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Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data

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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 (30 September 2023) | Viewed by 36075

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Special Issue Editors

Dr. Yaqian He

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Guest Editor

Department of Geography, University of Central Arkansas, Conway, AR 72035, USA
Interests: land cover and land use change; land-atmosphere interactions
Special Issues, Collections and Topics in MDPI journals

Dr. Fang Fang

E-Mail Website
Guest Editor

School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA
Interests: landscape mapping; object-based image analysis using LiDAR; machine learning algorithm
Special Issues, Collections and Topics in MDPI journals

Dr. Christopher Ramezan

E-Mail Website
Guest Editor

John Chambers College of Business and Economics, West Virginia University, Morgantown, WV, USA
Interests: land cover land use mapping; machine learning and deep learning; object-based image analysis, and sample selection

Special Issue Information

Dear Colleagues,

Land Cover and Land Use (LCLU) is an important component of the Earth system. It affects ecosystem services, water conservation, infectious disease outbreaks, food security, the climate system, urban forms, and so on. In the last few decades, remote sensing technology has evolved dramatically for better radiometric, spatial, and spectral resolution image products. Such advances in remote sensing, together with a variety of machine- and deep learning algorithms and multiple high-performance cloud computing platforms, further provide the potential and opportunities for a fine-resolution, global-scale, and dynamic LCLU mapping. In addition, along with the development of drone technology, the (near) real-time monitoring of land surface in different sectors, such as forestry and agriculture, has attracted much attention in recent years. The improved documenting of LCLU leads the scientific community to further explore the driving factors of LCLU changes and reveal the underlying mechanisms for better land use planning.

This Special Issue aims to solicit studies that provide insight about the remote sensing of land cover and land use mapping and its impact and driving factors at local, regional, or global scales. Topics may include anything from land cover and land use mapping for a certain vegetation type (e.g., pasture) at small scale to a more comprehensive large-scale evaluation or method. Therefore, multisource data integration for LCLU mapping, classification algorithms development, accuracy assessment, LCLU change detection, socioeconomic drivers, and so on, are all welcome.

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

(1) LCLU mapping using multi-sources data (e.g., multispectral, hyperspectral, LiDAR, and drone images);

(2) Forest species mapping;

(3) Crop types mapping;

(4) Coastal wetlands mapping;

(5) Classification and change detection algorithms or methods development (e.g., deep learning algorithm);

(6) Accuracy assessment method development for established LCLU products;

(7) Impacts of LCLU changes on ecosystems, disease, the hydrology cycle, the climate system, and so on;

(8) Physical and socioeconomic drivers of LCLU;

(9) LCLU policy;

(10) LCLU monitoring.

Dr. Yaqian He
Dr. Fang Fang
Dr. Christopher Ramezan
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

land cover and land use
land cover and land use change
remote sensing
classification
machine learning
accuracy assessment
socioeconomic drivers

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Published Papers (11 papers)

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Research

20 pages, 5342 KiB

Open AccessArticle

WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory

by Rui Zhu, Yumin Tan, Ziqing Luo, Yanzhe Shi, Jiale Wang, Guifei Jing and Xiaolu Wang

Remote Sens. 2024, 16(2), 257; https://doi.org/10.3390/rs16020257 - 9 Jan 2024

Viewed by 1178

Abstract

Land cover (LC) products play a crucial role in various fields such as change detection, resource management, and urban planning. The diversity in methods and principles used to create different products poses a challenge for researchers in choosing the most suitable one for research needs. Mainstream evaluation methods typically consider only a portion of the accuracy information from the product and require a significant effort in creating validation datasets. Here, we propose a relative accuracy assessment method for LC products based on optimal transport theory, which provides a comprehensive evaluation by utilizing a broader range of accuracy information within the product. The method can directly compute the similarity between the target product and the reference truth at a global scale, addressing the issue of quantitatively assessing product accuracy in the absence of a validation dataset. To validate the effectiveness of the method, we select Beijing as the study area to assess the accuracy of four LC products. The results suggest that the method allows for precise quantification of product accuracy, aligning closely with validation outcomes, which can provide valuable guidance to researchers in both product creation and selection. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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19 pages, 3953 KiB

