Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 18204

Share This Special Issue

Special Issue Editors

Dr. Qiang Yu

E-Mail Website
Guest Editor

The College of Forestry, Beijing Forestry University, Beijing 100083, China
Interests: complexity theory of spatial network; application of quantitative remote sensing in forestry; carbon use efficiency of forest ecosystem

Dr. Qunou Jiang

E-Mail Website
Guest Editor

School of soil and water conservation, Beijing Forestry University, Beijing 100083, China
Interests: environment of remote sensing; land use change and effect

Dr. Di Yang

E-Mail Website
Guest Editor

Wyoming Geographic Information Science Center, University of Wyoming, Laramie, WY 82071, USA
Interests: remote sensing applications on human-environment interactions; geospatial analytics; digital image processing; machine learning and GeoAI; citizen science; cloud-based big data analysis and management; land change science
Special Issues, Collections and Topics in MDPI journals

Dr. Dandan Zhao

E-Mail Website
Guest Editor

Department of Built Environment, Aalto University, Espoo, Finland
Interests: water resource management
Special Issues, Collections and Topics in MDPI journals

Dr. Xiao Huang

E-Mail Website
Guest Editor

Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
Interests: spatial analysis; remote sensing; human–environment interactions; social sensing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests and grass land are important aspects of natural ecosystems; they can provide ecosystem service functions such as species diversity, carbon fixation and oxygen release, climate regulation, soil and water conservation and vegetation productivity. Under the influence of human activities and climate change, the current spatial structure of forests and grass land is undergoing drastic changes, resulting in impacts on the various ecosystem services of natural ecosystems. The study of the changing characteristics of forests’ and grass lands’ spatial structures and the clarification of the relationship between structure and function is the basis for optimizing forests’ and grass lands’ spatial structures and improving ecosystem service functions.

At present, the rapid development of remote sensing technology provides us with a powerful tool to analyze the above-mentioned scientific problems. We can use MODIS, Landsat, Sentinel and other satellite images to realize long time series mapping of forest and grass land spatial distribution globally; GF, Quick Bird, IKONOS, World View and other satellites, as well as UAV data, can be used to map the spatial structures of forests and grass lands in small- and medium-sized areas. In desert areas, we can use MODIS, Himawari-8 and FY-4 satellite data to retrieve the transport process of sand dust aerosols and analyze the impact of forests’ and grass lands’ spatial structures on wind prevention and sand fixation. We can also establish statistical or mechanism models to realize the inversion of carbon sequestration, oxygen release, climate regulation and other functions.

This Special Issue focuses on the spatial structure, function and interaction of forests and grass lands supported by a variety of remote sensing technologies. Research fields covered in this Special Issue include multi-scale spatial–temporal evolution and driving force analysis of forests and grass lands, the interaction mechanism between changes in forests’ and grass lands’ spatial structures and ecological function, and the application of machine learning, complex system science, GEE models and other remote sensing technologies in forests’ and grass lands’ spatial structures and function analysis.

Articles may address, but are not limited to, the following topics:

The rapid mapping of the spatial structures of forests and grass land using Earth observation techniques in association with deep leaning approaches;
Multi-scale spatial structure analysis using satellite images, UAV aerial photos and ground survey data;
The analysis of spatial structures and the evolution of forests and grass lands based on machine learning and GEE models;
Multi-scale influence mechanisms of the spatial structures and functions of forests and grass lands;
A function-oriented optimization method for forests’ and grass lands’ spatial structures;
Forest soil carbon storage estimation and grass land yield simulation at the regional scale;
Uncertainties, error analysis or new methods for the calculation of the spatial structures and functions of forests and grass lands.

