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Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems

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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: 31 August 2024 | Viewed by 15086

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Special Issue Editors

Prof. Dr. Jiahua Zhang

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Guest Editor

Chinese Academy of Sciences (CAS), Beijing 100094, China
Interests: remote sensing of ecosystems; carbon and water cycle modelling; ecological investigation; land-use and -cover changes; vegetation dynamic; climate change and natural disasters
Special Issues, Collections and Topics in MDPI journals

Dr. Alex Okiemute Onojeghuo

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Guest Editor

JOLEXY Environmental Services Limited, Edmonton, AB, Canada
Interests: environmental remote sensing and GIS application; land-use and land-cover changes; landscape dynamics and ecosystem properties; wetland mapping

Prof. Dr. Fengmei Yao

E-Mail Website
Guest Editor

University of Chinese Academy of Sciences (UCAS), Huairou District, Beijing 101408, China
Interests: impact of climate change on the ecosystem; ecological disaster monitoring based on remote sensing and GIS; ecological carbon and water cycle modeling

Special Issue Information

Dear Colleagues,

The ecological environment is an important source of support for sustainable development and one of the leading global development drivers. Therefore, it is particularly critical to maintaining the global ecological balance. In recent years, the development of remote sensing and GIS technology has provided strong support for the study of ecological evolution and degradation, the long-term monitoring of its environmental effects, and fine analysis of environmental change behavior.

We are pleased to publish the Special Issue “Application of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems”. This Special Issue aims to integrate multi-scale remote sensing and GIS technologies to monitor the quality of the ecological environment at different spatial and temporal scales and further protect terrestrial ecology. Topics include, but are not limited to, terrestrial ecosystem services (carbon, water cycles, biochemical observations, climate change, drought, fire, heatwave, flooding, etc.) as well as spatial scales (environmental and ecological dynamics at different spatial scales) and time scales (ecological evolution from the paleoenvironment to the present).

Prof. Dr. Jiahua Zhang
Dr. Alex Okiemute Onojeghuo
Prof. Dr. Fengmei Yao
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

multi-scale remote sensing and GIS
terrestrial ecosystems
environmental and ecological dynamics
terrestrial ecosystem services
ecological evolution and degradation
carbon and water cycles
climate change and natural disasters
land-use change and human activities
sustainable development and ecological rehabilitation

Published Papers (10 papers)

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Research

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23 pages, 17463 KiB

Open AccessArticle

Identification of Ecological Sources Using Ecosystem Service Value and Vegetation Productivity Indicators: A Case Study of the Three-River Headwaters Region, Qinghai–Tibetan Plateau, China

by Xinyi Feng, Huiping Huang, Yingqi Wang, Yichen Tian and Liping Li

Remote Sens. 2024, 16(7), 1258; https://doi.org/10.3390/rs16071258 - 2 Apr 2024

Viewed by 594

Abstract

As a crucial component of the ecological security pattern, ecological source (ES) plays a vital role in providing ecosystem service value (ESV) and conserving biodiversity. Previous studies have mostly considered ES only from either landscape change pattern or ecological function perspectives, and have ignored their integration and spatio-temporal evolutionary modeling. In this study, we proposed a multi-perspective framework for the spatio-temporal characteristics of ES by ESV incorporating landscape aesthetics, carbon sink characteristics, ecological quality, and kernel NDVI (kNDVI). By integrating the revised ESV and the kernel normalized difference vegetation index as a foundation, we employed the spatial priority model to identify ES. This improvement aims to yield a more practical and specific ESV result. Applying this framework to the Three-River Headwaters Region (TRHR), a significant spatio-temporal change in ecological sources has been observed from 2000 to 2020. This performance provided a reference for ecological conservation in the TRHR. The results indicate that this ecological source identification framework has reliable accuracy and efficiency compared with the existing NRs in the TRHR. This method could reveal more precise spatio-temporal distributions of ES, enhancing ecosystem integrity and providing technical modeling support for developing cross-scale spatial planning and management strategies for nature reserve boundaries. The framework proposed in our research could serve as a reference for building ecological networks in other ecologically fragile areas. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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24 pages, 8273 KiB

