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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 32371

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Special Issue Editors

Dr. Jun Li

E-Mail Website
Guest Editor

College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Interests: remote sensing of ecology and environment; geospatial analysis; ecology and environment in mining areas; machine learning; spatiotemporal data mining

Dr. Xinyi Shen

E-Mail Website
Guest Editor

Civil and Environmental Engineering, University of Connecticut, 159 Discovery Dr., Storrs, CT 06269, USA
Interests: flood inundation modeling and observatory; Humans, Disasters, and the Built Environment; microwave remote sensing; artificial intelligence; compound flooding
Special Issues, Collections and Topics in MDPI journals

Dr. Qiusheng Wu

E-Mail Website1 Website2
Guest Editor

Department of Geography, University of Tennessee, Knoxville, TN 37996, USA
Interests: remote sensing; GIS; wetland hydrology; LiDAR; google earth engine
Special Issues, Collections and Topics in MDPI journals

Dr. Chengye Zhang

E-Mail Website
Guest Editor

College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Interests: quantitative remote sensing; multi- and hyper-spectral remote sensing; remote sensing of vegetation; machine learning; radiative transfer model
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past several decades, intensive human activities, such as urban expansion, industrial emissions, farming, deforestation, mining, tourism and land reclamation, have significantly altered the natural environment. In other words, the environmental changes have been deeply coupled with a variety of human activities, leading to various environmental problems, such as arable land degradation, deforestation, air pollution, and water pollution. Hence, effective environmental protection and governance are vital in the areas with intensive human activities, especially in the environmentally vulnerable areas, such as farming-pastoral transitional zones, wetlands, arid and semi-arid areas, coastal zones, and alpine-cold regions. In this issue, we call for the papers which address the following problems: (1) How to monitor the environmental alteration by human activities at different spatiotemporal scales; (2) How to quantify, evaluate, and predict the impact of human activities on the environment; (3) How to decompose or evaluate the compound influence of various natural disturbance (e.g., heat stress, water stress, and topography) and human alteration (e.g., water conservancy projects, mining, deforestation) on the environment; and (4) What is the corresponding pattern or mechanism of the environmental evolution to different human activities.

Satellite and airborne remote sensing has proven to be a valuable tool for retrieving environmental parameters, and it has been widely used for monitoring environmental change at regional or global scales, with advantages of looking back in the long-time history and the time-space continuum. Geospatial approaches have been popularly used in analyzing the coupled effects from multiple factors based on remotely sensed images and other geospatial datasets. Both remote sensing and geospatial approaches are critical for interpreting the impact of human activities on the environment.

This special issue aims to enrich the current knowledge in the area of “Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities”. We would like to invite you to contribute to this Special Issue of Remote Sensing by submitting original manuscripts, experimental work, and/or reviews in the related fields, from the perspectives of new theories, new datasets, new methods, new findings, and new applications. In this direction, the achievements in this special issue are expected to make scientific contributions to the environmental protection and governance affected by a variety of human activities.

Potential topics of the special issue include but are not limited to the following:

Development of new theories or paradigms for investigating the environment affected by human activities based on remote sensing and geospatial approaches.
Applications of new geospatial datasets for a better understanding of the environment affected by human activities.
New methods for retrieving environmental parameters (fractional vegetation cover, vegetation biodiversity, vegetation biomass, air & soil & water pollution, soil moisture, soil erosion, etc.) by remote sensing, quantifying and evaluating environmental quality, deriving the pattern/mechanism of environmental change, and decomposing the compound influence from driving factors.
New findings about the responding pattern or mechanism of the environmental evolution to different human activities.
New geospatial or remote sensing tools, software, platforms for studying the environment affected by human activities.