Dr. Qiang Yu
Dr. Qunou Jiang
Dr. Di Yang
Dr. Dandan Zhao
Dr. Xiao Huang
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-source remote sensing
unmanned aerial vehicle
deep learning
spatial analysis
forest and grass land structure
ecosystem function
complex systems science

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

20 pages, 6492 KiB

Open AccessArticle

Estimating the Surface Fuel Load of the Plant Physiognomy of the Cerrado Grassland Using Landsat 8 OLI Products

by Micael Moreira Santos, Antonio Carlos Batista, Eduardo Henrique Rezende, Allan Deyvid Pereira Da Silva, Jader Nunes Cachoeira, Gil Rodrigues Dos Santos, Daniela Biondi and Marcos Giongo

Remote Sens. 2023, 15(23), 5481; https://doi.org/10.3390/rs15235481 - 23 Nov 2023

Viewed by 906

Abstract

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining the fuel load and composition spatially and temporally, are pretty scarce. The need to obtain fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment requires a constant search for improved remote sensing techniques for determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations, (2) to estimate the fuel load through random forest and k-nearest neighbor (k-NN) algorithms in comparison to regression analyses, and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 × 3-pixel window surrounding the reference pixel. For multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, while the R² values of the models fitted using the random forest algorithm ranged from 0.52 to 0.83 and the R² values of those fitted using the k-NN algorithm ranged from 0.30 to 0.68. The estimates made through multiple linear regression analyses showed better results for the equations adjusted for the beginning of the dry season (May and June). Adopting the random forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for the Cerrado grassland relative to multiple linear regression analyses. The variable fraction-soil (FS) exerted the most significant effect on surface fuel load estimates, followed by the vegetation indices NDII, GVMI, DER56, NBR, and MSI, all of which use near-infrared and short-wave infrared channels in their calculations. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

20 pages, 12995 KiB

Open AccessArticle

Mapping Grassland Based on Bio-Climate Probability and Intra-Annual Time-Series Abundance Data of Vegetation Habitats

by Minxuan Sun, Zhengxin Ji, Xin Jiao, Fei Lun, Qiangqiang Sun and Danfeng Sun

Remote Sens. 2023, 15(19), 4723; https://doi.org/10.3390/rs15194723 - 27 Sep 2023

Viewed by 939

Abstract

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (10⁴ km²) of desert-to-steppe (D-S) and 2.34 (10⁴ km²) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

29 pages, 107351 KiB

Open AccessArticle

Optimizing Ecological Spatial Network Topology for Enhanced Carbon Sequestration in the Ecologically Sensitive Middle Reaches of the Yellow River, China

by Fei Wang, Hongqiong Guo, Qibin Zhang, Qiang Yu, Chenglong Xu and Shi Qiu

Remote Sens. 2023, 15(9), 2308; https://doi.org/10.3390/rs15092308 - 27 Apr 2023

Viewed by 1244

Abstract

The destruction of vegetation structure and quantity leads to the weakening of the carbon sequestration capacity of the ecosystem. Building an ecological spatial network is a potent method for studying vegetation spatial distribution structures. The relationship between the spatial distribution structure of vegetation networks and carbon sequestration, as approached from the perspective of complex network theory, is understudied. This study uses the minimum resistance model (MCR) and morphological spatial pattern analysis (MSPA) to study the eco-space network and ecological node spatial structure and topological characteristics of vegetation in the ecologically sensitive area of the middle reaches of the Yellow River (ESAMRYR). Based on the Carnegie-Ames-Stanford approach (CASA) model, the vegetation Net Primary Productivity (NPP) of the study area is calculated, and the ecological carbon sequestration function of the ecological node is estimated, and the relationship between the ecological node and the topological indicators is analyzed. The study shows that the forest land carbon storage in the regions situated toward the south and east of the Yellow River ecologically sensitive area is the highest, accounting for twice the proportion of the area, and is very important in terms of increasing carbon storage. Most of the ecological sources in the study area have a higher topological importance than functional importance, and the sources with low coordination are mainly distributed in the southwest and northeast. We construct a topology and function coupling optimization model (TFCO) to explore the coordination between vegetation structure and carbon sequestration function, to determine the network optimization direction, and to propose optimization solutions. Analysis of network robustness and carbon sequestration capacity shows that the sturdiness and carbon sequestration of the enhanced network are significantly improved. This study provides strategies and methods for protecting ecological sensitive areas, optimizing vegetation spatial distribution, and enhancing carbon sequestration capacity. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Graphical abstract

