documents; TC = total number of citations received for document; SJR = Scimago Journal Rank.
Table 2. Top 10 sources with the highest number of documents. R = rank; CY = coverage; ND = number of documents; TC = total number of citations received for document; SJR = Scimago Journal Rank.
RSourceScopus CYNDTCSJRCiteScore
1Forest Ecology and Management1976 to present17911,0681.295.8
2PLOS One2001 to present10235310.995.3
3Global Change Biology1995 to present9391154.1515.5
4Land Use Policy1984 to present8932851.677.5
5Remote Sensing of Environment1969 to present8283563.6117.6
6International Journal of Remote Sensing1980 to present8137420.925.9
7Remote Sensing1992 to present7514311.296.6
8Biotropica1979 to present7525020.813.6
9Environmental Research Letters2006 to present7425522.378.6
10Ecological Applications1991 to present7052781.867.8
Total top 10 journals 92050,860
Total documents 455720,608
Table 3. Top 10 leading authors by their number of publications and number of citations. ND = number of documents; TC = total number of citations received for document; HI = H-index.
Table 3. Top 10 leading authors by their number of publications and number of citations. ND = number of documents; TC = total number of citations received for document; HI = H-index.
AuthorCountryAffiliationsIntellectual StructureGlobal
Publication
HI
NDTCNDTC
Shimabukuro Y.E.BrazilInstituto Nacional de Pesquisas Espaciais502820251779741
Barlow J.United KingdomLancaster Environment Centre46538621013,69160
Asner G.P.United StatesArizona State University44497058254,423112
Fearnside P.M.BrazilInstituto Nacional de Pesquisas da Amazonia42318622616,54367
Peres C.A.United KingdomUniversity of East Anglia41446837425,10386
Morán E.F.United StatesIndiana University39276216614,73250
Aragão L.E.BrazilInstituto Nacional de Pesquisas Espaciais36152922413,44454
Perz S.G.United StatesUniversity of Florida36155499310130
Davidson E.A.United StatesUniversity of Maryland Center for Environmental Science35573024136,00793
Walker R.United StatesUniversity of Florida35246492402535
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Velastegui-Montoya, A.; Montalván-Burbano, N.; Peña-Villacreses, G.; de Lima, A.; Herrera-Franco, G. Land Use and Land Cover in Tropical Forest: Global Research. Forests 2022, 13, 1709. https://doi.org/10.3390/f13101709

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Velastegui-Montoya A, Montalván-Burbano N, Peña-Villacreses G, de Lima A, Herrera-Franco G. Land Use and Land Cover in Tropical Forest: Global Research. Forests. 2022; 13(10):1709. https://doi.org/10.3390/f13101709

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Velastegui-Montoya, Andrés, Néstor Montalván-Burbano, Gina Peña-Villacreses, Aline de Lima, and Gricelda Herrera-Franco. 2022. "Land Use and Land Cover in Tropical Forest: Global Research" Forests 13, no. 10: 1709. https://doi.org/10.3390/f13101709

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