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Remote Sensing of Ecosystems

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 104852

Special Issue Editors


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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing methodologies and applications in agriculture, water resources, and ecosystems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: plant biodiversity estimation by LiDAR and hyperspectral data; vegetation structural and ecological variable retrieval and modeling
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: forest aboveground biomass and dynamic changes monitoring based on LiDAR and time series remote sensing data

Special Issue Information

Dear Colleagues,

The remote sensing of ecosystems mainly focuses on the identification of types of ecosystems and their patterns, the monitoring of ecosystem functions, ecosystem services assessment, and the analysis of ecosystem processes by remote-sensing-based methods. The new generation of satellites and sensors provide additional earth observation data sources for ecosystem monitoring. However, the ability to identify ecosystem types still needs to be improved, and efforts need to be placed on intelligent information extraction (in the Big Data era). For ecosystem function monitoring, it is necessary to fully exploit the hidden features of remote sensing data and develop new indicators that are easy to process and reflect the functional characteristics of ecosystems. In addition, advanced models are needed to better assess ecosystem services by analyzing the implicit processes and performance of ecosystems. Combination with cloud platforms is a future trend in remote sensing for ecosystem analysis. This will provide opportunities for public participation in ecological protection, and will provide more data support for the assessment of the ecological effects of key national projects.

This Special Issue aims to publish original research that specifically addresses innovative techniques and methodologies for modelling, mapping, and detecting ecosystem status or evaluating ecosystem services and functions from local to global scales. We invite a wide range of contributions with multidisciplinary research about the following topics (not an exhaustive list):

  • Land cover/land change detection;
  • Ecological parameters and ecosystem functions;
  • Ecosystem service assessment;
  • Assessment of the ecological effects of key national projects;
  • Ecosystem observation instruments and platforms;
  • Ecosystem ground observation networks;
  • Big data of ecosystems;
  • Ecological cloud;
  • Remote sensing of biodiversity;
  • Remote sensing of forest ecosystems;
  • Remote sensing of grassland ecosystems;
  • Remote sensing of agricultural ecosystems;
  • Remote sensing of wetland ecosystems;
  • Remote sensing of urban ecosystems;
  • Remote sensing of desert ecosystems;
  • Remote sensing of marine ecosystems.

The contributors of this Special Issue are mainly (but not exclusively) from the 1st Academic Symposium on Remote Sensing of Ecosystems, 25–28 November 2021 (it has been postponed due to the COVID-19 pandemic, the specific time will be notified later), Shenzhen, China. Website: http://www.ecowatch2021.com/

Prof. Dr. Bingfang Wu
Prof. Dr. Yuan Zeng
Dr. Dan Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (33 papers)

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21 pages, 58343 KiB  
Article
Spatiotemporal Heterogeneity of Coastal Wetland Ecosystem Services in the Yellow River Delta and Their Response to Multiple Drivers
by Liting Yin, Wei Zheng, Honghua Shi, Yongzhi Wang and Dewen Ding
Remote Sens. 2023, 15(7), 1866; https://doi.org/10.3390/rs15071866 - 31 Mar 2023
Cited by 3 | Viewed by 1896
Abstract
Understanding on the spatiotemporal interactions between ecosystem services (ESs) and social–ecological drivers is crucial for the design of sustainable development strategies for coastal wetlands. In this paper, we took the Yellow River Delta (YRD) as a case study, based on multiple evaluation methods [...] Read more.
Understanding on the spatiotemporal interactions between ecosystem services (ESs) and social–ecological drivers is crucial for the design of sustainable development strategies for coastal wetlands. In this paper, we took the Yellow River Delta (YRD) as a case study, based on multiple evaluation methods to study the spatiotemporal dynamics of ESs in the YRD from 1980 to 2020. With the help of principal component analysis (PCA) for identification of multiple drivers, we researched the spatiotemporal differentiation and influence mechanism of drivers on ESs, using the coupling coordination degree (CCD) model and geographically and temporally weighted regression (GTWR) model, and subsequently provided the development strategy for each district in Dongying City. The results showed that (1) the patterns of ESs were spatially heterogeneous, with a fluctuating upward trend from 1980 to 2020, which was mainly affected by regulating service. (2) Our spatiotemporal analysis of ES interactions identified that cultural service was mainly disorder with other ESs. Nevertheless, in wetlands, various ESs can basically develop in a coordinated manner. (3) We integrated multiple drivers into five principal components by PCA, to which the response of ESs had spatial heterogeneity. (4) Consequently, we integrated spatiotemporal knowledge on ES interactions and their drivers into spatial planning. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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23 pages, 6296 KiB  
Article
Fitness for Purpose of Several Fractional Vegetation Cover Products on Monitoring Vegetation Cover Dynamic Change—A Case Study of an Alpine Grassland Ecosystem
by Renjie Huang, Jianjun Chen, Zihao Feng, Yanping Yang, Haotian You and Xiaowen Han
Remote Sens. 2023, 15(5), 1312; https://doi.org/10.3390/rs15051312 - 27 Feb 2023
Cited by 5 | Viewed by 1524
Abstract
Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of [...] Read more.
Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of high-precision FVC ground-measured data, the accuracy of these FVC products in some regions (such as the Qinghai–Tibet Plateau) is still unknown, which brings a certain impact on eco-environment monitoring and simulation. Here, based on current international mainstream FVC products (including GEOV1 and GEOV2 at Copernicus Global Land Services, GLASS from Beijing Normal University, and MuSyQ from National Earth System Science Data Center), the study of the dynamic change of vegetation cover and its influence factors were conducted in the three-rivers source region, one of the core regions on the Qinghai–Tibet Plateau, via the methods of trend analysis and partial correlation analysis, respectively. Our results found that: (1) The discrepancy in the eco-environment assessment results caused by the inconsistency of FVC products is reflected in the statistical value and the spatial distribution. (2) About 70% of alpine grassland in the three-rivers source region changing trend is controversial. (3) The limiting or driving factors of the alpine grassland change explained via different FVC products were significantly discrepant. Thus, before conducting these studies in the future, the uncertainties of the FVC products utilized should be validated first to acquire the fitness of the FVC products if the accuracy information of these products is unavailable within the study area. In addition, more high-precision FVC ground-measured data should be collected, helping us to validate FVC product uncertainty. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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23 pages, 17192 KiB  
Article
Fuzzy Assessment of Ecological Security on the Qinghai–Tibet Plateau Based on Pressure–State–Response Framework
by Tong Lu, Changjia Li, Wenxin Zhou and Yanxu Liu
Remote Sens. 2023, 15(5), 1293; https://doi.org/10.3390/rs15051293 - 26 Feb 2023
Cited by 12 | Viewed by 2042
Abstract
Climate change and human activities have caused a wide range of ecological risks in the Qinghai–Tibet Plateau (QTP) over the past two decades, such as land degradation and biodiversity loss. Therefore, it is imperative to assess the ecological security and drivers for its [...] Read more.