Dr. Jun Li
Dr. Xinyi Shen
Dr. Qiusheng Wu
Dr. Chengye Zhang
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

environment
ecology
human activities
remote sensing
GIS
geospatial analysis

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Published Papers (13 papers)

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Editorial

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3 pages, 147 KiB

Open AccessEditorial

Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities

by Chengye Zhang, Jun Li, Xinyi Shen and Qiusheng Wu

Remote Sens. 2024, 16(18), 3364; https://doi.org/10.3390/rs16183364 - 10 Sep 2024

Viewed by 909

Abstract

In the past few decades, drastic human activities such as urban expansion, agricultural and pastoral activities, water conservancy construction, deforestation, mining, and tourism have significantly altered regional ecological environments [...] Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

Research

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17 pages, 4387 KiB

Open AccessArticle

A Framework for the Construction of a Heritage Corridor System: A Case Study of the Shu Road in China

by Fengting Yue, Xiaoqin Li, Qian Huang and Dan Li

Remote Sens. 2023, 15(19), 4650; https://doi.org/10.3390/rs15194650 - 22 Sep 2023

Cited by 6 | Viewed by 1835

Abstract

Heritage corridors are methods to effectively protect and utilize linear cultural heritage based on the concept of regional conservation. The construction of a heritage corridor system is extremely important to preserve the natural environment of the heritage corridor area as well as the history and culture alongside. The majority of the research on the construction of heritage corridors heretofore focused on the generation of corridors, whereas studies on the classification of corridors are relatively limited, without a complete system for the construction of heritage corridors. Therefore, this paper aimed to (1) establish a comprehensive system for the construction of heritage corridors, (2) provide new ideas for the construction of heritage corridors, and (3) guide the scientific development of heritage corridors combining conservation and tourism. In the first place, the minimum cumulative resistance (MCR) model was applied to analyze the spatial structure of the study area and explore site selection of the heritage corridors; secondly, spatial syntax was used to measure the heritage corridors and determine the level of the heritage corridors; last but not least, the kernel density analysis was used to classify the types of heritage corridors. The present study shows that the heritage corridor system is built in a scientific approach, covering all aspects including construction, protection, and development. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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16 pages, 4196 KiB

Open AccessArticle

Spatiotemporal Pattern of Invasive Pedicularis in the Bayinbuluke Land, China, during 2019–2021: An Analysis Based on PlanetScope and Sentinel-2 Data

by Wuhua Wang, Jiakui Tang, Na Zhang, Yanjiao Wang, Xuefeng Xu and Anan Zhang

Remote Sens. 2023, 15(18), 4383; https://doi.org/10.3390/rs15184383 - 6 Sep 2023

Cited by 5 | Viewed by 1238

Abstract

The accurate identification and monitoring of invasive plants are of great significance to sustainable ecological development. The invasive Pedicularis poses a severe threat to native biodiversity, ecological security, socioeconomic development, and human health in the Bayinbuluke Grassland, China. It is imperative and useful to obtain a precise distribution map of Pedicularis for controlling its spread. This study used the positive and unlabeled learning (PUL) method to extract Pedicularis from the Bayinbuluke Grassland based on multi-period Sentinel-2 and PlanetScope remote sensing images. A change rate model for a single land cover type and a dynamic transfer matrix were constructed under GIS to reflect the spatiotemporal distribution of Pedicularis. The results reveal that (1) the PUL method accurately identifies Pedicularis in satellite images, achieving F1-scores above 0.70 and up to 0.94 across all three datasets: PlanetScope data (seven features), Sentinel-2 data (seven features), and Sentinel-2 data (thirteen features). (2) When comparing the three datasets, the number of features is more important than the spatial resolution in terms of use in the PUL method of Pedicularis extraction. Nevertheless, when compared with PlanetScope data, Sentinel-2 data demonstrated a higher level of accuracy in predicting the distribution of Pedicularis. (3) During the 2019–2021 growing season, the distribution area of Pedicularis decreased, and the distribution was mainly concentrated in the northeast and southeast of Bayinbuluke Swan Lake. The acquired spatiotemporal pattern of invasive Pedicularis could potentially be used to aid in controlling Pedicularis spread or elimination, and the methods proposed in this study could be adopted by the government as a low-cost strategy to identify priority areas in which to concentrate efforts to control and continue monitoring Pedicularis invasion. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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18 pages, 6698 KiB

Open AccessArticle

Quantifying Water Impoundment-Driven Air Temperature Changes in the Dammed Jinsha River, Southwest China

by Xinzhe Li, Jia Zhou, Yangbin Huang, Ruyun Wang and Tao Lu

Remote Sens. 2023, 15(17), 4280; https://doi.org/10.3390/rs15174280 - 31 Aug 2023

