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Ecosystem Services with Remote Sensing

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

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 23997

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Prof. Dr. Jiangbo Gao

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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: land use and ecosystem services; karst mountains; ecosystem service tradeoff; soil water retention; ecological restoration; soil conservation
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Special Issue Information

Dear Colleagues,

Ecosystem services refer to the environmental conditions and utility provided and maintained by ecosystems which humans rely on for survival and development. However, The Millennium Ecosystem Assessment Report highlighted that 60% of key ecosystem service items around the world have deteriorated since the late 20th century, and problems related to regulating services have become more severe. At present, in bridging natural and social systems, ecosystem services are a key research topic in ecology and geography and are receiving significant attention from researchers and scientific organizations.

Currently, the main themes in the field of ecosystem service include quantitative assessment, driving mechanisms, and correlation with human wellbeing. It can be seen that all of these themes, to different extents, rely on the application of remote sensing with the significant advantages of monitoring ecological structure and functions at multi-scales. Further, the quantitative calculation of ecosystem services based on remote sensing can provide a scientific basis for enhancing land use optimization and sustainable development.

This Special Issue aims to disseminate and share findings on national or regional ecosystem service assessment and its environmental stresses using remote sensing data, and the coupling of ecosystem services with human wellbeing. Original research articles or review manuscripts are invited in the following related areas:

Ecosystem services and tradeoff/synergy by remote sensing;
Driving mechanism of ecosystem services and the determinants from big scientific data;
Ecosystem services provision and demand based on remote sensing application;
Land use optimization through ecosystem service enhancing.

Dr. Jiangbo Gao
Guest Editor

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

Ecosystem services
Remote sensing
Driving factors and mechanism
Services provision and demand
Land use optimization

Published Papers (7 papers)

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Editorial

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5 pages, 187 KiB

Open AccessEditorial

Editorial for the Special Issue “Ecosystem Services with Remote Sensing”

by Jiangbo Gao

Remote Sens. 2020, 12(14), 2191; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142191 - 09 Jul 2020

Cited by 6 | Viewed by 1890

Abstract

Ecosystem services refer to the environmental conditions and utilities provided and maintained by ecosystems, which are the basis for the survival and development of human society. The studies on ecosystem services in quantitative assessments, driving mechanisms, and correlation with human well-being, based on remote sensing, have increased in recent years. Various applications of remote sensing in ecosystem services are reported in six papers published in this Special Issue. The major research topics covered by this Special Issue include the multi-method analysis (e.g., linear regression, geographical detector, and geographically weighted regression methodology) of the normalized difference vegetation index (NDVI) to reflect ecosystem structure, the dynamic changing process of ecosystem services, and the determinants, which include a new image-analysis method based on a time series of a biophysical variable and the application of fractional vegetation cover (FVC) to analyze the spatiotemporal relationship between ecosystem structure and function and the comprehensive study on ecosystem function and service based on multi-source remote sensing data. The application of remote sensing data to ecosystem services research has the advantage of monitoring ecological structure and functions at multi-scales. Furthermore, the quantitative calculation of ecosystem services, based on remote sensing, can provide a scientific basis for enhancing land use optimization and sustainable development. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

Research

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17 pages, 3169 KiB

Open AccessArticle

Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

by Linghui Guo, Liyuan Zuo, Jiangbo Gao, Yuan Jiang, Yongling Zhang, Shouchen Ma, Youfeng Zou and Shaohong Wu

Remote Sens. 2020, 12(8), 1332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081332 - 23 Apr 2020

Cited by 23 | Viewed by 2947

Abstract

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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20 pages, 2156 KiB

Open AccessArticle

Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China

by Ze Liang, Yueyao Wang, Fuyue Sun, Hong Jiang, Jiao Huang, Jiashu Shen, Feili Wei and Shuangcheng Li

Remote Sens. 2020, 12(8), 1328; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081328 - 22 Apr 2020

Cited by 11 | Viewed by 2840

Abstract

More than 3000 cities in China were used to study the effect of urbanization and local climate variability on urban vegetation across different geographical and urbanization conditions. The national scale estimation shows that China’s urban vegetation depicts a trend of degradation from 2000 to 2015, especially in developed areas such as the Yangtze River Delta. According to the panel models, the increase of precipitation (PREC), solar radiation (SRAD), air temperature (TEMP), and specific humidity (SHUM) all enhance urban vegetation, while nighttime light intensity (NLI), population density (POPDEN), and fractal dimension (FRAC) do the opposite. The effects change along the East–West gradient; the influences of PREC and SHUM become greater, while those of TEMP, SRAD, NLI, AREA, and FRAC become smaller. PREC, SHUM, and SRAD play the most important roles in Northeast, Central, and North China, respectively. The role of FRAC and NLI in East China is much greater than in other regions. POPDEN remains influential across all altitudes, while FRAC affects only low-altitude cities. NLI plays a greater role in larger cities, while FRAC and POPDEN are the opposite. In cities outside of the five major urban agglomerations, PREC has a great influence while the key factors are more diversified inside. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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16 pages, 4024 KiB

