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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 30163

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Special Issue Editors

Dr. Lingling Liu

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Guest Editor

Institute on the Environment, University of Minnesota, Saint Paul, MN 55108, USA
Interests: global environmental change; ecosystem service; ecosystem sustainability; land surface phenology; machine learning

Dr. Eric Lonsdorf

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Guest Editor

Institute on the Environment, University of Minnesota, SAINT PAUL, MN 55108, USA
Interests: ecological models; natural resource management; ecosystem services

Dr. Amanda Armstrong

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Guest Editor

1. Goddard Earth Sciences, Technology and Research (GESTAR I), University Space Research Association (USRA), Greenbelt, MD 21046, USA
2. Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA
Interests: ecological modeling; environmental change; remote sensing

Special Issue Information

Dear Colleagues,

As governments, business, and lending institutions are increasingly recognizing benefits from nature (a.k.a. ecosystem services), which have been progressively accounted for in the assessments of agricultural management, conservation prioritization, and sustainable development, obtaining accurate and accessible information on ecosystem services has become more important than ever. However, most ecosystem service models and decision support tools are using categorical representation of land-use and land-cover (LULC, a.k.a. traditional ecosystem services), with the assumption that all the habitats within each LULC type are identical. Using these methods poses challenges for both the accuracy and accessibility of the information and also a long delay (a year or more) between satellite monitoring and the development of land use classification. Applying state-of-the-art remote-sensing methodologies provides a variety of datasets across spatial and temporal extents and resolutions, and has tremendous potential to replace LULC-based biophysical tables with spatial and temporal continuous vegetation, climate, and topography features at a pixel level. Remote sensing, when paired with process models, could provide nearly real-time monitoring of the ecosystem processes and services provided rather than annual updates that are years behind.

The objective of this Special Issue is to provide a forum for new research and review articles on recent advances in integrating earth observations (EO) into ecosystem services (ES). We are looking for examples of new processes, datasets, improved classifications, and data-model integration methods that will help to improve ecosystem service model performance and decision-support tools. Contributions to this SI may focus on (but might not be limited to) the following major topics that use novel and Earth observation (EO)-based applications:

Improving the characterization of ecosystem biodiversity variables;
Improving decision-support tools that use ecosystem service models;
Modeling the temporal dynamics of ecosystem services;
Improving, validating, and calibrating ecosystem models;
Comparing the accuracy between traditional ES models and EO-based models;
Characterizing the influence of land-use and land-cover change on ecosystem-service supply and beneficiaries;
Evaluating human livelihoods impacted by climate change through ecosystem services (e.g., fires, flooding, temperature, precipitation, carbon, methane, etc.);
Characterizing changes in ecosystem-service supply and demand over time and space;
Mapping opportunities for ecosystem-service-based management (e.g., roadsides, field borders, transmission lines, and non-crop areas).

Dr. Lingling Liu
Dr. Eric Lonsdorf
Dr. Amanda Armstrong
Guest Editors

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Keywords

earth observations
ecosystem services
decision tools
land cover and land use
climate change

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Research

30 pages, 19732 KiB

Open AccessArticle

Analysis of Water Conservation Trends and Drivers in an Alpine Region: A Case Study of the Qilian Mountains

by Junyu Sun, Chenrui Ni and Mengmeng Wang

Remote Sens. 2023, 15(18), 4611; https://doi.org/10.3390/rs15184611 - 20 Sep 2023