Cited by 2 | Viewed by 1197

Abstract

A number of previous studies have contributed to a better understanding of the thermal impacts of dam-related reservoirs on stream temperature, but very few studies have focused on air temperature, especially at the catchment scale. In addition, due to the lack of quantitative analysis, the identification of the effects of water impoundment on regional air temperature is still lacking. We investigated the impacts of reservoirs on the regional air temperature changes before and after two large dam constructions in the lower Jinsha River located in southwest China, by using a 40 year record of reanalysis data at 90 m resolutions. Furthermore, the long short-term memory (LSTM) model was also employed to construct an impoundment effect on the temperature (IET) index. Research results indicate that compared to the pre-impoundment period (1980–2012), the variations in the air temperature at the catchment scale were reduced during the post-impoundment period (2013–2019). The annual maximum air temperature decreased by 0.4 °C relative to the natural regimes. In contrast, the cumulative effects of dam-related reservoirs increased the annual mean and minimum air temperature by 0.1 °C and 1.0 °C, respectively. Warming effects prevailed during the dry season and in the regions with high elevations, while cooling effects dominated within a 4 km buffer of the reservoirs. Therefore, this study offers important insights about the impacts of anthropogenic impoundments on air temperature changes, which could be useful for policymakers to have a more informed and profound understanding of local climate changes in dammed areas. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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22 pages, 12880 KiB

Open AccessArticle

Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China

by Yanan Jiang, Lu Liao, Huiyuan Luo, Xing Zhu and Zhong Lu

Remote Sens. 2023, 15(16), 3995; https://doi.org/10.3390/rs15163995 - 11 Aug 2023

Cited by 6 | Viewed by 1502

Abstract

Reservoir water and rainfall, leading to fluctuations groundwater levels, are the main triggering factors that induce landslides in the Three Gorges Reservoir area. This study investigates the response mechanism of landslide deformation under reservoir water and rainfall variations through long-time on-site observations. To address the non-stationary characteristics of the time-series records, joint time-frequency analysis (JTFA) is first introduced into our landslide prediction model. This model employs optimal variational mode decomposition (VMD) to obtain specific signal components with clear physical meaning, such as trend component and periodic components. Then, multi-scale response analysis between the displacement and external factors three wavelet methods was conducted. The analysis results show a 1 year primary cycle of the time series associated with the landslide evolution. The reservoir water level and rainfall show anti-phase fluctuations. The periodic displacement correlates significantly with rainfall, lagging by about two months. The reservoir water is anti-phase with the landslide displacement, preceding it by approximately three months (−51 ± 8° phase difference). For landslide displacement prediction, the gated recurrent units (GRU) neural network model is integrated into the deep learning forecasting architecture. The model takes into account the correlation and hysteresis effect of input variables. Through six experiments, we investigate the effect of data volume on model predictions to determine the optimal model. The results demonstrate that our proposed model ensures high performance in landslide prediction. Moreover, a comparison with six other intelligent algorithms shows the advantages of our model in terms of time-effectiveness and long-sequence forecasting. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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20 pages, 16004 KiB

Open AccessArticle

Accessing the Time-Series Two-Dimensional Displacements around a Reservoir Using Multi-Orbit SAR Datasets: A Case Study of Xiluodu Hydropower Station