19 pages, 5430 KiB

Open AccessArticle

Spatial–Temporal Evolutions of Ecological Environment Quality and Ecological Resilience Pattern in the Middle and Lower Reaches of the Yangtze River Economic Belt

by Lu Peng, Haowei Wu and Zhihui Li

Remote Sens. 2023, 15(2), 430; https://doi.org/10.3390/rs15020430 - 11 Jan 2023

Cited by 6 | Viewed by 2085

Abstract

Ecological environment quality and resilience assessment is an important prerequisite for ensuring the coordination and stability of socio-economic development and eco-environment protection. Remote sensing technology has provided new approaches for quantitatively evaluating regional ecological environment quality and resilience rapidly, accurately, and objectively. Taking the middle and lower reaches of the Yangtze River Economic Belt (YREBML) as an example, to assess ecological environment quality, this study calculated the remote sensing ecological index (RSEI) based on the Google Earth Engine using Moderate Resolution Imaging Spectroradiometer (MODIS) data with a spatial resolution of 500 m during 2000–2020. An evaluation index to assess ecological resilience and its spatial pattern based on the RSEI of 2000–2020 was then constructed. The evaluation index was constructed from two dimensions, including the sensitivity and adaptability of the RSEI. Finally, this study identified key factors that affect ecological residence based on a structural equation model. The results showed that the overall RSEI was at moderate and good levels in the YREBML during 2000–2020, accounting for more than 85% of the total area. Its spatial characteristics showed that the RSEI was higher in the middle reaches than in the lower reaches of the YREB, and higher in the south than in the north. The overall RSEI in the YREBML showed a decreasing trend during 2000–2020, with 54.36% of the region improving and 45.64% declining. Areas with declining RSEI were concentrated in Anhui, while the increasing RSEI was observed in Zhejiang. In addition, the spatial pattern of ecological resilience was characterized by high resilience in the north and east, and low resilience in the south and west. High resilience areas accounted for 40.48% of the YREBML, mainly contributed by Jiangxi and Hunan provinces. The driving factors analysis results indicated that economic development, natural disaster risk, and environmental pollution would further affect ecological resilience of urban systems. This study provides more scientific and effective data support for ecological environment monitoring and governance. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

16 pages, 8531 KiB

Open AccessArticle

Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data

by Shanshan Wang, Lizhi Jia, Liping Cai, Yijia Wang, Tianyu Zhan, Anqi Huang and Donglin Fan

Remote Sens. 2022, 14(23), 6011; https://doi.org/10.3390/rs14236011 - 27 Nov 2022

Cited by 2 | Viewed by 1746

Abstract

Grassland is one of the most widely distributed ecosystems on the Tibetan Plateau (TP) accounting for about 60% of the total area. The grassland degradation has spread throughout the TP, and the scope and degree are increasing. The inconsistency of multi-source data poses a great challenge to accurately obtaining information about grassland degradation on the TP. This study used five land cover products and six vegetation indexes to analyze the spatial-temporal change in grassland area and quality at the pixel level across the TP from 2000 to 2020. Then, 279 observed grassland degradation points that were collected from 86 published papers were used to verify the grassland degradation information. The grassland fusion product demonstrated that the grassland area increased by 8.84% from 2000 to 2020, and the rate of grassland degradation exceeded the rate of grassland greening during 2010–2020. The superimposed six vegetation indexes showed that 25.88% of the grassland quality has been degraded on the TP from 2000 to 2020. In Changdu City, Ganzi Tibetan Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, Aba Tibetan and Qiang Autonomous Prefecture, Rikaze City, Shannan City and Nagqu City, the grassland quality degraded by more than in 20% and the degraded grassland area exceeded 2000 km². The observed grassland degradation points were mainly distributed in the northeastern and central parts of the TP. The consistency of six vegetation indexes with the observed grassland degradation points on the TP was 56.63%, with solar-induced chlorophyll fluorescence (SIF) being more effective than other vegetation indexes for monitoring grassland degradation on the TP. In general, the degradation of grassland on the TP has been a looming problem in recent decades. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

18 pages, 3790 KiB

Open AccessArticle

The Grain for Green Program Enhanced Synergies between Ecosystem Regulating Services in Loess Plateau, China

by Ziyue Yu, Xiangzheng Deng and Ali Cheshmehzangi

Remote Sens. 2022, 14(23), 5940; https://doi.org/10.3390/rs14235940 - 24 Nov 2022