Open AccessArticle

Spatiotemporal Analysis of the Interactions between Ecosystem Services in Arid Areas and Their Responses to Urbanization and Various Driving Factors

by Ziyun Qiu, Yunlan Guan, Kefa Zhou, Yanfei Kou, Xiaozhen Zhou and Qing Zhang

Remote Sens. 2024, 16(3), 520; https://doi.org/10.3390/rs16030520 - 29 Jan 2024

Cited by 1 | Viewed by 910

Abstract

In recent years, rapid urban expansion and increasing ecological sensitivity in arid zones have led to extreme imbalances in ecosystem development. Therefore, there is an urgent need to balance the dual goals of synergistic development of ecosystem services (ESs) and increased urbanization. Previous studies have analyzed the impacts of urbanization on ESs but have selected a limited number of indicators and have not focused on the impacts of urbanization on ES pair interactions. In this study, six key ESs (water yield, habitat quality, soil conservation, carbon storage, carbon sequestration and oxygen production, and food production) and total ecosystem services (TESs) were selected, and trends in the temporal and spatial relationship between trade-offs and synergies were analyzed over 20 years. This study refined the living standards urbanization indicator and evaluated the impact of urbanization and multiple drivers on ESs and ES pair interrelationships based on geo-detectors and segmented linear regression. The results show that there is heterogeneity in the overall and regional ES trade-offs and synergistic relationships, and water yield (WY)-related ES pairs generally exhibit synergistic relationships at the overall level. Spatially, however, the trade-off ratio exceeds the synergy ratio. Segmented linear regression results show that the relationship between all the urbanization indicators and TESs demonstrates an upward trend followed by a downward trend. Measures such as the increase in man-made oases in the early stages of urbanization did have some positive effects on TESs. However, as urbanization increased, these positive effects were quickly offset by the negative effects of overdevelopment and environmental degradation, leading to an overall decline in TESs. Urbanization of construction land (CL) had the most direct impact on ecosystem services. In summary, due to special climatic constraints, arid zones are more sensitive than other ecosystems, and urban development is strictly limited by oasis capacity. As cities expand, attention needs to be focused on protecting ecological land and limiting the expansion of CL to promote the synergistic development of urbanization and ecosystem services in arid zones. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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26 pages, 25233 KiB

Open AccessArticle

Quantitative Assessment of Factors Influencing the Spatiotemporal Variation in Carbon Dioxide Fluxes Simulated by Multi-Source Remote Sensing Data in Tropical Vegetation

by Ruize Xu, Jiahua Zhang, Jingwen Wang, Fengmei Yao and Sha Zhang

Remote Sens. 2023, 15(24), 5677; https://doi.org/10.3390/rs15245677 - 8 Dec 2023

Viewed by 933

Abstract

Vegetation plays a vital role in the global carbon cycle, a function of particular significance in regulating carbon dioxide fluxes within tropical ecosystems. Therefore, it is crucial to enhance the precision of carbon dioxide flux estimates for tropical vegetation and to explore the determinants influencing carbon sequestration. In this study, Landsat series images and Sentinel-2 Multispectral Instrument satellite data were used to invert vegetation biophysical parameters, thereby improving the timeliness and resolution of state variables from the boreal ecosystem productivity simulator (BEPS). The BEPS model at a 30 m resolution was developed to accurately capture tropical vegetation carbon dioxide fluxes across Hainan Island (HN) over the preceding two decades. The impacts of climate variations and anthropogenic activities on the carbon dioxide fluxes of tropical vegetation were further quantified using quantile regression models and a land-use transfer matrix. Results indicate significant increases in both net primary productivity (NPP) and net ecosystem productivity (NEP) in HN during the period 2000–2020, by 5.81 and 4.29 g C/m² year, respectively. Spatial trends in vegetation carbon dioxide fluxes exhibited a consistent decline from inland regions to coastal zones. Anthropogenic activities were the dominant factor in the reduced stability of coastal NPP, while the post-2005 vegetation restoration promoted the southward expansion of high NPP (>1200 g C/m²) in the central part of HN. NPP in this tropical island was more sensitive to temperature than to precipitation, with a 1 °C temperature increase resulting in 4.1 g C/m² reduction in dry-season NPP compared to wet-season NPP. Upgrades of cropland quality and grassland restoration have improved NPP yields, and land use transfers have resulted in a 0.301 Tg C net increase in NPP. This study provides new insight into the improvement of the carbon dioxide flux model at a finer scale for tropical vegetation and highlights ecological construction as an adaptation strategy to enhance the carbon sinks of tropical vegetation under negative climate change conditions. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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17 pages, 3851 KiB

Open AccessArticle

Snow Avalanche Hazard Mapping Using a GIS-Based AHP Approach: A Case of Glaciers in Northern Pakistan from 2012 to 2022

by Afia Rafique, Muhammad Y. S. Dasti, Barkat Ullah, Fuad A. Awwad, Emad A. A. Ismail and Zulfiqar Ahmad Saqib

Remote Sens. 2023, 15(22), 5375; https://doi.org/10.3390/rs15225375 - 16 Nov 2023

Viewed by 1294

Abstract

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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21 pages, 4882 KiB

Open AccessArticle

Assessment of the Declining Degree of Farmland Shelterbelts in a Desert Oasis Based on LiDAR and Hyperspectral Imagery

by Chengwei Luo, Yuli Yang, Zhiming Xin, Junran Li, Xiaoxiao Jia, Guangpeng Fan, Junying Zhu, Jindui Song, Zhou Wang and Huijie Xiao

Remote Sens. 2023, 15(18), 4508; https://doi.org/10.3390/rs15184508 - 13 Sep 2023

Viewed by 1181

Abstract

The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740–950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS–AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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19 pages, 9953 KiB

Open AccessArticle

Decoupling Effect and Driving Factors of Land-Use Carbon Emissions in the Yellow River Basin Using Remote Sensing Data

by Xiaolei Wang, Xue Zhao, Shiru Zhang, Shouhai Shi and Xiang Zhang

Remote Sens. 2023, 15(18), 4446; https://doi.org/10.3390/rs15184446 - 9 Sep 2023

Viewed by 1116

Abstract

Land-use change is a crucial element influencing the patterns of carbon sinks/sources in the Yellow River Basin (YRB). Therefore, studying land-use carbon emissions (LUCE) in the YRB and the decoupling from economic development can help formulate emission reduction strategies. In order to explore the spatiotemporal characteristics of LUCE in the YRB, we estimated the LUCE in 69 cities in the YRB using the downscale energy balance table estimation method and land-use remote sensing data for seven phases from 1990 to 2020. The spatial and temporal features of LUCE were researched from three different spatial scales: the whole spatial scale of the YRB, the sub-basin level, and the city level. Furthermore, the Tapio decoupling model was utilized to research the decoupling state between LUCE and economic development using a multi-scale approach. The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the influencing factors of LUCE in the YRB. These results showed the following: (1) The LUCE in the YRB went through two stages: “stable growth” (1990–2000) and “rapid growth” (2000–2020). The LUCE increased from 165 million tons in 1990 to 1.414 billion tons in 2020, and the average annual growth rate was 25.12%. The spatial pattern of LUCE in the YRB exhibited significant variations, with the LUCE showing a geographic differentiation of midstream > downstream > upstream. (2) Except for the expansive coupling state during 2000–2005 (e: 0.952) and the expansive negative decoupling state during 2015–2020 (e: 2.151), the YRB was in the weak decoupling state for the majority of the time periods. (3) Economic development was the major positive driving factor for the rise of LUCE in this basin, while energy consumption intensity was the primary inhibiting factor. Through a discussion of the features and influencing factors of LUCE, these results can be utilized to provide carbon emission reduction recommendations tailored to the characteristics of cities’ resources and economic development, which will be helpful for achieving low-carbon and sustainable development in the YRB. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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22 pages, 11883 KiB

Open AccessCommunication

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

by Daqian Kong, Dekun Yuan, Haojie Li, Jiahua Zhang, Shanshan Yang, Yue Li, Yun Bai and Sha Zhang

Remote Sens. 2023, 15(8), 2086; https://doi.org/10.3390/rs15082086 - 15 Apr 2023

Cited by 1 | Viewed by 1794

Abstract

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUE_max (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R² = 0.86 and RMSE = 1.79 gC m⁻² d⁻¹ on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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17 pages, 7660 KiB

Open AccessArticle

Improved Understanding of Flash Drought from a Comparative Analysis of Drought with Different Intensification Rates

by Jiaqi Han, Jiahua Zhang, Shanshan Yang and Ayalkibet M. Seka

Remote Sens. 2023, 15(8), 2049; https://doi.org/10.3390/rs15082049 - 12 Apr 2023

Cited by 3 | Viewed by 2127

Abstract

The rapid intensification of drought, commonly known as flash drought, has recently drawn widespread attention from researchers. However, how the characteristics and drivers, as well as the ecological impacts of rapidly intensified droughts, differ from those of slowly intensified ones still remains unclear over the globe. To this end, we defined three types of droughts based on the root zone soil moisture (RZSM) decline rates, flash droughts, general droughts, and creep droughts, and then implemented a comparative analysis between them across the globe and the 26 Intergovernmental Panel on Climate Change Special Report on Extremes (IPCC-SREX) regions. The ensemble of RZSM from multiple reanalysis datasets was used to reduce the uncertainties. According to the frequency analysis, our findings suggest that flash droughts contributed to the majority of drought events during 1980–2019, indicating the prevalence of rapid transition from an energy-limited to a water-limited condition in most of the regions. The comparative results of vegetation responses show that flash droughts are more likely to happen in the growing season, leading to faster but relatively minor vegetation deterioration compared to the slowly intensified ones. By analyzing the precipitation and temperature anomalies in the month of drought onset, we found the role of temperature (precipitation) on drought intensification can be generalized as the warmer (drier) the climate is or the faster the drought intensifies, but the main driving forces vary by region. Unlike temperature dominating in midwestern Eurasia and northern high latitudes, precipitation plays a prominent role in the monsoon regions. However, the temperature is expected to be the decisive driver in the warming future, given its monotonically increased contribution over the past four decades. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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20 pages, 7502 KiB

