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Remote Sensing and GIS Technologies for Sustainable Ecosystem Management

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 9197

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Dr. Guiming Zhang

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Guest Editor

Department of Geography & the Environment, University of Denver, Denver, CO 80208, USA
Interests: environmental modeling; volunteered geographic information; geospatial big data; geo-computation; GIScience
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Paul C. Sutton

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Guest Editor

Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA
Interests: sustainability; ecological economics; geospatial science
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Dear Colleagues,

Ecosystem management is defined as “management driven by explicit goals, executed by policies, protocols, and practices, and made adaptable by monitoring and research based on our best understanding of the ecological interactions and processes necessary to sustain ecosystem composition, structure, and function” (Christensen et al., 1996). It is “the application of ecological science to resource management to promote long-term sustainability of ecosystems and the delivery of essential ecosystem goods and services to society” (Chapin, Matson, Mooney, & Vitousek, 2002). As such, sustainability is an intrinsic goal of ecosystem management. Facing challenges brought by global environmental change, sustainable ecosystem management is critical to achieving the United Nations Sustainable Development Goals (SDGs), as ecosystem integrity underlies all 17 SDGs.

Typical operational steps towards sustainable ecosystem management include the following (Brussard, Reed, & Tracy, 1998): (1) delineate the ecosystem to be managed; (2) define strategic management goals; (3) develop a comprehensive understanding of the ecosystem; (4) obtain socioeconomic data; (5) link the socioeconomic and ecological data in an appropriate model; (6) implement experimental management actions; and (7) monitor management results for regularly reviewing and adjusting the management strategy. Remote sensing and geographic information system (GIS) technologies, due to their superior capability of collecting, managing, and analyzing socioeconomic and ecological data and modeling geographic/ecological processes, can provide a great deal of support to implement these steps. In this Special Issue, we invite scholarly contributions related to the use of remote sensing and GIS to support any theoretical and/or practical aspects of sustainable management of all types of ecosystems (urban, wetland, forest, grassland, etc.).

Research articles or reviews submitted to this Special Issue may address, but are not limited to, the following topics:

Ecosystem evaluation;
Ecosystem monitoring;
Ecological modeling;
Ecosystem valuation;
Ecological economics;
Biodiversity and wildlife;
Human–environment interactions;
Ecosystem management policy.

Dr. Guiming Zhang
Prof. Dr. Paul C. Sutton
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

ecosystem assessment
ecosystem services
ecosystem monitoring
ecological modeling
ecological economics
ecosystem valuation
biodiversity and wildlife
human–environment interactions
sustainability science

Published Papers (6 papers)

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Research

26 pages, 20352 KiB

Open AccessArticle

Multi-Temporal Analysis of Environmental Carrying Capacity and Coastline Changes in Yueqing City

by Zitong Pan, Yi Wang and Zhice Fang

Remote Sens. 2023, 15(21), 5170; https://doi.org/10.3390/rs15215170 - 30 Oct 2023

Viewed by 813

Abstract

With the rapid development of coastal cities, environmental problems are becoming increasingly severe. Therefore, it is imminent to assess the environmental carrying capacity (ECC) of coastal cities. We take Yueqing City, China, as the study area and establish an ECC evaluation system. For the objectivity and scientificity of this study, the coefficient of variation-back propagation neural network (CV-BPNN) method is used to determine the weight of the indicators and a multi-temporal evaluation is conducted. This paper also explores the relationship between coastline changes and ECC variations for the first time. The results indicate: (1) The ECC of Yueqing City first decreased and then increased, and the inland ECC is better than the coastal area. The future trend is expected to rise. (2) The coastline is continuously extending seaward, and the natural coastline retention rate gradually declines. (3) The coupling coordination degree between the change in the ECC and the change in the coastline shows a trend of “first fluctuation, then stability, and then decline,” and the ecological environment situation was still challenging. (4) Based on the above results, some suggestions are put forward to strengthen coastal ecological development and promote the sustainable development of coastal cities. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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30 pages, 16099 KiB

Open AccessArticle

Multi-Factor Collaborative Analysis of Conservation Effectiveness of Nature Reserves Based on Remote Sensing Data and Google Earth Engine

by Jin Zhang, Cunyong Ju, Tijiu Cai, Houcai Sheng and Xia Jing

Remote Sens. 2023, 15(18), 4594; https://doi.org/10.3390/rs15184594 - 18 Sep 2023

Cited by 1 | Viewed by 1143

Abstract

Protected areas (PAs) play a crucial role in safeguarding biological resources and preserving ecosystems. However, the lack of standardized and highly operational criteria for evaluating their conservation effectiveness, particularly across different ecological types, remains a significant gap in the literature. This study aims to address this gap by constructing a conservation effectiveness evaluation model for two distinct types of PAs in Heilongjiang Province, China: the Zhalong National Nature Reserve (ZlNNR), a wetland ecological reserve; and the Mudanfeng National Nature Reserve (MdfNNR), a forest ecological reserve. We employed various methods, including land use dynamic index, visual analysis of landscape patterns, remote sensing inversion, and a multi-factor comprehensive assessment model, to assess changes in conservation effectiveness from 2000 to 2020. Our findings reveal a contrast between the two PAs. In the ZlNNR, croplands and water bodies increased significantly by 4069.4 ha (K = 1.5820%) and 2541.58 ha (K = 3.2692%). In the MdfNNR, impervious lands increased greatly by 65.35 ha (K = 7.4021%), whereas forest lands decreased by 125 ha (K = −0.067%). The core area of the two PAs displayed increased landscape regularity, whereas the experimental area showed heightened landscape diversity. In ZlNNR, the MPS_L value increased by 134.91%, whereas the PD_L value decreased by 57.43%, indicating a more regular landscape pattern. In MdfNNR, the SHDI_L value decreased by 110.7%, whereas the PD_L value increased by 52.55%, indicating a more fragmented landscape pattern. The area with improved vegetation trends in ZlNNR was 8.59% larger than in MdfNNR, whereas the area with degraded vegetation trends was 4.86% smaller than in MdfNNR. In all years, the high effectiveness area was larger in ZlNNR than in MdfNNR, whereas the medium and low effectiveness areas were smaller in ZlNNR compared to MdfNNR. This study provides a scientifically rigorous assessment method for evaluating the conservation effectiveness of different types of PAs, laying a solid theoretical foundation and practical guidance for future conservation strategies. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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24 pages, 4828 KiB

Open AccessArticle

Spatial Heterogeneity of Watershed Ecosystem Health and Identification of Its Influencing Factors in a Mountain–Hill–Plain Region, Henan Province, China

by Hejie Wei, Qing Han, Yi Yang, Ling Li and Mengxue Liu

Remote Sens. 2023, 15(15), 3751; https://doi.org/10.3390/rs15153751 - 27 Jul 2023

Cited by 6 | Viewed by 1113

Abstract

A watershed ecosystem is a compound ecosystem composed of land and rivers, and its health is closely related to the sustainable development of the region it is located in. The Yihe River Basin (YRB) in central China’s Henan province, which is located in the north–south transition zone and has a mountain–hill–plain landscape from the upstream to the downstream, is adopted as the research area in this study. A watershed ecosystem health assessment system is constructed based on an ecosystem vigor–organization–resilience–service supply and demand harmony (EVORSH) framework and utilized to assess the ecosystem health in the YRB by taking a 3 km × 3 km grid as the evaluation unit. Thirteen factors are selected from natural and human social factors, and from them, the factors that influence watershed ecosystem health through the generation of spatial heterogeneity are identified using the geographical detector model. The following findings are obtained. (1) The mean value of ecosystem health levels in the YRB is 0.65 and at the good level. The ecosystem health has considerable spatial heterogeneity. The areas with high–high concentration are distributed in the mountains in the upper reaches of the YRB, and the areas with low–low concentration are mainly distributed in the plain areas in the middle reaches of the YRB. (2) The geographical detector result shows that 9 of 13 factors have a considerable impact on the spatial distribution of the YRB’s ecosystem health. The interaction between two factors is enhanced synergically. The decisive power of population density, rainfall, and potential evapotranspiration are more than 0.5, so these three are the main factors that influence the distribution of ecosystem health in the YRB. (3) The EVORSH framework is suitable for the measurement of ecosystem health in the YRB. The evaluation result is consistent with the actual situation in the YRB. A 3 km × 3 km grid is used as the basic research unit, and it can more accurately and scientifically express the spatial heterogeneity of ecosystem health in the YRB compared with the macro evaluation unit. This study can provide a scientific basis for ecological protection and high-quality development planning in the YRB. By integrating multi-dimensional data and methods, the EVORSH framework proposed in this study can quickly and scientifically assess the status of ecosystem health, identify the influencing factors of spatial heterogeneity, and could be applied in other similar watersheds. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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18 pages, 6834 KiB

Open AccessArticle

Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System

by Yuanxu Ma, Dongqi Sun, Weihua Liu, Yongfa You, Siyuan Wang, Zhongchang Sun and Shaohua Wang