Climate change and human activities have caused a wide range of ecological risks in the Qinghai–Tibet Plateau (QTP) over the past two decades, such as land degradation and biodiversity loss. Therefore, it is imperative to assess the ecological security and drivers for its sustainable development. However, there still lacks a spatial understanding of ecological security in the QTP, as well as the geographic driving forces. In this study, a pressure–state–response (PSR) framework and the coupled fuzzy and obstacle degree models were used to evaluate the spatial pattern and factors that modulate ecological security of the QTP. The southeast of the plateau exhibited high pressure and state levels, indicating that population and economic development activities were concentrated in these regions owing to the good natural conditions. The ecological security evaluation value of the QTP is moderately low, with a median value of 47.4 (the full mark is 100). Seven regions with low ecological security were identified where targeted planning and governance measures should be implemented based on the local natural and economic conditions. Population density, net primary productivity index (NPP) of vegetation, and GDP per unit area were the main factors that modulated ecological security in the QTP, with obstacles accounting for 17.52%, 13.20%, and 12.97%, respectively. These results improve our understanding of the major vulnerable areas and main driving forces of ecological security, providing key information for optimization of ecological security pattern in the QTP. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 8843 KiB  
Article
Discrimination among Climate, Human Activities, and Ecosystem Functional-Induced Land Degradation in Southern Africa
by Zidong Li, Changjia Li, Dexin Gao and Shuai Wang
Remote Sens. 2023, 15(2), 403; https://doi.org/10.3390/rs15020403 - 9 Jan 2023
Cited by 2 | Viewed by 2344
Abstract
Land degradation threatens ecosystems and socio-economic development of Southern Africa. Evaluation of land degradation is widely conducted using a remote-sensed indicator to provide key information for alleviating degradation. However, the commonly used single indicator cannot reveal complex degradation processes. In this study, we [...] Read more.
Land degradation threatens ecosystems and socio-economic development of Southern Africa. Evaluation of land degradation is widely conducted using a remote-sensed indicator to provide key information for alleviating degradation. However, the commonly used single indicator cannot reveal complex degradation processes. In this study, we conducted an integrated evaluation by utilizing linear regression, residual trend analysis, and sequential regression methods to detect visible, potential human-induced, and functional land degradation in Southern Africa. The results showed that visible, potential, and functional land degradation accounted for 8%, 9.6%, and 21.9% of the entire study area, respectively. In total, 34% (171.96 × 104 km2) of the region exhibited one or more forms of land degradation; 28.9% (146.01 × 104 km2) of the land experienced a single land degradation type, whereas 5.1% (25.95 × 104 km2) exhibited intensified degradation by two or three forms. Land degradation was more severe in South Africa, Angola, Botswana, and Mozambique. Potential degradation (11.76%) and functional degradation (56.88%) may co-exist with vegetation greening. This study suggests that a single indicator assessment underestimates the overall land degradation, and thus integrated indicators and methods are better for a comprehensive assessment. Spatial pattern and degradation process analyses are useful for the formulation of land restoration policies in Southern Africa. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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23 pages, 8316 KiB  
Article
Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data
by Hantian Wu, Bo Huang, Zhaoju Zheng, Zonghan Ma and Yuan Zeng
Remote Sens. 2022, 14(23), 6166; https://doi.org/10.3390/rs14236166 - 5 Dec 2022
Cited by 3 | Viewed by 3198
Abstract
Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are [...] Read more.
Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are strongly related to the city’s heterogeneity and have significant spatial-temporal characteristics but fail to address the albedo of the urban surface as a unique variable in urban thermal environment research. This study selects Beijing as the experimental area for exploring the spatial-temporal characteristics of the urban surface albedo and the albedo’s uniqueness in environmental research on urban spaces. Our results show that the urban surface albedo at high spatial resolution can better represent the urban spatial heterogeneity, seasonal variation, building canyon, and pixel adjacency effects. Urban surface albedo is associated with building density and height, land surface temperature (LST), and fractional vegetation cover (FVC). Furthermore, albedo can reflect livability and environmental rating due to the variances of building materials and architectural formats in the urban development. Hence, we argue that the albedo of the urban surface can be considered as a unique variable for improving the acknowledgment of the urban environment and human livability with wider application in urban environmental research. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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21 pages, 4818 KiB  
Article
Multi-Scale Validation and Uncertainty Analysis of GEOV3 and MuSyQ FVC Products: A Case Study of an Alpine Grassland Ecosystem
by Jianjun Chen, Renjie Huang, Yanping Yang, Zihao Feng, Haotian You, Xiaowen Han, Shuhua Yi, Yu Qin, Zhiwei Wang and Guoqing Zhou
Remote Sens. 2022, 14(22), 5800; https://doi.org/10.3390/rs14225800 - 17 Nov 2022
Cited by 4 | Viewed by 3305
Abstract
Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC [...] Read more.
Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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20 pages, 10828 KiB  
Article
Using the Geodetector Method to Characterize the Spatiotemporal Dynamics of Vegetation and Its Interaction with Environmental Factors in the Qinba Mountains, China
by Shuhui Zhang, Yuke Zhou, Yong Yu, Feng Li, Ruixin Zhang and Wenlong Li
Remote Sens. 2022, 14(22), 5794; https://doi.org/10.3390/rs14225794 - 16 Nov 2022
Cited by 15 | Viewed by 2478
Abstract
Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north–south transition zone in China, is an environmentally fragile area that is vulnerable [...] Read more.