Cited by 1 | Viewed by 4474

Abstract

The water conservation service of an ecosystem reflects the sustainability of regional water resources and is significant to human survival and sustainable development. However, global climate warming and intensified human activities pose substantial challenges to regional water conservation services, especially in an alpine region with a fragile ecological environment, which is more sensitive to climate factors and land use pattern changes. In this study, the Qilian Mountains (QLM) region was chosen as the study area to investigate water conservation trends and drivers in an alpine region. The InVEST model was used to estimate water conservation in the QLM from 2000 to 2020. In addition, the characteristics of the spatiotemporal variation in the water conservation were analyzed using a combination of the Theil–Sen median trend and Mann–Kendall method, coefficient of variation, and Hurst exponent, and the main driving factors affecting these changes were determined using partial correlation analysis and contribution analysis. The main conclusions are as follows: (1) The predicted water conservation in the QLM based on the InVEST model’s water yield module had a relative inaccuracy of 5.96%, and the mean water conservation in the QLM from 2000 to 2020 was approximately 78.08 × 10⁸ m³. (2) The water conservation showed a slight increase over the whole QLM region, with a change rate of 0.565 mm/a; yet, it showed a distinct spatial distribution pattern characterized by “more in the east than in the west”. (3) The contribution of the various land use categories to the total water conservation, from highest to lowest, was according to the following: grassland (62.44%) > unutilized land (15.99%) > forest (11.44%) > cultivated land (9.86%) > construction land (0.45%) > water (0.03%). (4) Precipitation exhibited a significant positive correlation, with contribution ratios of approximately 58.50% to the variation in the water content, whereas potential evapotranspiration and surface temperature showed a nonsignificant negative correlation with contribution ratios of approximately 2.17% and 2.08%, respectively. The results can provide scientific reference for ecological protection in the QLM and other similar alpine environment areas. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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17 pages, 8426 KiB

Open AccessArticle

Influences of Ecological Restoration Programs on Ecosystem Services in Sandy Areas, Northern China

by Shixian Xu, Yuan Su, Wei Yan, Yuan Liu, Yonghui Wang, Jiaxin Li, Kaixuan Qian, Xiuyun Yang and Xiaofei Ma

Remote Sens. 2023, 15(14), 3519; https://doi.org/10.3390/rs15143519 - 12 Jul 2023

Cited by 7 | Viewed by 1897

Abstract

Ecosystem services (ESs) are important for supporting human development. However, a changing climate and anthropogenic impacts are resulting in the degradation of dryland ecosystems to varying degrees. While there has been the global implementation of Ecological Restoration Programs (ERPs) to restore degraded ecosystems, there remains limited comprehensive assessment of their impacts on ESs of drylands. In this study, the sandy areas of northern China were used as the study area. The RUSLE, RWEQ, CASA, and InVEST models were used to simulate four major ESs: soil conservation (SC), sand fixation (SF), carbon sequestration (CS), and water yield (WY). The study aimed to evaluate the influences of various ERPs on major ESs. The dominant factors affecting the overall benefits provided by ESs were also identified. Since ERPs were implemented, forest areas have increased by 2.8 × 10⁴ km², whereas the areas of cropland, shrubland, and grassland have decreased. There were generally increasing trends in SF, SC, and CS, whereas there was a decreasing trend in WY. We then used a scenario-based simulation approach to eliminate the influence of climate variability on ESs. The results showed increasing trends in SF, SC, and CS, whereas there were minimal changes in WY. The results suggested that although ERPs can significantly increase regional ESs, unregulated expansion in vegetation can result in a water crisis and affect regional water security. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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27 pages, 58133 KiB

Open AccessArticle

Spatiotemporal Analysis and Multi-Scenario Prediction of Ecosystem Services Based on Land Use/Cover Change in a Mountain-Watershed Region, China

by Jingyi Liu, Yong Zhou, Li Wang, Qian Zuo, Qing Li and Nan He

Remote Sens. 2023, 15(11), 2759; https://doi.org/10.3390/rs15112759 - 25 May 2023