by Qi Chen, Heng Zhang, Bing Xu, Zhe Liu and Wenxiang Mao

Remote Sens. 2023, 15(1), 168; https://doi.org/10.3390/rs15010168 - 28 Dec 2022

Cited by 2 | Viewed by 1938

Abstract

The construction of large-scale hydropower stations could solve the problem of China’s power and energy shortages. However, the construction of hydropower stations requires reservoir water storage. Artificially raising the water level by several tens of meters or even hundreds of meters will undoubtedly change the hydrogeological conditions of an area, which will lead to surface deformation near the reservoir. In this paper, we first used SBAS-InSAR technology to monitor the surface deformation near the Xiluodu reservoir area for various data and analyzed the surface deformation of the Xiluodu reservoir area from 2014 to 2019. By using the 12 ALOS2 ascending data, the 100 Sentinel-1 ascending data, and the 97 Sentinel-1 descending data, the horizontal and vertical deformations of the Xiluodu reservoir area were obtained. We found that the Xiluodu reservoir area is mainly deformed along the vertical shore, with a maximum deformation rate of 250 mm/a, accompanied by vertical deformation, and the maximum deformation rate is 60 mm/a. Furthermore, by analyzing the relationship between the horizontal deformation sequence, the vertical deformation sequence, and the impoundment, we found the following: (1) Since the commencement of Xiluodu water storage, the vertical shore direction displacement has continued to increase, indicating that the deformation caused by the water storage is not due to the elastic displacement caused by the load, but by irreversible shaping displacement. According to its development trend, we speculate that the vertical shore direction displacement will continue to increase until it eventually stabilizes; (2) Vertical displacement increases rapidly in the initial stage of water storage; after two water-storage cycles, absolute settlement begins to slow down in the vertical direction, but its deformation still changes with the change in the storage period. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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18 pages, 2946 KiB

Open AccessArticle

Evolution Simulation and Risk Analysis of Land Use Functions and Structures in Ecologically Fragile Watersheds

by Yafei Wang, Yao He, Jiuyi Li and Yazhen Jiang

Remote Sens. 2022, 14(21), 5521; https://doi.org/10.3390/rs14215521 - 2 Nov 2022

Cited by 6 | Viewed by 1885

Abstract

The evolution of land use functions and structures in ecologically fragile watersheds have a direct impact on regional food security and sustainable ecological service supply. Previous studies that quantify and simulate land degradation in ecologically fragile areas from the perspective of long-term time series and the spatial structure of watersheds are rare. This paper takes the Huangshui Basin of the Qinghai-Tibet Plateau in China as a case study and proposes a long-time series evolution and scenario simulation method for land use function using the Google Earth Engine platform, which realizes the simulation of land use function and structure in ecologically fragile areas by space–time cube segmentation and integrated forest-based prediction. This allows the analysis of land degradation in terms of food security and ecological service degradation. The results show that: (1) the land use function and structure evolution of the Huangshui watershed from 1990 to 2020 have a significant temporospatial variation. In the midstream region, the construction land expanded 151.84% from 1990 to 2004, driven by urbanization and western development policy; in the middle and downstream region, the loss of farmland was nearly 12.68% from 1995 to 2005 due to the combined influence of the policy of returning farmland to forest and urban expansion. (2) By 2035, the construction land in the watershed will be further expanded by 28.47%, and the expansion intensity will be close to the threshold in the upstream and midstream areas and will continue to increase by 33.53% over 2020 in downstream areas. (3) The evolution of land use function and structure will further induce land degradation, causing a 15.30% loss of farmland and 114.20 km² of occupation of ecologically vulnerable areas, seriously threatening food security and ecological protection. Accordingly, this paper proposes policy suggestions to strengthen the spatial regulation for land degradation areas and the coordination of upstream, midstream, and downstream development. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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17 pages, 3844 KiB

Open AccessArticle

Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models

by Zhizhou Yang, Lei Zou, Jun Xia, Yunfeng Qiao and Diwen Cai

Remote Sens. 2022, 14(7), 1714; https://doi.org/10.3390/rs14071714 - 1 Apr 2022

Cited by 9 | Viewed by 2575

Abstract

Urban water quality is facing strongly adverse degradation in rapidly developing areas. However, there exists a huge challenge to estimating the inner features and predicting the variation of long-term water quality due to the lack of related monitoring data and the complexity of urban water systems. Fortunately, multi-remote sensing data, such as nighttime light and evapotranspiration (ET), provide scientific data support and reasonably reveal the variation mechanisms. Here, we develop an integrated decomposition-reclassification-prediction method for water quality by integrating the CEEMDN method, the RF method mothed, and the genetic algorithm-support vector machine model (GA-SVM). The degression of the long-term water quality was decomposed and reclassified into three different frequency terms, i.e., high-frequency, low-frequency, and trend terms, to reveal the inner mechanism and dynamics in the CEEMDAN method. The RF method was then used to identify the teleconnection and the significance of the selected driving factors. More importantly, the GA-SVM model was designed with two types of model schemes, which were the data-driven model (GA-SVMd) and the integrated CEEMDAN-GA-SVM model (defined as GA-SVMc model), in order to predict urban water quality. Results revealed that the high-frequency terms for NH₃-N and TN had a major contribution to the water quality and were mainly dominated by hydrometeorological factors such as ET, rainfall, and the dynamics of the lake water table. The trend terms revealed that the water quality continuously deteriorated during the study period; the terms were mainly regulated by the land use and land cover (LULC), land metrics, population, and yearly rainfall. The predicting results confirmed that the integrated GA-SVMc model had better performance than single data-driven models (such as the GA-SVM model). Our study supports that the integrated method reveals variation rules in water quality and provides early warning and guidance for reducing the water pollutant concentration. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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22 pages, 55475 KiB

Open AccessArticle

A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN

by Jun Li, Tingting Qin, Chengye Zhang, Huiyu Zheng, Junting Guo, Huizhen Xie, Caiyue Zhang and Yicong Zhang

Remote Sens. 2022, 14(7), 1579; https://doi.org/10.3390/rs14071579 - 24 Mar 2022

Cited by 12 | Viewed by 2893

Abstract

Mining has caused considerable damage to vegetation coverage, especially in grasslands. It is of great significance to investigate the specific contributions of various factors to vegetation cover change. In this study, fractional vegetation coverage (FVC) is used as a proxy indicator for vegetation coverage. We constructed 50 sets of geographically weighted artificial neural network models for FVC and its driving factors in the Shengli Coalfield. Based on the idea of differentiation, we proposed the geographically weighted differential factors-artificial neural network (GWDF-ANN) to quantify the contributions of different driving factors on FVC changes in mining areas. The highlights of the study are as follows: (1) For the 50 models, the average RMSE was 0.052. The lowest RMSE was 0.007, and the highest was 0.112. For the MRE, the average value was 0.007, the lowest was 0.001, and the highest was 0.023. The GWDF-ANN model is suitable for quantifying FVC changes in mining areas. (2) Precipitation and temperature were the main driving factors for FVC change. The contributions were 32.45% for precipitation, 24.80% for temperature, 22.44% for mining, 14.44% for urban expansion, and 5.87% for topography. (3) Over time, the contributions of precipitation and temperature exhibited downward trends, while mining and urban expansion showed positive trajectories. For topography, its contribution remains generally unchanged. (4) As the distance from the mining area increases, the contribution of mining gradually decreases. At 200 m away, the contribution of mining was 26.69%; at 2000 m away, the value drops to 17.8%. (5) Mining has a cumulative effect on vegetation coverage both interannually and spatially. This study provides important support for understanding the mechanism of vegetation coverage change in mining areas. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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31 pages, 8445 KiB

Open AccessArticle

Spatio-Temporal Changes in Ecosystem Service Value and Its Coordinated Development with Economy: A Case Study in Hainan Province, China

by Jie Fu, Qing Zhang, Ping Wang, Li Zhang, Yanqin Tian and Xingrong Li

Remote Sens. 2022, 14(4), 970; https://doi.org/10.3390/rs14040970 - 16 Feb 2022

Cited by 36 | Viewed by 4038

Abstract

Ecosystem service value is crucial to people’s intuitive understanding of ecological protection and the decision making with regard to ecological protection and economic green development. This study improved the benefit transfer method to evaluate ESV in Hainan Province, proposed the coupling analysis method of economic and environmental coordination, and explored the relationship between ESV and economic development based on the medium-resolution remote sensing land use data and socio-economic data from 2000 to 2020. The results show that Hainan Province’s ESV decreased by 33.305 billion CNY from 2000 to 2020. The highest ESV per unit area was found in the water system and forest ecosystem, mainly distributed in the central mountainous area. The overall condition of EEC decreased from a basic coordination state to a moderate disorder state. Areas with high economic development had better EEC, such as Haikou and Sanya. Meanwhile, we analyzed the driving force of ESV and EEC by Geodetector. The results show that land use intensity was the most important driving factor affecting ESV, with a contribution rate of 0.712. Total real estate investment was the most important driving factor affecting EEC, with a contribution rate of 0.679. These results provide important guidance for the coordinated development of regional economy and ecosystem protection. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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18 pages, 807 KiB