Remote Sens. 2022, 14(23), 6119; https://doi.org/10.3390/rs14236119 - 02 Dec 2022

Cited by 1 | Viewed by 1482

Abstract

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m³. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m³ corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m³, which was higher than that in rivers with a range of 2–4 mg/m³. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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19 pages, 9834 KiB

Open AccessArticle

Quantifying the Ecological Effectiveness of Poverty Alleviation Relocation in Karst Areas

by Qing Feng, Zhongfa Zhou, Changli Zhu, Wanlin Luo and Lu Zhang

Remote Sens. 2022, 14(23), 5920; https://doi.org/10.3390/rs14235920 - 23 Nov 2022

Cited by 4 | Viewed by 1479

Abstract

During the 13th Five-Year Plan period (2015–2020), the Poverty alleviation relocation (PAR), led by the Chinese government in the karst regions of southwest China, aimed to cope with poverty and ameliorate the ecological environment. Nevertheless, few research results have involved quantitative assessment of the ecological effectiveness of PAR. Moreover, few studies on the ecological effects of migration relocation have distinguished the effects of relocation on climatic factors and other ecological restoration projects concerning the ecological environment. It remains unclear to what extent PAR affects the regional ecological environment. In order to quantitatively assess the extent of PAR’s ecological restoration contribution, we adopted the Remote Sensing Ecological Index (RSEI) model, which integrates the four more intuitive and critical influencing factors of greenness, moisture, dryness, and heat. On the Google earth engine (GEE) platform, utilizing its powerful remote sensing data storage capacity and computational capability, we quantitatively assessed the spatial and temporal distribution characteristics of ecological environmental quality (EEQ). As revealed by our research findings, overall EEQ showed a fluctuating upward trend over the period 1996–2021 in the study area, exhibiting an improvement of 22.66%. Mann–Kendall mutation test curves showed the most significant improvement occurred after 2015, with an improvement of 8.06%. Based on the residual analysis model, in order to remove the influence of climatic factors and other anthropogenic activities, and to assess the driving effectiveness of PAR, PAR was remarkedly effective in ameliorating EEQ, causing the RSEI to improve by 0.0221–0.0422. The LISA correlation model further analyzed that 44.91% of regional PAR implementation exerted a remarkable influence on RSEI change, of which 54.59% belonged to positive correlation. Aside from that, we also found that not all areas involved in PAR experienced ameliorated RSEI. In the western region, where the human–land conflict is prominent and the ecology is more fragile, PAR exhibited a significant effect in ameliorating EEQ, but in the eastern region, where the EEQ foundation is better, PAR did not show significant effect, and, thus, the ecological restoration effect of PAR exhibited noticeable geographical suitability. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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37 pages, 8511 KiB

Open AccessArticle

Mapping the Spatiotemporal Pattern of Sandy Island Ecosystem Health during the Last Decades Based on Remote Sensing

by Yuan Chi and Dahai Liu

Remote Sens. 2022, 14(20), 5208; https://doi.org/10.3390/rs14205208 - 18 Oct 2022

Cited by 3 | Viewed by 1818

Abstract

Sandy islands suffer multiple external disturbances and thus experience drastic temporal ecological changes, and the two parts, that is, the states of essential components (Part 1) and the resilience under multiple disturbances (Part 2), are both indispensable to the sandy island ecosystem health. In this study, a model for the sandy island ecosystem health was established by integrating the two parts. In Part 1, the states were measured following the framework of vegetation, soil, and landscape, and a total of 12 factors in the three components were adopted. In Part 2, three typical disturbances, namely, geomorphological change, soil salinization, and human influence, were identified, and the resilience across different time intervals was measured by clarifying the intrinsic correlations between the components and disturbances. A sandy island ecosystem health index (SIEHI) was proposed based on the two parts, and Chongming Island was selected as the study area to demonstrate the model. The results indicated that the SIEHI continuously increased from 1988 to 2017, denoting the good effects of “Eco-Island construction” on Chongming Island. In different components and factors, the vegetation and soil components contributed more than the landscape component to the sandy island ecosystem health, and vegetation quality and soil carbon/nitrogen were the factors that made the most contributions. In different disturbances, the human influence played a major role in driving the spatiotemporal variations of the sandy island ecosystem health. Farming and building construction contributed the most and accounted for 37.12% and 35.59% of the total human influence, respectively, while traffic development exerted the highest influence per area. Then, influence coefficients of different human activities on the sandy island were determined, and measures for different functional zones were proposed for balancing the protection and development and achieving the sandy island ecosystem-based management. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technologies for Sustainable Ecosystem Management)

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