Understanding the driving mechanisms of vegetation development is critical for maintaining terrestrial ecosystem function in mountain areas, especially under the background of climate change. The Qinba Mountains (QBM), a critical north–south transition zone in China, is an environmentally fragile area that is vulnerable to climate change. It is essential to characterize how its ecological environment has changed. Currently, such a characterization remains unclear in the spatiotemporal patterns of the nonlinear effects and interactions between environmental factors and vegetation changes in the QBM. Here, we utilized the Normalized Difference Vegetation Index (NDVI), obtained from Google Earth Engine (GEE) platform, as an indicator of terrestrial ecosystem conditions. Then, we measured the spatiotemporal heterogeneity for vegetation variation in the QBM from 2003 to 2018. Specifically, the Geodetector method, a new geographically statistical method without linear assumptions, was employed to detect the interaction between vegetation and environmental driving factors. The results indicated that there is a trend of a general increase in vegetation growth amplitude (the average NDVI increased from 0.810 to 0.858). The areas with an NDVI greater than 0.8 are mainly distributed in the Qinling Mountains and the Daba Mountains, which account for more than 76.39% of the QBM area. For the entire region, the global Moran’s index of the NDVI is greater than 0.95, indicating that vegetation is highly concentrated in the spatial domain. The Geodetector identified that landform type was the primary factor in controlling vegetation changes, contributing 24.19% to the total variation, while the explanatory powers of the aridity index and the wetness index for vegetation changes were 22.49% and 21.47%, respectively. Furthermore, the interaction effects between any two factors outperformed the influence of a single environmental variable. The interaction between air temperature and the aridity index was the most significant element, contributing to 47.10% of the vegetation variation. These findings can not only improve our understanding in the interactive effects of environmental forces on vegetation change, but also be a valuable reference for ecosystem management in the QBM area, such as ecological conservation planning and the assessment of ecosystem functions. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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23 pages, 5979 KiB  
Article
Characterizing the Turning Points in Ecosystem Functioning and Their Linkages to Drought and Human Activities over the Arid and Semi-Arid Regions of Northern China
by Xiuchao Hong, Fang Huang, Hongwei Zhang and Ping Wang
Remote Sens. 2022, 14(21), 5396; https://doi.org/10.3390/rs14215396 - 27 Oct 2022
Cited by 1 | Viewed by 1624
Abstract
Identifying the changes in dryland functioning and the drivers of those changes are critical for global ecosystem conservation and sustainability. The arid and semi-arid regions of northern China (ASARNC) are located in a key area of the generally temperate desert of the Eurasian [...] Read more.
Identifying the changes in dryland functioning and the drivers of those changes are critical for global ecosystem conservation and sustainability. The arid and semi-arid regions of northern China (ASARNC) are located in a key area of the generally temperate desert of the Eurasian continent, where the ecological conditions have experienced noticeable changes in recent decades. However, it is unclear whether the ecosystem functioning (EF) in this region changed abruptly and how that change was affected by natural and anthropogenic factors. Here, we estimated monthly rain use efficiency (RUE) from MODIS NDVI time series data and investigated the timing and types of turning points (TPs) in EF by the Breaks For Additive Season and Trend (BFAST) family algorithms during 2000–2019. The linkages between the TPs, drought, the frequency of land cover change, and socioeconomic development were examined. The results show that 63.2% of the pixels in the ASARNC region underwent sudden EF changes, of which 26.64% were induced by drought events, while 55.67% were firmly associated with the wetting climate. Wet and dry events were not detected in 17.69% of the TPs, which might have been caused by human activities. TP types and occurrences correlate differently with land cover change frequency, population density, and GDP. The improved EF TP type was correlated with the continuous humid climate and a reduced population density, while the deteriorated EF type coincided with persistent drought and increasing population density. Our research furthers the understanding of how and why TPs of EF occur and provides fundamental data for the conservation, management, and better decision-making concerning dryland ecosystems in China. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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19 pages, 4044 KiB  
Article
Spatiotemporal Patterns and Driving Factors of Ecological Vulnerability on the Qinghai-Tibet Plateau Based on the Google Earth Engine
by Zhengyuan Zhao, Ting Li, Yunlong Zhang, Da Lü, Cong Wang, Yihe Lü and Xing Wu
Remote Sens. 2022, 14(20), 5279; https://doi.org/10.3390/rs14205279 - 21 Oct 2022
Cited by 11 | Viewed by 2198
Abstract
With the background of climate change and intensified human activities, environmental problems are becoming increasingly prominent on the Qinghai-Tibet Plateau (QTP). For the development of efficient environmental policies and protection measures, quick and accurate assessments of the spatiotemporal patterns in ecological vulnerability are [...] Read more.
With the background of climate change and intensified human activities, environmental problems are becoming increasingly prominent on the Qinghai-Tibet Plateau (QTP). For the development of efficient environmental policies and protection measures, quick and accurate assessments of the spatiotemporal patterns in ecological vulnerability are crucial. Based on the Google Earth Engine (GEE) platform, we used Moderate Resolution Imaging Spectroradiometer (MODIS), Shuttle Radar Topography Mission (SRTM), and human footprint (HFP) datasets to analyze the spatiotemporal distributions and main driving factors of the remote sensing ecological vulnerability index (RSEVI) for the QTP. Moreover, spatial autocorrelation analysis and the standard deviational ellipse (SDE) were used to analyze the spatiotemporal characteristics. Our results showed that the RSEVI gradually increased from the southeast to the northwest of the QTP. From 2000 to 2018, the potential vulnerability area increased by 6.59 × 104 km2, while the extreme vulnerability area decreased by 1.84 × 104 km2. Moran’s I value of the RSEVI was greater than 0 and increased, indicating that the aggregation degree was increasing. The gravity center was located in Nagqu, Tibet, and shifted to the northwest from 2000 to 2015 and to the southeast from 2015 to 2018. The SDE rotated in a counterclockwise direction. The three most important driving factors of ecological vulnerability were wetness, land surface temperature (LST), and the normalized difference vegetation index (NDVI), indicating that climate and vegetation were the dominant factors. Moreover, this study developed a promising method for the ecological vulnerability assessment of large-scale and long time series datasets, and it provides theoretical support for the ecological conservation and sustainable development of the QTP under global change. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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22 pages, 4981 KiB  
Article
Effects of Human Disturbance on Riparian Wetland Landscape Pattern in a Coastal Region
by Shiguang Shen, Jie Pu, Cong Xu, Yuhua Wang, Wan Luo and Bo Wen
Remote Sens. 2022, 14(20), 5160; https://doi.org/10.3390/rs14205160 - 15 Oct 2022
Cited by 5 | Viewed by 2000
Abstract
The wetland ecosystem along a river in a coastal region has great significance in purifying water bodies, regulating climate, and providing habitat for animals and plants. Studying the effects of human disturbances on the landscape patterns of wetlands is of great significance to [...] Read more.