Open AccessArticle

Spatially Variable Relationships between Karst Landscape Pattern and Vegetation Activities

by Wenjuan Hou and Jiangbo Gao

Remote Sens. 2020, 12(7), 1134; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071134 - 02 Apr 2020

Cited by 15 | Viewed by 2472

Abstract

Based on the theories of structure–function correlation in Geography, and landscape pattern-ecological function correlation in Landscape Ecology, the correlation between land use fragmentation and vegetation activity was quantified. Effective mesh size (m_eff) was calculated to represent landscape fragmentation for land use, and the normalized difference vegetation index (NDVI) was used to reflect vegetation activity. The geographically weighted regression (GWR) model was applied to explore the spatial non-stationary relationship between m_eff and NDVI in a karst basin of the southwestern China, where environmental factors (i.e., climate, topography, and vegetation) are spatially heterogeneous. The spatial variation and scale dependence of landscape fragmentation and its relationship with vegetation activity, as well as the influence of lithology types and landforms relief, were considered. Firstly, the optimal ‘slide window’ size for landscape fragmentation was determined to be 500 m, and spatial pattern of m_eff displayed clear heterogeneity with a serious degree of fragmentation. Landscape fragmentation was more severe in carbonate areas than non-carbonate areas, reflecting the influence of landforms relief. More serious fragmentation in dolomite areas meant that the impact of human activities on the landscape morphological characteristics was much more significant than that in the limestone areas with steeper slope. Multi-scale analysis was used to verify a neighborhood size of 7 km for GWR in the study area. Negative effects on vegetation activity from landscape structural changes were more significant in limestone areas, which may be due to the more vulnerable ecosystems there. This research can provide scientific guidance for landscape management in karst regions as it considers the multi-scaled and spatially heterogeneous effects of lithology, geomorphology, and human factors on landscape structure and its correlation with vegetation activity. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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18 pages, 1928 KiB

Open AccessArticle

Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Ecological Red Line Areas

by Jiangbo Gao, Yuan Jiang, Huan Wang and Liyuan Zuo

Remote Sens. 2020, 12(3), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030399 - 26 Jan 2020

Cited by 28 | Viewed by 2943

Abstract

Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing’s ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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13 pages, 4052 KiB

Open AccessArticle

Annual Green Water Resources and Vegetation Resilience Indicators: Definitions, Mutual Relationships, and Future Climate Projections

by Matteo Zampieri, Bruna Grizzetti, Michele Meroni, Enrico Scoccimarro, Anton Vrieling, Gustavo Naumann and Andrea Toreti

Remote Sens. 2019, 11(22), 2708; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222708 - 19 Nov 2019

Cited by 13 | Viewed by 5264

Abstract

Satellites offer a privileged view on terrestrial ecosystems and a unique possibility to evaluate their status, their resilience and the reliability of the services they provide. In this study, we introduce two indicators for estimating the resilience of terrestrial ecosystems from the local to the global levels. We use the Normalized Differential Vegetation Index (NDVI) time series to estimate annual vegetation primary production resilience. We use annual precipitation time series to estimate annual green water resource resilience. Resilience estimation is achieved through the annual production resilience indicator, originally developed in agricultural science, which is formally derived from the original ecological definition of resilience i.e., the largest stress that the system can absorb without losing its function. Interestingly, we find coherent relationships between annual green water resource resilience and vegetation primary production resilience over a wide range of world biomes, suggesting that green water resource resilience contributes to determining vegetation primary production resilience. Finally, we estimate the changes of green water resource resilience due to climate change using results from the sixth phase of the Coupled Model Inter-comparison Project (CMIP6) and discuss the potential consequences of global warming for ecosystem service reliability. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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22 pages, 10389 KiB

Open AccessArticle

Multitemporal Remote Sensing Based on an FVC Reference Period Using Sentinel-2 for Monitoring Eichhornia crassipes on a Mediterranean River

by Youssra Ghoussein, Hervé Nicolas, Jacques Haury, Ali Fadel, Pascal Pichelin, Hussein Abou Hamdan and Ghaleb Faour

Remote Sens. 2019, 11(16), 1856; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161856 - 09 Aug 2019

Cited by 20 | Viewed by 4996

Abstract

Invasive aquatic plants are a serious global ecological and socio-economic problem because they can cause local extinction of native species and alter navigation and fishing. Eichhornia crassipes (water hyacinth) is a dangerous invasive floating plant that is widely distributed throughout the world. In Lebanon, it has spread since 2006 in the Al Kabir River. Remote sensing techniques have been widely developed to detect and monitor dynamics and extents of invasive plants such as water hyacinth over large areas. However, they become challenging to use in narrow areas such as the Al Kabir River and we developed a new image-analysis method to extract water hyacinth areas on the river. The method is based on a time series of a biophysical variable obtained from Sentinel-2 images. After defining a reference period between two growing cycles, we used the fractional vegetation cover (FVC) to estimate the water hyacinth surface area in the river. This method makes it possible to monitor water hyacinth development and estimate the total area it colonizes in the river corridor. This method can help ecologists and other stakeholders to map invasive plants in rivers and improve their control. Full article

(This article belongs to the Special Issue Ecosystem Services with Remote Sensing)

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