Cited by 5 | Viewed by 2116

Abstract

Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. The impact of LUCC on ESs in the Qingjiang Watershed (QJW), an ecologically sensitive area, and LUCC’s role in future ESs under different land use scenarios are crucial to promoting ecological conservation and land use management. This paper assessed water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) using the InVEST model, and their responses to LUCC in the QJW from 1990 to 2018 using the geodetector and multiscale geographically weighted regression. We predicted land use patterns using the Logistic–CA–Markov model and their effects on ESs in 2034 under business as usual (BAU), ecological land protection (ELP), arable land protection (ALP) and ecological economic construction (EEC) scenarios. From 1990 to 2018, the area of cropland and woodland decreased by 28.3 and 138.17 km², respectively, while the built-up land increased by 96.65 km². The WY increased by 18.92%, while the SC, CS and HQ decreased by 26.94%, 1.05% and 0.4%, respectively. The increase in the arable land area led to a increase in WY, and the decrease in forest land and the increase in construction land led to a decrease in SC, CS and HQ. In addition to being influenced by land use patterns, WY and SC were influenced mainly by meteorological and topographical factors, respectively. In 2034, there was an obvious spatial growth conflict between cropland and construction land, especially in the area centered on Lichuan, Enshi and Yidu counties. Under four scenarios, WY and SC were ranked ALP > BAU > EEC > ELP, while CS and HQ were ranked ELP > EEC > BAU > ALP. Considering the sustainable eco-socio-economic development of the QJW, the EEC scenario can be chosen as a future development plan. These results can indicate how to rationally improve the supply of watershed ESs through land resource allocation, promoting sustainable regional development in mountainous watershed areas. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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24 pages, 25319 KiB

Open AccessArticle

Sensitivity Assessment of Land Desertification in China Based on Multi-Source Remote Sensing

by Yu Ren, Xiangjun Liu, Bo Zhang and Xidong Chen

Remote Sens. 2023, 15(10), 2674; https://doi.org/10.3390/rs15102674 - 21 May 2023

Cited by 5 | Viewed by 2665

Abstract

Desertification, a current serious global environmental problem, has caused ecosystems and the environment to degrade. The total area of desertified land is about 1.72 million km² in China, which is extensively affected by desertification. Estimating land desertification risks is the top priority for the sustainable development of arid and semi-arid lands in China. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the sensitivity of land desertification in China. Based on multi-source remote sensing data, this study integrated natural and human factors, calculated the land desertification sensitivity index by overlaying four indicators (soil quality, vegetation quality, climate quality, and management quality), and explored the driving forces of desertification using a principal component and correlation analysis. It was found that the spatial distribution of desertification sensitivity areas in China shows a distribution pattern of gradually decreasing from northwest to southeast, and the areas with very high and high desertification sensitivities were about 620,629 km² and 2,384,410 km², respectively, which accounts for about 31.84% of the total area of the country. The very high and high desertification sensitivity areas were mainly concentrated in the desert region of northwest China. The principal component and correlation analysis of the sub-indicators in the MEDALUS model indicated that erosion protection, drought resistance, and land use were the main drivers of desertification in China. Furthermore, the aridity index, soil pH, plant coverage, soil texture, precipitation, soil depth, and evapotranspiration were the secondary drivers of desertification in China. Moreover, the desertification sensitivity caused by drought resistance, erosion protection, and land use was higher in the North China Plain region and Guanzhong Basin. The results of the quantitative analysis of the driving forces of desertification based on mathematical statistical methods in this study provide a reference for a comprehensive strategy to combat desertification in China and offer new ideas for the assessment of desertification sensitivity at macroscopic scales. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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22 pages, 23897 KiB

Open AccessArticle

A Novel Mine-Specific Eco-Environment Index (MSEEI) for Mine Ecological Environment Monitoring Using Landsat Imagery

by Peipei Zhang, Xidong Chen, Yu Ren, Siqi Lu, Dongwei Song and Yingle Wang

Remote Sens. 2023, 15(4), 933; https://doi.org/10.3390/rs15040933 - 8 Feb 2023

Cited by 9 | Viewed by 3362

Abstract

The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p < 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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18 pages, 6486 KiB