The wetland ecosystem along a river in a coastal region has great significance in purifying water bodies, regulating climate, and providing habitat for animals and plants. Studying the effects of human disturbances on the landscape patterns of wetlands is of great significance to the protection and management of an ecosystem. This study used Guannan County and Guanyun County, two counties in China that are located on both banks of the Xinyi River as the study area. The spatiotemporal characteristics of the landscape pattern evolution of wetlands and their relationship with human interference from 2009 to 2020 were analyzed by the landscape dynamic rate, landscape conversion matrix, landscape indices, human disturbance index, and the quadratic regression equation. The results showed that: (1) Except for the increase in the area of beach and paddy fields, the area of other landscape types decreased; (2) the changes in wetlands were heterogeneous and showed different trends in different regions; (3) the boundary shape’s complexity and the landscape pattern’s fragmentation showed a decreasing–increasing trend and the connectivity and the diversity of the landscape decreased; and (4) the human disturbance index increased from 2009 to 2014 and then decreased from 2014 to 2020, declining outward from the places where towns and construction land aggregated. Moreover, there was an inverted U-type relationship with the landscape pattern indices. The findings provide direct, specific, and explicit information and theoretical guidance for the protection of wetlands along the river in the coastal region as well as for the restoration of wetland ecosystem function and the improvement of wetland biodiversity in relevant regions. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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21 pages, 5171 KiB  
Article
A Perspective on the Impact of Grassland Degradation on Ecosystem Services for the Purpose of Sustainable Management
by Xin Lyu, Xiaobing Li, Dongliang Dang, Huashun Dou, Kai Wang, Jirui Gong, Hong Wang and Shiliang Liu
Remote Sens. 2022, 14(20), 5120; https://doi.org/10.3390/rs14205120 - 13 Oct 2022
Cited by 8 | Viewed by 2486
Abstract
Grassland degradation seriously threatens the ability of grassland to provide ecosystem services. Grassland ecological restoration and sustainable management decision making depends on an accurate understanding of the impacts of grassland degradation on ecosystem services. Based on the assessment of grassland degradation and four [...] Read more.
Grassland degradation seriously threatens the ability of grassland to provide ecosystem services. Grassland ecological restoration and sustainable management decision making depends on an accurate understanding of the impacts of grassland degradation on ecosystem services. Based on the assessment of grassland degradation and four key ecosystem services, including the net primary production (NPP), ecosystem carbon pool (EC), soil conservation (SC), and soil loss by wind (SL), the impacts of grassland degradation on ecosystem services and their relationships were analyzed. The impacts of climate change and grazing pressure on the relationship between grassland degradation and ecosystem services were revealed. Based on the “climate change and grazing pressure-grassland degradation-ecosystem services” network, the study puts forward specific suggestions on grassland ecological restoration and sustainable management under the premise of fully balancing ecological restoration and stakeholder relationships. The results showed that grassland degradation had a significant impact on ecosystem services and their relationships, but it varied with the types of ecosystem services. Although the degraded grassland in the study area has been in a state of recovery and ecosystem services have been improving in the past 20 years, the degradation of grassland in some areas has intensified, and there are still ecological risks, so it is necessary to continue to carry out ecological restoration work. On this basis, taking the local conditions into consideration, grassland ecological restoration and sustainable management policy suggestions were proposed. The study can provide a scientific reference for ecological protection and sustainable development in arid and semi-arid areas, and help to improve human well-being. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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20 pages, 7881 KiB  
Article
Dynamic Monitoring of Environmental Quality in the Loess Plateau from 2000 to 2020 Using the Google Earth Engine Platform and the Remote Sensing Ecological Index
by Jing Zhang, Guijun Yang, Liping Yang, Zhenhong Li, Meiling Gao, Chen Yu, Enjun Gong, Huiling Long and Haitang Hu
Remote Sens. 2022, 14(20), 5094; https://doi.org/10.3390/rs14205094 - 12 Oct 2022
Cited by 16 | Viewed by 4596
Abstract
The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To [...] Read more.
The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 year1. The overall environment quality was moderate (0.4–0.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 3371 KiB  
Article
Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge
by Beibei Shen, Lei Ding, Leichao Ma, Zhenwang Li, Alim Pulatov, Zheenbek Kulenbekov, Jiquan Chen, Saltanat Mambetova, Lulu Hou, Dawei Xu, Xu Wang and Xiaoping Xin
Remote Sens. 2022, 14(17), 4196; https://doi.org/10.3390/rs14174196 - 26 Aug 2022
Cited by 10 | Viewed by 2293
Abstract
Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) [...] Read more.
Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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18 pages, 3681 KiB  
Article
A Systematic Classification Method for Grassland Community Division Using China’s ZY1-02D Hyperspectral Observations
by Dandan Wei, Kai Liu, Chenchao Xiao, Weiwei Sun, Weiwei Liu, Lidong Liu, Xizhi Huang and Chunyong Feng
Remote Sens. 2022, 14(15), 3751; https://doi.org/10.3390/rs14153751 - 5 Aug 2022
Cited by 4 | Viewed by 1797
Abstract
The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner [...] Read more.
The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner Mongolia Autonomous Region, China, we propose a systematic classification method (SCM) for hyperspectral grassland community identification using China’s ZiYuan 1-02D (ZY1-02D) satellite. First, the sample label data were selected from the field-collected samples, vegetation map data, and function zoning data for the Nature Reserve. Second, the spatial features of the images were extracted using extended morphological profiles (EMPs) based on the reduced dimensionality of principal component analysis (PCA). Then, they were input into the random forest (RF) classifier to obtain the preclassification results for grassland communities. Finally, to reduce the influence of salt-and-pepper noise, the label similarity probability filter (LSPF) method was used for postclassification processing, and the RF was again used to obtain the final classification results. The results showed that, compared with the other seven (e.g., SVM, RF, 3D-CNN) methods, the SCM obtained the optimal classification results with an overall classification accuracy (OCA) of 94.56%. In addition, the mapping results of the SCM showed its ability to accurately identify various ground objects in large-scale grassland community scenes. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 3105 KiB  
Article
Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing
by Sharyn M. Hickey and Ben Radford
Remote Sens. 2022, 14(14), 3365; https://doi.org/10.3390/rs14143365 - 13 Jul 2022
Cited by 4 | Viewed by 3427
Abstract
Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea [...] Read more.