Open AccessArticle

Finding Misclassified Natura 2000 Habitats by Applying Outlier Detection to Sentinel-1 and Sentinel-2 Data

by David Moravec, Vojtěch Barták and Petra Šímová

Remote Sens. 2023, 15(18), 4409; https://doi.org/10.3390/rs15184409 - 7 Sep 2023

Viewed by 1037

Abstract

The monitoring of Natura 2000 habitats (Habitat Directive 92/43/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called “attribute outliers”, i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott’s rule, and 95% quantiles in χ² distribution). The Mahalanobis distance thresholded by the 95% χ² quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat/land cover mapping activities where the detection of misclassifications or changes is needed. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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23 pages, 13319 KiB

Open AccessArticle

Multi-Difference Image Fusion Change Detection Using a Visual Attention Model on VHR Satellite Data

by Jianhui Luo, Qiang Chen, Lei Wang and Yixiao Huang

Remote Sens. 2023, 15(15), 3799; https://doi.org/10.3390/rs15153799 - 30 Jul 2023

Cited by 2 | Viewed by 1741

Abstract

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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16 pages, 15261 KiB

Open AccessArticle

Analysis of Four Decades of Land Use and Land Cover Change in Semiarid Tunisia Using Google Earth Engine

by Nesrine Kadri, Sihem Jebari, Xavier Augusseau, Naceur Mahdhi, Guillaume Lestrelin and Ronny Berndtsson

Remote Sens. 2023, 15(13), 3257; https://doi.org/10.3390/rs15133257 - 24 Jun 2023

Cited by 16 | Viewed by 4404

Abstract

Semiarid Tunisia is characterized by agricultural production that is delimited by water availability and degraded soil. This situation is exacerbated by human pressure and the negative effects of climate change. To improve the knowledge of long-term (1980 to 2020) drivers for Land Use and Land Cover (LULC) changes, we investigated the semiarid Rihana region in central Tunisia. A new approach involving Google Earth Engine (GEE) was used to map LULC using Landsat imagery and vegetative indices (NDVI, MSAVI, and EVI) by applying a Random Forest (RF) classifier. A Rapid Participatory Systemic Diagnosis (RPSD) was used to consider the relation between LULC changes and their key drivers. The methodology relied on interviews with the local population and experts. Focus groups were conducted with practicians of the Regueb Agricultural Extension Services, followed by semi-structured interviews with 52 households. Results showed the following: (1) the RF classifier in Google Earth Engine had strong performance across diverse Landsat image types resulting in overall classification accuracy of ≥0.96 and a kappa coefficient ≥0.93; (2) rainfed olive land increased four times during the study period while irrigated agriculture increased substantially during the last decade; rangeland and rainfed annual crops decreased by 58 and 88%, respectively, between 1980 and 2021; (3) drivers of LULC changes are predominately local in nature, including topography, local climate, hydrology, strategies of household, effects of the 2010 revolution, associated increasing demand for natural resources, agricultural policy, population growth, high cost of agricultural input, and economic opportunities. To summarize, changes in LULC in Rihana are an adaptive response to these various factors. The findings are important to better understand ways towards sustainable management of natural resources in arid and semiarid regions as well as efficient methods to study these processes. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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29 pages, 3473 KiB

Open AccessArticle

Advancing High-Resolution Land Cover Mapping in Colombia: The Importance of a Locally Appropriate Legend

by J. Camilo Fagua, Susana Rodríguez-Buriticá and Patrick Jantz

Remote Sens. 2023, 15(10), 2522; https://doi.org/10.3390/rs15102522 - 11 May 2023

Cited by 3 | Viewed by 2988

Abstract

Improving the remote sensing frameworks related to land cover mapping is necessary to make informed policy, development, planning, and natural resource management decisions. These efforts are especially important in tropical countries where technical capacity is limited. Land cover legend specification is a critical first step when mapping land cover, with consequences for its subsequent use and interpretation of results. We integrated the temporal metrics of SAR (Synthetic Aperture Radar) and multispectral data (Sentinel-1 and Sentienel-2) with visual pixel classifications and field surveys using five machine learning algorithms that apply different statistical methods to assess the prediction and mapping of two different land cover legends at a high spatial resolution (10 m) in a tropical region with seasonal flooding. The evaluated legends were CORINE (Coordination of Information on the Environment) and ECOSO, a legend that we defined based on the ecological and socio-economic conditions of the study area. Compared with previous studies, we obtained high accuracies for land cover modeling (kappa = 0.82) and land cover mapping (kappa = 0.76) when using ECOSO. We also found that the CORINE legend generated lower accuracies than the ECOSO legend (kappa = 0.79 for land cover modeling and kappa = 0.61 for the land cover mapping). Although CORINE was developed for European environments, it is the official land cover legend of Colombia, a South American country with tropical ecosystems not found in Europe. Therefore, some of the CORINE classes have ambiguous definitions for the study area, explaining the lower accuracy of its modeling and mapping. We used free and open-access data and software in this research; thus, our methods can be applied in other tropical regions. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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19 pages, 12076 KiB