Open AccessArticle

Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework

by Xi Chen, Wenzhi Zhao, Jiage Chen, Yang Qu, Dinghui Wu and Xuehong Chen

Remote Sens. 2021, 13(24), 5177; https://doi.org/10.3390/rs13245177 - 20 Dec 2021

Cited by 10 | Viewed by 3363

Abstract

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990

{km}^{2}

) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach

90 %

. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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28 pages, 21457 KiB

Open AccessArticle

Monitoring the Characteristics of Ecological Cumulative Effect Due to Mining Disturbance Utilizing Remote Sensing

by Quansheng Li, Junting Guo, Fei Wang and Ziheng Song

Remote Sens. 2021, 13(24), 5034; https://doi.org/10.3390/rs13245034 - 10 Dec 2021

Cited by 19 | Viewed by 2987

Abstract

This study conducted land cover classification and inversion analysis to estimate land surface temperature, soil moisture, specific humidity, atmospheric water vapor density, and relative humidity using remote sensing and multi-source mining data. Using 1990–2020 data from the Shendong mining area in Inner Mongolia, China, the eco-environmental evolution and the ecological cumulative effects (ECE) of mining operations were characterized and analyzed at a long-term scale. The results show that while the eco-environment was generally stable, mining activities affected the eco-environment at the initial stage (1990–2000) to a certain degree. During the rapid development stage of coal mining, the eco-environment was severely damaged, and the ECE were significant at the temporal scale. The absolute value of the change rate of ecological parameters was increasing. Due to an increased focus on ecological restoration, starting in 2010, the environmental indicators gradually stabilized and the eco-environment improved considerably, ushering in a period of stability for coal mining activities. The absolute value of the change rate of ecological parameters became stable. Analysis of the change in eco-environmental indicators with distance and comparison to the contrast area showed the ECE characteristics from mining disturbance at the spatial scale. This study shows that remote sensing technology can be used to characterize the ECE from mining operations and analyze eco-environmental indicators, providing crucial information in support of ecological protection and restoration, particularly in coal mining areas. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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12 pages, 2649 KiB

Open AccessTechnical Note

Extraction of Water Body Information from Remote Sensing Imagery While Considering Greenness and Wetness Based on Tasseled Cap Transformation

by Chao Chen, Huixin Chen, Jintao Liang, Wenlang Huang, Wenxue Xu, Bin Li and Jianqiang Wang

Remote Sens. 2022, 14(13), 3001; https://doi.org/10.3390/rs14133001 - 23 Jun 2022

Cited by 27 | Viewed by 3430

Abstract

Water, as an important part of ecosystems, is also an important topic in the field of remote sensing. Shadows and dense vegetation negatively affect most traditional methods used to extract water body information from remotely sensed images. As a result, extracting water body information with high precision from a wide range of remote sensing images which contain complex ground-based objects has proved difficult. In the present study, a method used for extracting water body information from remote sensing imagery considers the greenness and wetness of ground-based objects. Ground objects with varied water content and vegetation coverage have different characteristics in their greenness and wetness components obtained by the Tasseled Cap transformation (TCT). Multispectral information can be output as brightness, greenness, and wetness by Tasseled Cap transformation, which is widely used in satellite remote sensing images. Hence, a model used to extract water body information was constructed to weaken the influence of shadows and dense vegetation. Jiangsu and Anhui provinces are located along the Yangtze River, China, and were selected as the research area. The experiment used the wide-field-of-view (WFV) sensor onboard the Gaofen-1 satellite to acquire remotely sensed photos. The results showed that the contours and spatial extent of the water bodies extracted by the proposed method are highly consistent, and the influence of shadow and buildings is minimized; the method has a high Kappa coefficient (0.89), overall accuracy (92.72%), and user accuracy (88.04%). Thus, the method is useful in updating a geographical database of water bodies and in water resource management. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities)

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