Cited by 6 | Viewed by 1927

Abstract

Decades of reckless deforestation have caused serious soil erosion and land desertification issues in the Loess Plateau (LP). “Grain for Green” Program (GFGP), one of the world’s largest ecological restoration projects, is crucial to improve the ecological environment. Previous studies have demonstrated that GFGP lowers soil erosion in the LP. However, there are trade-offs and synergies between ecological services. Does strengthening soil conservation prevent enhancing other ecosystem services? Consequently, can the GFGP improve many ecological services simultaneously? This study compares changes in NDVI prior to and following the implementation of the GFGP in LP to the enhancement of ecosystem services. During the research period, the LP’s overall vegetation cover rose significantly, particularly in the GFGP’s major counties. Significant improvements were made to ecosystem services such as carbon sequestration, soil conservation, and habitat quality. The GFGP enhanced the synergistic linkages between ecological services. The implementation of the GFGP decreased water yield, suggesting trade-offs with other ecosystem services. Additionally, we investigate regional trade-offs/synergies between ecosystem services and their influencing factors, which were influenced by topographic and climatic variables. To maximize the benefits of ecological restoration efforts, we need a deeper understanding of the relationships between ecosystem services and the mechanisms that drive them. Thus, policymakers can scientifically exert control over local influences on ecosystem services, either by boosting the provision of specific services or by limiting specific influences in order to maintain ecosystem stability. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

19 pages, 8173 KiB

Open AccessArticle

Optimization of the Ecological Network Structure Based on Scenario Simulation and Trade-Offs/Synergies among Ecosystem Services in Nanping

by Zixuan Wang, Ling Xiao, Haiming Yan, Yuanjing Qi and Qun’ou Jiang

Remote Sens. 2022, 14(20), 5245; https://doi.org/10.3390/rs14205245 - 20 Oct 2022

Cited by 7 | Viewed by 1681

Abstract

The optimization of the ecological network structure in Nanping can provide a scientific reference for guaranteeing ecological safety in Southeast China. This study estimated ecosystem services in Nanping with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model based on land-use data from 2020 to 2025 simulated with the CLUE-S model under the natural development scenario and ecological protection scenario and then explored their trade-offs and synergies. The ecological network structure was, thereafter, optimized in terms of the eco-matrix, eco-corridors and nodes based on simulated land use and ecosystem services. The results suggested that the average habitat quality and total soil retention increased, while the average degradation index and total water yield decreased under the ecological protection scenario, indicating that the ecological environment quality tended to be improved. In addition, soil retention had significant synergies with habitat quality and water yield, and habitat quality had significant trade-offs with ecological degradation and water yield on the regional scale under two scenarios, while ecological degradation also showed significant trade-offs with soil retention and water yield. In addition, the results suggested that 11 additional ecological sources could be added, and the number of eco-corridors increased from 15 to 136; a total of 1019 ecological break points were restored, and 1481 stepping stone patches were deployed, which jointly made network circuitry, edge/node ratio and network connectivity reach 0.45, 1.86 and 0.64, respectively, indicating that optimization could effectively improve the structure and connectivity of the ecological network. These findings can provide a theoretical basis for improving the ecological network structure and ecological service functions in Nanping and other regions. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

19 pages, 10017 KiB

Open AccessArticle

Scale Effects and Time Variation of Trade-Offs and Synergies among Ecosystem Services in the Pearl River Delta, China

by Wei Liu, Jinyan Zhan, Fen Zhao, Chengxin Wang, Jun Chang, Michael Asiedu Kumi and Manman Leng

Remote Sens. 2022, 14(20), 5173; https://doi.org/10.3390/rs14205173 - 16 Oct 2022

Cited by 9 | Viewed by 1454

Abstract

Natural and socioeconomic variables have an impact on ecosystem services (ESs). The ESs trade-offs/synergies are informed by the reality that the same inputs have varying impacts on different ESs. Changing scales and time can alter dominant drivers and biophysical linkages of ESs, affecting their relationships. Although it is often assumed that ES relationships vary across scales, quantitatively testing this assumption with multiple ES is rare. Therefore, this study evaluated the five key ESs in the Pearl River Delta (PRD) from 1990 to 2015. We also employed a statistical approach to investigate the temporal variations, scale dependency, and spatial heterogeneity of ES trade-offs and synergies. The results demonstrated that: (1) The PRD’s synergetic interaction among ESs has been steadily improving over time; (2) The interaction between ESs dramatically altered as the research scale increased; (3) We discovered that the linkages among the soil conservation (SC), carbon sequestration (CS), water yield (WY), and habitat quality (HQ) were primarily synergistic. ESs of SC, CS, WY, and HQ were found to have negative correlations with grain production. This study will strengthen the understanding of the temporal changes and spatial scales of ESs relationships for decision-makers, which is beneficial to ecosystem management. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