Open AccessArticle

Spatial–Temporal Changes in Land Use and Their Driving Forces in the Circum-Bohai Coastal Zone of China from 2000 to 2020

by Jian Cui, Wenxin Ji, Peng Wang, Mingshui Zhu and Yaohui Liu

Remote Sens. 2023, 15(9), 2372; https://doi.org/10.3390/rs15092372 - 30 Apr 2023

Cited by 7 | Viewed by 1998

Abstract

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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19 pages, 3176 KiB

Open AccessArticle

Combining LSTM and PLUS Models to Predict Future Urban Land Use and Land Cover Change: A Case in Dongying City, China

by Xin Zhao, Ping Wang, Songhe Gao, Muhammad Yasir and Qamar Ul Islam

Remote Sens. 2023, 15(9), 2370; https://doi.org/10.3390/rs15092370 - 30 Apr 2023

Cited by 8 | Viewed by 3426

Abstract

Land use is a process that turns a piece of land’s natural ecosystem into an artificial one. The mix of plant and man-made covers on the Earth’s surface is known as land cover. Land use is the primary external force behind change in land cover, and land cover has an impact on how land use is carried out, resulting in a synergistic interaction between the two at the Earth’s surface. In China’s Shandong Peninsula city cluster, Dongying is a significant coastal port city. It serves as the administrative hub for the Yellow River Delta and is situated in Shandong Province, China’s northeast. The changes in its urban land use and land cover in the future are crucial to understanding. This research suggests a prediction approach that combines a patch-generation land use simulation (PLUS) model and long-term short-term memory (LSTM) deep learning algorithm to increase the accuracy of predictions of future land use and land cover. The effectiveness of the new method is demonstrated by the fact that the average inaccuracy of simulating any sort of land use in 2020 is around 5.34%. From 2020 to 2030, 361.41 km² of construction land is converted to cropland, and 424.11 km² of cropland is converted to water. The conversion areas between water and unused land and cropland are 211.47 km² and 148.42 km², respectively. The area of construction land and cropland will decrease by 8.38% and 3.64%, respectively, while the area of unused land, water, and grassland will increase by 5.53%, 2.44%, and 0.78%, respectively. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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20 pages, 4291 KiB

Open AccessArticle

A Reference-Free Method for the Thematic Accuracy Estimation of Global Land Cover Products Based on the Triple Collocation Approach

by Pengfei Chen, Huabing Huang, Wenzhong Shi and Rui Chen

Remote Sens. 2023, 15(9), 2255; https://doi.org/10.3390/rs15092255 - 24 Apr 2023

Cited by 1 | Viewed by 1804

Abstract

Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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18 pages, 13231 KiB