Open AccessArticle

Spatiotemporal Evolution and Influencing Mechanisms of Ecosystem Service Value in the Tarim River Basin, Northwest China

by Shuai Zhang, Yin Wang, Yang Wang, Zhi Li and Yifeng Hou

Remote Sens. 2023, 15(3), 591; https://doi.org/10.3390/rs15030591 - 18 Jan 2023

Cited by 21 | Viewed by 2326

Abstract

The Tarim River Basin (TRB) is situated in the hinterland of northwest China, which is an extremely arid and fragile ecological zone. In recent years, the region’s ecological civilization construction has been facing huge challenges that are exacerbated by climate change and human activities. In order to verify the current ecological status of TRB, this paper explores the spatial and temporal variation in ecosystem service value (ESV) and the impact mechanism based on LUCC data from 2000 to 2020, using the adjusted unit area value equivalent method, the elasticity index method and the geo-probe analysis method. The results show that: (1) the ESV of the TRB has fluctuated since 2000, increasing by CNY 14.02 billion, especially in the Hotan River region. Among the individual ecosystem services, the increase in regulatory services is the largest, rising to CNY 8.842 billion. The growth of ESV mostly occurred in the mountains and oases. (2) The rise in ESV is mainly due to the conversion of barren land to water and grassland; ESV loss is mainly affected by the conversion of water to cropland and barren land and grassland to cropland and barren land. (3) Human activity impact or intensity (HAI) is the key driving factor for the spatial stratified heterogeneity of ESV, followed by elevation (DEM). In the interaction analysis, HAI∩DEM interaction is the primary reason for ESV’s spatial differentiation. The study’s findings show that the combined effects of human activities, DEM, and hydrothermal conditions underlie the spatial stratified heterogeneity of ESV in the TRB. This conclusion provides a scientific basis for future ecological civilization construction planning. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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23 pages, 47130 KiB

Open AccessArticle

Relationship between Topological Structure and Ecosystem Services of Forest Grass Ecospatial Network in China

by Linzhe Yang, Teng Niu, Qiang Yu, Xiao Zhang and Heng Wu

Remote Sens. 2022, 14(19), 4700; https://doi.org/10.3390/rs14194700 - 21 Sep 2022

Cited by 11 | Viewed by 2237

Abstract

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China’s ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R² = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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20 pages, 5269 KiB

Open AccessArticle

Spatiotemporal Dynamics of Ecological Condition in Qinghai-Tibet Plateau Based on Remotely Sensed Ecological Index

by Jiaxi Cao, Entao Wu, Shuhong Wu, Rong Fan, Lei Xu, Ke Ning, Ying Li, Ri Lu, Xixi Xu, Jian Zhang, Junliu Yang, Le Yang and Guangchun Lei

Remote Sens. 2022, 14(17), 4234; https://doi.org/10.3390/rs14174234 - 27 Aug 2022

Cited by 27 | Viewed by 2635

Abstract

The eco-system in the Qinghai-Tibet Plateau (QTP) is extremely fragile, and highly vulnerable to climate change. Knowledge of the changes in the ecological conditions is vital to mitigate the impact of climate change. In this study, we investigated the trend of ecological conditions of the QTP using the remotely sensed ecological index (RSEI), which is the first PCA (principal component analysis) axis of the four indexes derived from the MODIS (Moderate resolution Imaging Spectroradiometer) images captured in the growing season of 2000–2020. The four indexes, i.e., NDVI (normalized difference vegetation index), heat (land surface temperature, LST), wetness (tasseled cap wetness index, WET) and dryness (normalized difference impervious surface index, NDBSI), were calculated on the Google Earth Engine platform. Using land use cover change (LUCC) data, long-term meteorological records and the supplementary annual livestock production, we explored the drivers of spatiotemporal changes in the RSEI. The results show the following points: (1) the ecological conditions of the QTP have remarkable spatiotemporal variations. There were two ecological degradation periods, one of them occurred in the central region during 2005–2010, mainly attributed to the rising temperatures and decreasing precipitation. The other occurred during 2015–2020, driven primarily by overgrazing in the southwest. From 2000 to 2005, it was a period of rapid ecological restoration in the ecologically fragile northeast region. (2) The contribution rate of pc1 was stable at 60%, while the contribution rate of pc2 remained below 40%, indicating that pc1 demonstrated most of the characteristics of the four indexes. The correlation coefficients between NDVI and WET with pc1 are both positive, while LST and NDBSI have negative correlation coefficients, i.e., negative effects. This is consistent with the actual situation. (3) Overgrazing caused grass degradation in the southwest area of the QTP, which might be the main reason for the poor ecological conditions (i.e., low RSEI value) during 2015–2020. (4) Temperature and precipitation showed an increasing trend during the study period. A warmer and wetter climate is expected to have profound impacts on the ecosystems in QTP and practices should be concentrated on identifying climate-sensitive ecosystem components and designating adaptative options. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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17 pages, 8567 KiB