Mangroves are a globally important ecosystem experiencing significant anthropogenic and climate impacts. Two subtypes of mangrove are particularly vulnerable to climate-induced impacts (1): tidally submerged forests and (2) those that occur in arid and semi-arid regions. These mangroves are either susceptible to sea level rise or occur in conditions close to their physiological limits of temperature and freshwater availability. The spatial extent and impacts on these mangroves are poorly documented, because they have structural and environmental characteristics that affect their ability to be detected with remote sensing models. For example, tidally submerged mangroves occur in areas with large tidal ranges, which limits their visibility at high tide, and arid mangroves have sparse canopy cover and a shorter stature that occur in fringing and narrow stands parallel to the coastline. This study introduced the multi-dimensional space–time randomForest method (MSTRF) that increases the detectability of these mangroves and applies this on the North-west Australian coastline where both mangrove types are prevalent. MSTRF identified an optimal four-year period that produced the most accurate model (Accuracy of 80%, Kappa value 0.61). This model was able to detect an additional 32% (76,048 hectares) of mangroves that were previously undocumented in other datasets. We detected more mangrove cover using this timeseries combination of annual median composite Landsat images derived from scenes across the whole tidal cycle but also over climatic cycles such as EÑSO. The median composite images displayed less spectral differences in mangroves in the intertidal and arid zones compared to individual scenes where water was present during the tidal cycle or where the chlorophyll reflectance was low during hot and dry periods. We found that the MNDWI (Modified Normalised Water Index) and GCVI (Green Chlorophyll Vegetation Index) were the best predictors for deriving the mangrove layer using randomForest. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 5979 KiB  
Article
Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features
by Shan He, Peng Peng, Yiyun Chen and Xiaomi Wang
Remote Sens. 2022, 14(13), 3153; https://doi.org/10.3390/rs14133153 - 30 Jun 2022
Cited by 8 | Viewed by 2246
Abstract
Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection [...] Read more.
Machine learning (ML) classifiers have been widely used in the field of crop classification. However, having inputs that include a large number of complex features increases not only the difficulty of data collection but also reduces the accuracy of the classifiers. Feature selection (FS), which can availably reduce the number of features by selecting and reserving the most essential features for crop classification, is a good tool to solve this problem effectively. Different FS methods, however, have dissimilar effects on various classifiers, so how to achieve the optimal combination of FS methods and classifiers to meet the needs of high-precision recognition of multiple crops remains an open question. This paper intends to address this problem by coupling the analysis of three FS methods and six classifiers. Spectral, textual, and environmental features are firstly extracted as potential classification indexes from time-series remote sensing images from France. Then, three FS methods are used to obtain feature subsets and combined with six classifiers for coupling analysis. On this basis, 18 multi-crop classification models (FS–ML models) are constructed. Additionally, six classifiers without FS are constructed for comparison. The training set and the validation set for these models are constructed by using the Kennard-Stone algorithm with 70% and 30% of the samples, respectively. The performance of the classification model is evaluated by Kappa, F1-score, accuracy, and other indicators. The results show that different FS methods have dissimilar effects on various models. The best FS–ML model is RFAA+-RF, and its Kappa coefficient can reach 0.7968, which is 0.33–46.67% higher than that of other classification models. The classification results are highly dependent on the original classification index sets. Hence, the reasonability of combining spectral, textural, and environmental indexes is verified by comparing them with the single feature index set. The results also show that the classification strategy combining spectral, textual, and environmental indexes can effectively improve the ability of crop recognition, and the Kappa coefficient is 9.06–65.52% higher than that of the single unscreened feature set. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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14 pages, 9207 KiB  
Article
The Warming Effect of Urbanization in the Urban Agglomeration Area Accelerates Vegetation Growth on the Urban–Rural Gradient
by Zhitao Feng, Zhenhuan Liu and Yi Zhou
Remote Sens. 2022, 14(12), 2869; https://doi.org/10.3390/rs14122869 - 15 Jun 2022
Cited by 2 | Viewed by 2369
Abstract
Urbanization has changed the environmental conditions of vegetation growth, such as the heat island effect, which has an indirect impact on vegetation growth. However, the extent to which the direct and indirect effects of the thermal environment changes caused by urbanization on vegetation [...] Read more.
Urbanization has changed the environmental conditions of vegetation growth, such as the heat island effect, which has an indirect impact on vegetation growth. However, the extent to which the direct and indirect effects of the thermal environment changes caused by urbanization on vegetation growth are unclear. In this study, taking the example of the Guangdong–Hong Kong–Macao Greater Bay Area, a fast-growing national urban agglomeration in China, the relationship between vegetation growth and warming conditions during the period from 2001 to 2020 were explored by the net primary productivity (NPP) and land surface temperature (LST), based on the vegetation growth theory, in urban environments. The results show that there is a significant exponential relationship between the warming and the growth of large-scale vegetation. This relationship is mainly attributable to thermal environmental factors, since their multi-year average contribution rate on the interannual scale is 95.02%. The contribution rate varies on the seasonal scale, according to which the contribution rate is the largest in autumn and the smallest in winter. This research is of great significance for predicting the potential response of vegetation growth to future climate warming and improving vegetation growth in urban areas. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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21 pages, 18388 KiB  
Article
Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia
by Battsetseg Tuvdendorj, Hongwei Zeng, Bingfang Wu, Abdelrazek Elnashar, Miao Zhang, Fuyou Tian, Mohsen Nabil, Lkhagvadorj Nanzad, Amanjol Bulkhbai and Natsagsuren Natsagdorj
Remote Sens. 2022, 14(8), 1830; https://doi.org/10.3390/rs14081830 - 11 Apr 2022
Cited by 20 | Viewed by 3929
Abstract
Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 [...] Read more.