Open AccessArticle

Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery

by Alex Okiemute Onojeghuo, Yuxin Miao and George Alan Blackburn

Remote Sens. 2023, 15(6), 1517; https://doi.org/10.3390/rs15061517 - 9 Mar 2023

Cited by 9 | Viewed by 2844

Abstract

Rice is a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and temporal resolution satellite imagery and efficient classification techniques. This study used similar phenological data from Sentinel-2 (S2) optical and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellite imagery to identify paddy rice distribution with deep learning (DL) techniques. Using Google Earth Engine (GEE) and U-Net Convolutional Neural Networks (CNN) segmentation, a workflow that accurately delineates smallholder paddy rice fields using multi-temporal S1 SAR and S2 optical imagery was investigated. The study′s accuracy assessment results showed that the optimal dataset for paddy rice mapping was a fusion of S2 multispectral bands (visible and near infra-red (VNIR), red edge (RE) and short-wave infrared (SWIR)), and S1-SAR dual polarization bands (VH and VV) captured within the crop growing season (i.e., vegetative, reproductive, and ripening). Compared to the random forest (RF) classification, the DL model (i.e., ResU-Net) had an overall accuracy of 94% (three percent higher than the RF prediction). The ResU-Net paddy rice prediction had an F1-Score of 0.92 compared to 0.84 for the RF classification generated using 500 trees in the model. Using the optimal U-Net classified paddy rice maps for the dates analyzed (i.e., 2016–2020), a change detection analysis over two epochs (2016 to 2018 and 2018 to 2020) provided a better understanding of the spatial–temporal dynamics of paddy rice agriculture in the study area. The results indicated that 377,895 and 8551 hectares of paddy rice fields were converted to other land-use over the first (2016–2018) and second (2018–2020) epochs. These statistics provided valuable insight into the paddy rice field distribution changes across the selected districts analyzed. The proposed DL framework has the potential to be upscaled and transferred to other regions. The results indicated that the approach could accurately identify paddy rice fields locally, improve decision making, and support food security in the region. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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15 pages, 4463 KiB

Open AccessTechnical Note

Aboveground Biomass Dynamics of a Coastal Wetland Ecosystem Driven by Land Use/Land Cover Transformation

by Wenli Wu, Jiahua Zhang, Yun Bai, Sha Zhang, Shanshan Yang, Malak Henchiri, Ayalkibet Mekonnen Seka and Lkhagvadorj Nanzad

Remote Sens. 2023, 15(16), 3966; https://doi.org/10.3390/rs15163966 - 10 Aug 2023

Viewed by 1022

Abstract

Accurately estimating aboveground biomass (AGB) is essential for assessing the ecological functions of coastal wetlands, and AGB of coastal wetlands is affected by Land use/land cover (LULC) types of conversion. To address this issue, in the current study, we used the Boreal Ecosystem Productivity Simulator (BEPS) model to simulate the AGB of the Yellow River Delta during 2000–2015. Based on the LULC types transform, we analyzed the spatiotemporal dynamics of the AGB simulation results and their relationship with the human-nature driving process. At the same time, combined with the actual situation of LULC transformation in the Yellow River Delta, a new driving process (Replace) is introduced. The results show that from 2000 to 2015, 755 km² of natural wetlands in the Yellow River Delta were converted into constructed wetlands, and AGB increased by 386,121 Mg. Both single and multiple driving processes contributed to the decrease in AGB, with 72.6% of the increase in AGB associated with single artificial (such as Restore) or natural (such as Accretion) driving processes and 27.4% of the increase in AGB associated with multiple driving processes. Naturally driven processes bring much more AGB gain than loss, and human-driven processes bring the largest AGB gain. LULC conversion brought on by anthropogenic and natural driving processes has a large impact on AGB in coastal wetlands, and exploring this impact has a significant role in planning coastal wetland land use and protecting blue carbon ecosystems. Full article

(This article belongs to the Special Issue Applications of Multi-Scale Remote Sensing and GIS Technology to Study Terrestrial Ecosystems)

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