Open AccessArticle

Soil Salinity Variations and Associated Implications for Agriculture and Land Resources Development Using Remote Sensing Datasets in Central Asia

by Simon Measho, Fadong Li, Petri Pellikka, Chao Tian, Hubert Hirwa, Ning Xu, Yunfeng Qiao, Sayidjakhon Khasanov, Rashid Kulmatov and Gang Chen

Remote Sens. 2022, 14(10), 2501; https://doi.org/10.3390/rs14102501 - 23 May 2022

Cited by 12 | Viewed by 4189

Abstract

Global agricultural lands are becoming saline because of human activities that have affected crop production and food security worldwide. In this study, the spatiotemporal variability of soil electrical conductivity (EC) in Central Asia was evaluated based on high-resolution multi-year predicted soil EC data, Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product, precipitation, reference evapotranspiration, population count, and soil moisture datasets. We primarily detected pixel-based soil EC trends over the past three decades and correlated soil EC with potential deriving factors. The results showed an overall increase in salt-affected areas between 1990 and 2018 for different land cover types. The soil EC trend increased by 6.86% (p < 0.05) over Central Asia during 1990–2018. The open shrub lands dominated by woody perennials experienced the highest increasing soil salinity trend, particularly in Uzbekistan and Turkmenistan local areas, while there was a decreasing soil EC trend in the cropland areas, such as in Bukhara and Khorezm (Uzbekistan). The main factors that affect the variability of soil salinity were strongly associated with population pressure and evapotranspiration. This study provides comprehensive soil EC variations and trends from the local to regional scales. Agriculture and land resource managers must tackle the rising land degradation concerns caused by the changing climate in arid lands and utilise geoinformatics. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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18 pages, 5086 KiB

Open AccessArticle

Construction of an Ecological Security Pattern in an Urban–Lake Symbiosis Area: A Case Study of Hefei Metropolitan Area

by Xin Fan, Yuejing Rong, Chongxin Tian, Shengya Ou, Jiangfeng Li, Hong Shi, Yi Qin, Jiawen He and Chunbo Huang

Remote Sens. 2022, 14(10), 2498; https://doi.org/10.3390/rs14102498 - 23 May 2022

Cited by 16 | Viewed by 2745

Abstract

In the context of rapid urbanization, building an ecological security pattern that takes into account both ecological protection and economic growth is of great significance for guiding high-quality regional development. Taking the Hefei metropolitan area as an example, we identified the ecological source from three aspects—the importance of ecosystem services, ecological sensitivity, and landscape connectivity—by using NPP-VIIRS night light data, impervious surfaces, and the topographical index to the rest of the landscape resistance surface, and the least cumulative resistance model to identify ecological corridors and ecological buffer zones. We then constructed a comprehensive regional ecological security pattern. The results show the following: (1) The ecological source area of the Hefei metropolitan area is 15,538.74 km², accounting for 24.5% of the total study area. It is mainly composed of the Dabie Mountains, the Yangtze River, the Huai River, and Chaohu Lake. (2) The area of an ecological buffer zone, ecological transition zone, and development and construction zone account for 21.8%, 39.7%, and 38.5%, respectively. Among them, the ecological buffer zone serves as a protective barrier for the ecological source area; therefore, development and construction activities should be restricted. The ecological transition zone should be constructed with low development intensity, and the development and construction zone can be carried out with greater development intensity. (3) The total length of the ecological corridor is 2816.89 km, with the mainland of the corridor being cultivated land. Identified by superposition of the land use, the area of conflict of urban expansion is 305.23 km², mainly distributed along the Yangtze River and around Chao Lake. The results may provide decision support for the construction of ecological security in the study area. Full article

(This article belongs to the Special Issue Integrating Earth Observations into Ecosystem Service Models)

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