Accurate and early crop-type maps are essential for agricultural policy development and food production assessment at regional and national levels. This study aims to produce a crop-type map with acceptable accuracy and spatial resolution in northern Mongolia by optimizing the combination of Sentinel-1 (S1) and Sentinel-2 (S2) images with the Google Earth Engine (GEE) environment. A total of three satellite data combination scenarios are set, including S1 alone, S2 alone, and the combination of S1 and S2. In order to avoid the impact of data gaps caused by clouds on crop classification, this study reconstructed the time series of S1 and S2 with a 10-day interval using the median composite method, linear moving interpolation, and Savitzky–Golay (SG) filter. Our results indicated that crop-type classification accuracy increased with the increase in data length to all three data combination scenarios. S2 alone has higher accuracy than S1 alone and the combination of S1 and S2. The crop-type map with the highest accuracy was generated using S2 data from 150 days of the year (DOY) (11 May) to 260 DOY (18 September). The OA and kappa were 0.93 and 0.78, respectively, and the F1-score for spring wheat and rapeseed were 0.96 and 0.80, respectively. The classification accuracy of the crop increased rapidly from 210 DOY (end of July) to 260 DOY (August to mid-September), and then it remained stable after 260 DOY. Based on our analysis, we filled the gap of the crop-type map with 10 m spatial resolution in northern Mongolia, revealing the best satellite combination and the best period for crop-type classification, which can benefit the achievement of sustainable development goals 2 (SDGs2). Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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15 pages, 2794 KiB  
Article
Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses
by Yuyang Xie, Tao Yang, Xiaofeng Wang, Xi Chen, Shuxin Pang, Juan Hu, Anxian Wang, Ling Chen and Zehao Shen
Remote Sens. 2022, 14(8), 1806; https://doi.org/10.3390/rs14081806 - 8 Apr 2022
Cited by 23 | Viewed by 3415
Abstract
Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation [...] Read more.
Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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29 pages, 11545 KiB  
Article
Incorporation of Net Radiation Model Considering Complex Terrain in Evapotranspiration Determination with Sentinel-2 Data
by Linjiang Wang, Bingfang Wu, Abdelrazek Elnashar, Weiwei Zhu, Nana Yan, Zonghan Ma, Shirong Liu and Xiaodong Niu
Remote Sens. 2022, 14(5), 1191; https://doi.org/10.3390/rs14051191 - 28 Feb 2022
Cited by 4 | Viewed by 2931
Abstract
Evapotranspiration (ET) is the primary mechanism of water transformation between the land surface and atmosphere. Accurate ET estimation given complex terrain conditions is essential to guide water resource management in mountainous areas. This study is based on the ETWatch model driven by Sentinel-2 [...] Read more.
Evapotranspiration (ET) is the primary mechanism of water transformation between the land surface and atmosphere. Accurate ET estimation given complex terrain conditions is essential to guide water resource management in mountainous areas. This study is based on the ETWatch model driven by Sentinel-2 remote sensing data at a spatial resolution of 10 m incorporating a net radiation model considering the impact of a complex terrain. We tested our model with two years of data in two regions with a high relief near the Huairou (2020) and Baotianman (2019) weather stations. Regarding the validation results of the ET model, the coefficient of determination (R2) reached 0.84 in Huairou and 0.86 in Baotianman, while the root mean square error (RMSE) value reached 0.59 mm in Baotianman and 0.82 mm in Huairou. The validation results indicated that the model is applicable in regions with a complex terrain, and the ET results can capture topographic textures. In terms of the slope aspect, the ET value on south-facing slopes is higher than that on north-facing slopes in both study areas. Accurate ET monitoring in mountainous regions with a high relief yields a profound meaning in obtaining a better understanding of the characteristics of heat and water fluxes at different vegetation growth stages and underlying surface types, which can provide constructive suggestions for water management in mountainous areas. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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16 pages, 5296 KiB  
Article
A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region
by Yuanyuan Meng, Caiyong Wei, Yanpei Guo and Zhiyao Tang
Remote Sens. 2022, 14(4), 961; https://doi.org/10.3390/rs14040961 - 16 Feb 2022
Cited by 10 | Viewed by 2696
Abstract
Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and [...] Read more.
Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and associated phenology features between planted forests and other vegetation types. In this study, taking advantage of the Google Earth Engine (GEE) platform and taking the Ningxia Hui Autonomous Region in northwestern China as an example, we developed an approach to map planted forests in an arid region by applying long-term features of the NDVI derived from dense Landsat time series. Our land cover map achieved a satisfactory accuracy and relatively low uncertainty, with an overall accuracy of 93.65% and a kappa value of 0.92. Specifically, the producer (PA) and user accuracies (UA) were 92.48% and 91.79% for the planted forest class, and 93.88% and 95.83% for the natural forest class, respectively. The total planted forest area was estimated as 3608.72 km2 in 2020, accounting for 20.60% of the study area. The proposed mapping approach can facilitate assessment of the restoration effects of ecological engineering and research on ecosystem services and stability of planted forests. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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16 pages, 5862 KiB  
Article
Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe
by Cong Xu, Yuan Zeng, Zhaoju Zheng, Dan Zhao, Wenjun Liu, Zonghan Ma and Bingfang Wu
Remote Sens. 2022, 14(3), 671; https://doi.org/10.3390/rs14030671 - 30 Jan 2022
Cited by 13 | Viewed by 3939
Abstract
Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and [...] Read more.
Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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28 pages, 12098 KiB  
Article
Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China
by Zhipeng Li, Jie Ding, Heyu Zhang and Yiming Feng
Remote Sens. 2021, 13(24), 4995; https://doi.org/10.3390/rs13244995 - 8 Dec 2021
Cited by 7 | Viewed by 2819
Abstract
Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data [...] Read more.
Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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22 pages, 8547 KiB  
Article
Spatiotemporal Evolution of Wetland Eco-Hydrological Connectivity in the Poyang Lake Area Based on Long Time-Series Remote Sensing Images
by Yang Xia, Chaoyang Fang, Hui Lin, Huizhong Li and Bobo Wu
Remote Sens. 2021, 13(23), 4812; https://doi.org/10.3390/rs13234812 - 27 Nov 2021
Cited by 15 | Viewed by 2960
Abstract
Hydrological connectivity is important for maintaining the stability and function of wetland ecosystems. Small-scale hydrological connectivity restricts large-scale hydrological cycle processes. However, long-term evolutionary studies and quantitative evaluation of the hydrological connectivity of wetlands in the Poyang Lake area have not been sufficiently [...] Read more.
Hydrological connectivity is important for maintaining the stability and function of wetland ecosystems. Small-scale hydrological connectivity restricts large-scale hydrological cycle processes. However, long-term evolutionary studies and quantitative evaluation of the hydrological connectivity of wetlands in the Poyang Lake area have not been sufficiently conducted. In this study, we collected 21 Landsat remote sensing images and extracted land use data from 1989 to 2020, introducing a morphological spatial pattern analysis model to assess the wetland hydrological connectivity. A comprehensive method for evaluating the hydrological connectivity of wetlands was established and applied to the Poyang Lake area. The results showed that, over the course of 31 years, the wetland landscape in the Poyang Lake area changed dramatically, and the wetland area has generally shown a decreasing and then increasing trend, among which the core wetland plays a dominant role in the hydrological connectivity of the Poyang Lake area. In addition, the hydrological connectivity decreases as the core wetland area decreases. From 1989 to 2005, the landscape in the Poyang Lake area focused mainly on the transition from wetland to non-wetland. From 2005 to 2020, the conversion of wetland landscape types shows a clear reversal compared to the previous period, showing a predominant shift from non-wetland to wetland landscapes. The eco-hydrological connectivity of the wetlands in the Poyang Lake area from 1989 to 2020 first decreased, and then increased after 2005. In the early stage of the study (1989−2005), we found that the connectivity of 0.3444 in 2005 was the lowest value in the study period. A resolution of 30 m and an edge effect width of 60 m were optimal for studying the hydrological connectivity of wetlands in the Poyang Lake area. The main drivers of the changes in hydrological connectivity were precipitation and the construction of large-scale water conservation projects, as well as changes in land use. This study provides a good basis for assessing hydrological connectivity in a meaningful way, and is expected to provide new insights for maintaining and restoring biodiversity and related ecosystem services in the Poyang Lake area. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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18 pages, 7238 KiB  
Article
Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine
by Hao Fu, Wei Zhao, Qiqi Zhan, Mengjiao Yang, Donghong Xiong and Daijun Yu
Remote Sens. 2021, 13(23), 4785; https://doi.org/10.3390/rs13234785 - 25 Nov 2021
Cited by 3 | Viewed by 2407
Abstract
Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for [...] Read more.
Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 5033 KiB  
Article
Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm
by Panpan Wei, Weiwei Zhu, Yifan Zhao, Peng Fang, Xiwang Zhang, Nana Yan and Hao Zhao
Remote Sens. 2021, 13(23), 4762; https://doi.org/10.3390/rs13234762 - 24 Nov 2021
Cited by 11 | Viewed by 2150
Abstract
Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and [...] Read more.
Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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19 pages, 2953 KiB  
Article
Analysis of Landscape Connectivity among the Habitats of Asian Elephants in Keonjhar Forest Division, India
by Bismay Ranjan Tripathy, Xuehua Liu, Melissa Songer, Babar Zahoor, W. M. S. Wickramasinghe and Kirti Kumar Mahanta
Remote Sens. 2021, 13(22), 4661; https://doi.org/10.3390/rs13224661 - 19 Nov 2021
Cited by 12 | Viewed by 4083
Abstract
Land development has impacted natural landforms extensively, causing a decline in resources and negative consequences to elephant populations, habitats, and gene flow. Often, elephants seek to fulfill basic needs by wandering into nearby human communities, which leads to human–elephant conflict (HEC), a serious [...] Read more.
Land development has impacted natural landforms extensively, causing a decline in resources and negative consequences to elephant populations, habitats, and gene flow. Often, elephants seek to fulfill basic needs by wandering into nearby human communities, which leads to human–elephant conflict (HEC), a serious threat to conserving this endangered species. Understanding elephant space use and connectivity among their habitats can offset barriers to ecological flow among fragmented populations. We focused on the Keonjhar Forest Division in Eastern India, where HEC has resulted in the deaths of ~300 people and several hundred elephants, and damaged ~4100 houses and ~12,700 acres of cropland between 2001 and 2018. Our objectives were to (1) analyze elephant space use based on their occupancy; (2) map connectivity by considering the land structure and HEC occurrences; (3) assess the quality of mapped connectivity and identify potential bottlenecks. We found that (1) the study area has the potential to sustain a significant elephant population by providing safe connectivity; (2) variables like forests, precipitation, rural built-up areas, cropland, and transportation networks were responsible for predicting elephant presence (0.407, SE = 0.098); (3) five habitat cores, interconnected by seven corridors were identified, of which three habitat cores were vital for maintaining connectivity; (4) landscape features, such as cropland, rural built-up, mining, and transportation networks created bottlenecks that could funnel elephant movement. Our findings also indicate that overlooking HEC in connectivity assessments could lead to overestimation of functionality. The study outcomes can be utilized as a preliminary tool for decision making and early planning during development projects. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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13 pages, 2895 KiB  
Article
Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods
by Soo-In Sohn, Young-Ju Oh, Subramani Pandian, Yong-Ho Lee, John-Lewis Zinia Zaukuu, Hyeon-Jung Kang, Tae-Hun Ryu, Woo-Suk Cho, Youn-Sung Cho and Eun-Kyoung Shin
Remote Sens. 2021, 13(20), 4149; https://doi.org/10.3390/rs13204149 - 16 Oct 2021
Cited by 19 | Viewed by 3560
Abstract
The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in [...] Read more.
The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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20 pages, 6216 KiB  
Article
Forest Cover and Sustainable Development in the Lumbini Province, Nepal: Past, Present and Future
by Bhagawat Rimal, Hamidreza Keshtkar, Nigel Stork and Sushila Rijal
Remote Sens. 2021, 13(20), 4093; https://doi.org/10.3390/rs13204093 - 13 Oct 2021
Cited by 9 | Viewed by 6326
Abstract
The analysis of forest cover change at different scales is an increasingly important research topic in environmental studies. Forest Landscape Restoration (FLR) is an integrated approach to manage and restore forests across various landscapes and environments. Such restoration helps to meet the targets [...] Read more.
The analysis of forest cover change at different scales is an increasingly important research topic in environmental studies. Forest Landscape Restoration (FLR) is an integrated approach to manage and restore forests across various landscapes and environments. Such restoration helps to meet the targets of Sustainable Development Goal (SDG)–15, as outlined in the UN Environment’s sixth Global Outlook, which includes the sustainable management of forests, the control of desertification, reducing degradation, biodiversity loss, and the conservation of mountain ecosystems. Here, we have used time series Landsat images from 1996 to 2016 to see how land use, and in particular forest cover, have changed between 1996 and 2016 in the Lumbini Province of Nepal. In addition, we simulated projections of land cover (LC) and forest cover change for the years 2026 and 2036 using a hybrid cellular automata Markov chain (CA–Markov) model. We found that the overall forest area increased by 199 km2 (2.1%), from a 9491 km2 (49.3%) area in 1996 to 9691 km2 (50.3%) area in 2016. Our modeling suggests that forest area will increase by 81 km2 (9691 to 9772 km2) in 2026 and by 195 km2 (9772 km2 to 9966 km2) in 2036. They are policy, planning, management factors and further strategies to aid forest regeneration. Clear legal frameworks and coherent policies are required to support sustainable forest management programs. This research may support the targets of the Sustainable Development Goals (SDG), the land degradation neutral world (LDN), and the UN decade 2021–2031 for ecosystem restoration. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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24 pages, 7153 KiB  
Article
Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
by Donghui Shi, Yishao Shi and Qiusheng Wu
Remote Sens. 2021, 13(17), 3540; https://doi.org/10.3390/rs13173540 - 6 Sep 2021
Cited by 4 | Viewed by 4330
Abstract
Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a [...] Read more.
Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a multidimensional assessment framework for evaluating water provisioning ecosystem services by integrating multi-source remote sensing products. We applied the multidimensional framework to assess lake water ecosystem services in the state of Minnesota, US. We found that: (1) the water provisioning ecosystem services degraded during 1998–2018 from three assessment perspectives; (2) the output, efficiency, and trend indices have stable distribution and various spatial clustering patterns from 1998 to 2018; (3) high-level efficiency depends on high-level output, and low-level output relates to low-level efficiency; (4) Western Minnesota, including Northwest, West Central, and Southwest, degraded more severely than other zones in water provisioning services; (5) human activities impact water provisioning services in Minnesota more than climate changes. These findings can benefit policymakers by identifying the priorities for better protection, conservation, and restoration of lake ecosystems. Our multidimensional assessment framework can be adapted to evaluate ecosystem services in other regions. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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18 pages, 5827 KiB  
Article
The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity
by Yujin Zhao, Yihan Sun, Wenhe Chen, Yanping Zhao, Xiaoliang Liu and Yongfei Bai
Remote Sens. 2021, 13(15), 3034; https://doi.org/10.3390/rs13153034 - 2 Aug 2021
Cited by 16 | Viewed by 4759
Abstract
Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent [...] Read more.
Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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14 pages, 3795 KiB  
Technical Note
Calculation of the Rub’ al Khali Sand Dune Volume for Estimating Potential Sand Sources
by Fahad Almutlaq, Faten Nahas and Kevin Mulligan
Remote Sens. 2022, 14(5), 1216; https://doi.org/10.3390/rs14051216 - 2 Mar 2022
Cited by 2 | Viewed by 6555
Abstract
The Rub’ al Khali desert (or Empty Quarter) is the largest and perhaps most significant sand sea in the world. Located on the southern Arabian Peninsula, the dune field has remained largely unexplored owing to the harsh clime and difficult terrain. This study [...] Read more.
The Rub’ al Khali desert (or Empty Quarter) is the largest and perhaps most significant sand sea in the world. Located on the southern Arabian Peninsula, the dune field has remained largely unexplored owing to the harsh clime and difficult terrain. This study takes advantage of geospatial technology (interpolations, supervised classification, minimum focal statistic) to extract information from the data contained in global Digital Elevation Model (DEM)s, satellite imagery. The main objective here is to identify and map different dune forms within the sand sea, estimate the volume of sand and explore probable sources of sand. The analysis of dune color strongly suggests that the sand is not completely reworked and intermixed. If this is true, a spatial variability map of the mineral composition of the sand could be very revealing. The red sand is quite pronounced, the largest volume of sand (~36%) is associated with the yellow color class. Yellow sand covers most of the western part of the dunes field and seems to be a transitional color between red and white sand in the eastern part of the dune field. This suggests that the yellow sand might be derived from both local and regional sources, or it might be less oxidized, reworked, or have a different composition that represents a combination of red and white sand. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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17 pages, 5131 KiB  
Technical Note
Unraveling the Spatio-Temporal Relationship between Ecosystem Services and Socioeconomic Development in Dabie Mountain Area over the Last 10 years
by Jianfeng Liu, Lin Chen, Zhonghua Yang, Yifan Zhao and Xiwang Zhang
Remote Sens. 2022, 14(5), 1059; https://doi.org/10.3390/rs14051059 - 22 Feb 2022
Cited by 10 | Viewed by 1913
Abstract
The Dabie Mountain area is a typical poverty-stricken area in China. It is of great significance to evaluate the ecosystem service value and its impact mechanism toward optimizing the ecological structure and coordinating ecological protection and economic development. This study determined the ecosystem [...] Read more.
The Dabie Mountain area is a typical poverty-stricken area in China. It is of great significance to evaluate the ecosystem service value and its impact mechanism toward optimizing the ecological structure and coordinating ecological protection and economic development. This study determined the ecosystem service value coefficient and calculated the ecosystem service value (ESV) according to the regional economic development in the past ten years, and the ESV was spatialized based on NPP, which is closely related to ecological function. The temporal and spatial variation of ESV was then analyzed, and an RDE index was proposed to describe its response to land cover change. Further, the relationship between ESV and several parameters that reflect socioeconomic development was researched and analyzed. The results show that the total ESV in the study area first decreased and then increased, with an overall increase of CNY 3.895 billion. Among the land cover types, forest land had the greatest impact, contributing more than 70%. In the ecosystem service functions, the contribution of regulation function exceeded 50%. ESV was found to be sensitive to land cover change. On average, every 1 km2 change leads to an ESV change of about CNY 1 million. Socioeconomic-related parameters were negatively correlated with ESV, among which the correlation with per capita disposable income was the weakest, indicating that there was no obvious contradiction between human well-being and ESV. Therefore, a path for harmonious symbiotic development can be found between man and nature. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
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