23 pages, 15297 KiB

Open AccessArticle

Evaluation of the Spatiotemporal Evolution of China’s Ecological Spatial Network Function–Structure and Its Pattern Optimization

by Hongjun Liu, Teng Niu, Qiang Yu, Linzhe Yang, Jun Ma and Shi Qiu

Remote Sens. 2022, 14(18), 4593; https://doi.org/10.3390/rs14184593 - 14 Sep 2022

Cited by 11 | Viewed by 1832

Abstract

(1) Background: Eco−spatial networks play an important role in enhancing ecosystem services and landscape connectivity. It is necessary to study landscape structure optimization to achieve synergistic gains in network connectivity and ecosystem functionality. (2) Method: Based on remote sensing data, RS and GIS were used to evaluate the spatiotemporal changes in ecosystem services in China. Combined with complex network theory, the spatiotemporal evolution of China’s ecological spatial network and its topological structure from 2005 to 2020 is discussed. Network function–structure co−optimization was carried out using the edge augmentation strategy. (3) Result: The “three River resource” has high water conservation and high soil and water conservation in southeastern hilly areas. There is strong windbreak and sand fixation in southeastern Inner Mongolia. In the past 15 years, there have been about 8200 sources and about 14,000 corridors. The network has the characteristics of small−world and heterogeneity. After optimization, 18 sources and 3180 corridors are added, and the network connectivity and robustness are stronger. Finally, five regions are divided according to the network heterogeneity and corresponding protection and management countermeasures are proposed to provide scientific guidance for the country’s territorial space planning. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

Other

Jump to: Research

16 pages, 23584 KiB

Open AccessTechnical Note

Mapping Coastal Wetlands and Their Dynamics in the Yellow River Delta over Last Three Decades: Based on a Spectral Endmember Space

by Kun Tan, Danfeng Sun, Wenjun Dou, Bin Wang, Qiangqiang Sun, Xiaojie Liu, Haiyan Zhang, Yang Lan and Fei Lun

Remote Sens. 2023, 15(20), 5003; https://doi.org/10.3390/rs15205003 - 18 Oct 2023

Cited by 1 | Viewed by 981

Abstract

The accurate mapping and analysis of coastal wetlands and their dynamics are crucial for local coastal wetland protection, sustainable social development, and biodiversity preservation. However, detailed mapping and comprehensive analysis of coastal wetlands remain scarce. In this study, we utilized Landsat-TM/OLI remote sensing data and employed the linear spectral mixture analysis (LSMA) method to map changes in coastal wetlands and analyze their dynamics in the Yellow River Delta (YRD) from 1991 to 2020. Our mapping results demonstrate high accuracy and are consistent with previous studies, boasting an overall accuracy exceeding 96%. During the period of 1991–2020, the YRD estuary expanded by approximately 8744.58 ha towards the east and north. The vegetation of P. australis and S. salsa underwent transformation due to agricultural practices or degradation to bare flats. Moreover, these areas saw extensive colonization by the invasive species S. alterniflora. Over the three decades, S. alterniflora expanded approximately 5 km along the coast, significantly impacting the local coastal wetland biodiversity. Furthermore, a considerable number of natural wetlands transitioned into human-made wetlands from 1991 to 2014. In particular, bare flats underwent substantial changes, transforming into aquaculture sites and salt exploitation areas. These dynamics in coastal wetlands had significant repercussions on local ecosystems, including wetland fragmentation, biodiversity depletion, and water pollution. However, post-2014, numerous wetland protection strategies were implemented, resulting in the restoration of natural wetlands. Detailed wetland mapping and dynamic analysis furnish valuable insights for the management, protection, and sustainable utilization of diverse coastal wetlands. Full article

(This article belongs to the Special Issue Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data)

► Show Figures

Journal Menu

Journal Browser

Research on the Structure and Function of Forest and Grassland Based on Multi-Source Remote Sensing Data

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI