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Landscape Ecology in Remote Sensing
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Landscape Ecology in Remote Sensing

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 January 2023) | Viewed by 56394

Special Issue Editors

Dr. Jie Dai

E-Mail Website
Guest Editor
Center for Global Discovery and Conservation Science, Arizona State University Tempe, Tempe, AZ 85281, USA
Interests: remote sensing; imaging spectroscopy; coupled human and natural systems
Prof. Dr. Li An

E-Mail Website
Guest Editor
Department of Geography, San Diego State University, San Diego, CA 92182, USA
Interests: human-environment; landscape ecology; GIScience; complex systems

Special Issue Information

Dear Colleagues,

Among numerous definitions, landscape ecology can be summarized as the study of interactions between landscape patterns and various ecological processes, such as energy flux, material flow, and species distribution. Traditional airborne and spaceborne remote sensing imagery have been extensively involved with studies in landscape ecology. High spatial resolution aerial photography and moderate to coarse resolution satellite imagery, including those collected by Landsat, Sentinel and MODIS, provide spatial information of ecological processes and input for statistical analysis and computer simulations. In addition to traditional data, the notable progress of new platforms, such as unmanned airborne vehicles (UAV), and emerging techniques, including light detection and ranging (LiDAR), as well as high-fidelity imaging spectroscopy, has enabled novel perspectives, approaches and results in landscape ecology research. This Special Issue aims to summarize the applications of traditional remote sensing, meanwhile deepening the knowledge of emerging platforms and techniques in the studies of landscape ecology. We encourage the submission of manuscripts that address the following, but are not limited to, topics in understanding landscape patterns, ecological processes, and their interactions:

  • Aboveground biomass estimation
  • Biodiversity investigation
  • Species habitat and distribution mapping
  • Landscape pattern mapping, analysis, and implications
  • Application of LiDAR, imaging spectroscopy and other emerging techniques

Dr. Jie Dai
Prof. Dr. Li An
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.

Keywords

  • landscape ecology
  • remote sensing
  • biomass
  • biodiversity
  • species distribution
  • land pattern
  • LiDAR
  •  imaging spectroscopy

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Published Papers (7 papers)

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20 pages, 5416 KiB  
Article
Coupling an Ecological Network with Multi-Scenario Land Use Simulation: An Ecological Spatial Constraint Approach
by Wenbin Nie, Bin Xu, Shuai Ma, Fan Yang, Yan Shi, Bintao Liu, Nayi Hao, Renwu Wu, Wei Lin and Zhiyi Bao
Remote Sens. 2022, 14(23), 6099; https://doi.org/10.3390/rs14236099 - 1 Dec 2022
Cited by 17 | Viewed by 2833
Abstract
To balance ecological protection and urban development, a land use simulation model that couples an ecological network (EN) and multiple scenarios was developed based on the PLUS model. The simulation of land use in the Qiantang River Basin in 2030 successfully demonstrates the [...] Read more.
To balance ecological protection and urban development, a land use simulation model that couples an ecological network (EN) and multiple scenarios was developed based on the PLUS model. The simulation of land use in the Qiantang River Basin in 2030 successfully demonstrates the usefulness of the EN-PLUS model. In this model, conventional ecological constraints (nature reserves and water areas) and three different EN levels were taken as restricted conversion areas during the simulation. Then, four ecological constraints were coupled with four simulation scenarios: business as usual (BAU), rapid urban development (RUD), ecological protection (EP), and urban- and ecology-balanced (UEB). Information from the analysis of model simulation results can be used to reduce the potential damage to a range of land cover types. However, this protective effect is not obvious under the RUD scenario due to the impact of significant human disturbance. Furthermore, although EP is the scenario with the least ecological damage at the whole watershed scale, this is not the case for all subbasins. This indicates the existence of a landscape scale effect. Therefore, the best development scenario should be selected by comprehensively weighing the scale effect and the ecological characteristics of each subbasin. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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38 pages, 15698 KiB  
Article
Mapping Floristic Composition Using Sentinel-2A and a Case Study Evaluation of Its Application in Elephant Movement Ecology in Sagalla, Kenya
by Gloria Mugo, Lydia Tiller and Lucy King
Remote Sens. 2022, 14(21), 5386; https://doi.org/10.3390/rs14215386 - 27 Oct 2022
Cited by 2 | Viewed by 36892
Abstract
The quantification of vegetation structure and composition at local and global scales provides valuable information for understanding the balance of the natural and human-made environment, which is crucial for natural resource planning and management, and the sustenance of ecosystem biodiversity. In this study, [...] Read more.
The quantification of vegetation structure and composition at local and global scales provides valuable information for understanding the balance of the natural and human-made environment, which is crucial for natural resource planning and management, and the sustenance of ecosystem biodiversity. In this study, we proposed using the Sentinel 2A imagery to classify vegetation cover into communities based on the floristic association of individual vegetation species. We apply traditional remote sensing techniques to process the satellite image and identify training regions of interest (ROI) which are thoroughly assessed for spectral uniqueness before using the pixel-based supervised classification algorithms for our classification. Ground truthing assessment and species dominance computations are done to determine the vegetation community composition and naming based on floristic associations. We apply the floristic compositions output in analysing elephant movement tracks in the area, to assess the potential influence the location of specific vegetation species and communities utilized by elephants has on their movement and presence, as well as on elephant bulls and family groupings. The results show that the 10 m spatial resolution Sentinel-2A is suitable for investigating and mapping vegetation species in communities for large-scale mapping operations. We determined Near-Infrared band 8 and shortwave Infrared band 11 as key for identifying and differentiating ROIs at the floristic association community vegetation mapping level. We attained an overall accuracy of 87.395%. The analysis proved the 10 m spatial resolution of Sentinel 2A to be sufficient in distinguishing vegetation communities, including those with similar dominant species but variations in other contributing species. We also found a direct connection between vegetation location and elephant movement based on the summative analysis of utilised vegetation by the different elephant groupings. Bull elephants were predominantly present in areas with Combretum, family groups in areas with Commiphora, and mixed groups with both bulls and families in areas with Commiphora, and Cissus. This study shows the value that remote-sensing scientific support can offer conservationists and governments in objective evidence-based land management, policy making and governance. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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16 pages, 3289 KiB  
Article
Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread
by Gavin M. Schag, Douglas A. Stow, Philip J. Riggan and Atsushi Nara
Remote Sens. 2022, 14(16), 3980; https://doi.org/10.3390/rs14163980 - 16 Aug 2022
Cited by 6 | Viewed by 2398
Abstract
The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during [...] Read more.
The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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30 pages, 20993 KiB  
Article
Evolution Analysis of Ecological Networks Based on Spatial Distribution Data of Land Use Types Monitored by Remote Sensing in Wuhan Urban Agglomeration, China, from 2000 to 2020
by Yanchi Lu, Yaolin Liu, Dan Huang and Yanfang Liu
Remote Sens. 2022, 14(11), 2618; https://doi.org/10.3390/rs14112618 - 30 May 2022
Cited by 21 | Viewed by 3394
Abstract
Construction and protection of ecological networks (ENs) is considered to be an effective means to curb habitat fragmentation and strengthen landscape connectivity. In this study, a complete evaluation framework of ENs based on “quality–function–structure” was proposed to support the formulation of protection strategies [...] Read more.
Construction and protection of ecological networks (ENs) is considered to be an effective means to curb habitat fragmentation and strengthen landscape connectivity. In this study, a complete evaluation framework of ENs based on “quality–function–structure” was proposed to support the formulation of protection strategies for ENs. First, we built the ENs of Wuhan urban agglomeration (WUA) from 2000 to 2020 based on the advantages of circuit theory and remote sensing data of land use monitoring. The results showed that land development activities are an important driving force for the temporal and spatial evolution of global ENs. Forest fragmentation, transitional urban expansion, and agricultural reclamation were important inducements for the shrinkage of ecological sources. They may also increase the resistance of species migration, which will lead to qualitative change and even fracture of ecological corridors. Second, circuit theory, centrality index, and complex network theory were applied to evaluate the quality defects, functional connectivity, and topology characteristics of ENs in WUA, respectively, from 2000 to 2020. The results showed that the antagonism between ecological corridors and land development activities led to ecological quality defects (ecological barriers and pinchpoints). Different land development models had differential effects on centrality indexes. Moreover, the main trunk in the northern Dabie Mountains and the southern Mufu mountains was developed, while the secondary trunks were abundant in the middle of WUA. Finally, we proposed protection strategies for ENs based on the coupling of the “quality–function–structure” of WUA in 2020. It is suggested that all ecological sources must be included in nature reserves to prevent natural or manmade erosion. The key areas to be repaired were determined through the quality evaluation of ecological corridors. The priority of construction and protection of ecological corridors was determined by coupling two topological structures and functions. We argue that specific protection strategies and directions can be determined according to the construction objectives of local ENs. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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25 pages, 7145 KiB  
Article
Land Cover Dynamics on the Lower Ganges–Brahmaputra Delta: Agriculture–Aquaculture Transitions, 1972–2017
by Daniel Sousa and Christopher Small
Remote Sens. 2021, 13(23), 4799; https://doi.org/10.3390/rs13234799 - 26 Nov 2021
Cited by 5 | Viewed by 2867
Abstract
Aquaculture in tropical and subtropical developing countries has expanded in recent years. This practice is controversial due to its potential for serious economic, food security, and environmental impacts—especially for intensive operations in and near mangrove ecosystems, where many shrimp species spawn. While considerable [...] Read more.
Aquaculture in tropical and subtropical developing countries has expanded in recent years. This practice is controversial due to its potential for serious economic, food security, and environmental impacts—especially for intensive operations in and near mangrove ecosystems, where many shrimp species spawn. While considerable effort has been directed toward understanding aquaculture impacts, maps of spatial extent and multi-decade spatiotemporal dynamics remain sparse. This is in part because aquaculture ponds (ghers) can be challenging to distinguish from other shallow water targets on the basis of water-leaving radiance alone. Here, we focus on the Lower Ganges–Brahmaputra Delta (GBD), one of the most expansive areas of recent aquaculture growth on Earth and adjacent to the Sundarbans mangrove forest, a biodiversity hotspot. We use a combination of MODIS 16-day EVI composites and 45 years (1972–2017) of Landsat observations to characterize dominant spatiotemporal patterns in the vegetation phenology of the area, identify consistent seasonal optical differences between flooded ghers and other land uses, and quantify the multi-decade expansion of standing water bodies. Considerable non-uniqueness exists in the spectral signature of ghers on the GBD, propagating into uncertainty in estimates of spatial extent. We implement three progressive decision boundaries to explicitly quantify this uncertainty and provide liberal, moderate, and conservative estimates of flooded gher extent on three different spatial scales. Using multiple extents and multiple thresholds, we quantify the size distribution of contiguous regions of flooded gher extent at ten-year intervals. The moderate threshold shows standing water area within Bangladeshi polders to have expanded from less than 300 km2 in 1990 to over 1400 km2 in 2015. At all three scales investigated, the size distribution of standing water bodies is increasingly dominated by larger, more interconnected networks of flooded areas associated with aquaculture. Much of this expansion has occurred in immediate proximity to the Bangladeshi Sundarbans. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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21 pages, 5892 KiB  
Article
Extracting Frequent Sequential Patterns of Forest Landscape Dynamics in Fenhe River Basin, Northern China, from Landsat Time Series to Evaluate Landscape Stability
by Yue Zhang, Xiangnan Liu, Qin Yang, Zhaolun Liu and Yu Li
Remote Sens. 2021, 13(19), 3963; https://doi.org/10.3390/rs13193963 - 3 Oct 2021
Cited by 12 | Viewed by 3106
Abstract
The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel [...] Read more.
The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel method for analyzing the dynamics of landscape patterns. First, patch density (PD), largest patch index (LPI), landscape shape index (LSI), and contiguity index (CI) were used to identify the types of forest spatial patterns. The frequent sequential pattern mining method was used to detect the frequent subsequences from the time series of landscape pattern types from 1991 to 2020 and further evaluate the forest landscape stability of the Fenhe River Basin in China. The results show that different frequent sequence patterns have conspicuous spatial and temporal differences, which describe the evolution processes and stability changes during a certain period of forest evolution and play an important role in the analysis of forest dynamics. The proportion of the disturbed regions to the total forest area exhibited a downward trend. The long-term evolution pattern indicates that there are many evolution processes and trends in the forest at the same time, showing an aggregation distribution law. Compared with 2016, the forest landscape has become complete in 2020, and the overall stability of the Fenhe River Basin has improved. This study can provide scientific support to land managers and policy implementers and offer a new perspective for studying forest landscape pattern changes and evaluating landscape stability. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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13 pages, 2986 KiB  
Technical Note
LiDAR Reveals the Process of Vision-Mediated Predator–Prey Relationships
by Yanwen Fu, Guangcai Xu, Shang Gao, Limin Feng, Qinghua Guo and Haitao Yang
Remote Sens. 2022, 14(15), 3730; https://doi.org/10.3390/rs14153730 - 4 Aug 2022
Cited by 5 | Viewed by 2613
Abstract
Exploring the processes of interspecific relationships is crucial to understanding the mechanisms of biodiversity maintenance. Visually detecting interspecies relationships of large mammals is limited by the reconstruction accuracy of the environmental structure and the timely detection of animal behavior. Hence, we used backpack [...] Read more.
Exploring the processes of interspecific relationships is crucial to understanding the mechanisms of biodiversity maintenance. Visually detecting interspecies relationships of large mammals is limited by the reconstruction accuracy of the environmental structure and the timely detection of animal behavior. Hence, we used backpack laser scanning (BLS) to reconstruct the high-resolution three-dimensional environmental structure to simulate the process of a predator approaching its prey, indicating that predator tigers would reduce their visibility by changing their behavior. Wild boars will nibble off about 5m of branches around the nest in order to create better visibility around the nest, adopting an anti-predation strategy to detect possible predators in advance. Our study not only points out how predator–prey relationships are affected by visibility as the environment mediates it, but also provides an operable framework for exploring interspecific relationships from a more complex dimension. Finally, this study provides a new perspective for exploring the mechanisms of biodiversity maintenance. Full article
(This article belongs to the Special Issue Landscape Ecology in Remote Sensing)
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