Open AccessArticle

Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery

by Anastasios Tzepkenlis, Konstantinos Marthoglou and Nikos Grammalidis

Remote Sens. 2023, 15(8), 2027; https://doi.org/10.3390/rs15082027 - 11 Apr 2023

Cited by 17 | Viewed by 6273

Abstract

Nowadays, different machine learning approaches, either conventional or more advanced, use input from different remote sensing imagery for land cover classification and associated decision making. However, most approaches rely heavily on time-consuming tasks to gather accurate annotation data. Furthermore, downloading and pre-processing remote sensing imagery used to be a difficult and time-consuming task that discouraged policy makers to create and use new land cover maps. We argue that by combining recent improvements in deep learning with the use of powerful cloud computing platforms for EO data processing, specifically the Google Earth Engine, we can greatly facilitate the task of land cover classification. For this reason, we modify an efficient semantic segmentation approach (U-TAE) for a satellite image time series to use, as input, a single multiband image composite corresponding to a specific time range. Our motivation is threefold: (a) to improve land cover classification performance and at the same time reduce complexity by using, as input, satellite image composites with reduced noise created using temporal median instead of the original noisy (due to clouds, calibration errors, etc.) images, (b) to assess performance when using as input different combinations of satellite data, including Sentinel-2, Sentinel-1, spectral indices, and ALOS elevation data, and (c) to exploit channel attention instead of the temporal attention used in the original approach. We show that our proposed modification on U-TAE (mIoU: 57.25%) outperforms three other popular approaches, namely random forest (mIoU: 39.69%), U-Net (mIoU: 55.73%), and SegFormer (mIoU: 53.5%), while also using fewer training parameters. In addition, the evaluation reveals that proper selection of the input band combination is necessary for improved performance. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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22 pages, 18102 KiB

Open AccessArticle

Spatial–Temporal and Driving Factors of Land Use/Cover Change in Mongolia from 1990 to 2021

by Junming Hao, Qingrun Lin, Tonghua Wu, Jie Chen, Wangping Li, Xiaodong Wu, Guojie Hu and Yune La

Remote Sens. 2023, 15(7), 1813; https://doi.org/10.3390/rs15071813 - 29 Mar 2023

Cited by 12 | Viewed by 3909

Abstract

During the past several decades, desertification and land degradation have become more and more serious in Mongolia. The drivers of land use/cover change (LUCC), such as population dynamics and climate change, are increasingly important to local sustainability studies. They can only be properly analyzed at small scales that capture the socio-economic conditions. Several studies have been carried out to examine the pattern of LUCC in Mongolia, but they have been focused on changes in single land types at a local scale. Although some of them were carried out at the national scale, the data interval is more than 10 years. A small-scale and year-by-year dataset of LUCC in Mongolia is thus needed for comprehensive analyses. We obtained year-by-year land use/cover changes in Mongolia from 1990 to 2021 using Landsat TM/OLI data. First, we established a random forest (RF) model. Then, in order to improve the classification accuracy of the misclassification of cropland, grassland, and built and barren areas, the classification and regression trees model (CART) was introduced for post-processing. The results show that 17.6% of the land surface has changed at least once among the six land categories from 1990 to 2021. While the area of barren land has significantly increased, the grassland and forest areas have exhibited a decreasing trend in the past 32 years. The other land types do not show promising changes. To determine the driving factors of LUCC, we applied an RF feature importance ranking to environmental factors, physical factors, socioeconomic factors, and accessibility factors. The mean annual precipitation (MAP), evapotranspiration (ET), mean annual air temperature (MAAT), DEM, GDP, and distance to railway are the main driving factors that have determined the distribution and changes in land types. Interestingly, unlike the global anti-V-shaped pattern, we found that the land use/cover changes show an N-shaped trend in Mongolia. These characteristics of land use/cover change in Mongolia are primarily due to the agricultural policies and rapid urbanization. The results present comprehensive land use/cover change information for Mongolia, and they are of great significance for policy-makers to formulate a scientific sustainable development strategy and to alleviate the desertification of Mongolia. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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25 pages, 18137 KiB

Open AccessArticle

Land Use/Land Cover Mapping Based on GEE for the Monitoring of Changes in Ecosystem Types in the Upper Yellow River Basin over the Tibetan Plateau

by Senyao Feng, Wenlong Li, Jing Xu, Tiangang Liang, Xuanlong Ma, Wenying Wang and Hongyan Yu

Remote Sens. 2022, 14(21), 5361; https://doi.org/10.3390/rs14215361 - 26 Oct 2022

Cited by 21 | Viewed by 5178

Abstract

The upper Yellow River basin over the Tibetan Plateau (TP) is an important ecological barrier in northwestern China. Effective LULC products that enable the monitoring of changes in regional ecosystem types are of great importance for their environmental protection and macro-control. Here, we combined an 18-class LULC classification scheme based on ecosystem types with Sentinel-2 imagery, the Google Earth Engine (GEE) platform, and the random forest method to present new LULC products with a spatial resolution of 10 m in 2018 and 2020 for the upper Yellow River Basin over the TP and conducted monitoring of changes in ecosystem types. The results indicated that: (1) In 2018 and 2020, the overall accuracy (OA) of LULC maps ranged between 87.45% and 93.02%. (2) Grassland was the main LULC first-degree class in the research area, followed by wetland and water bodies and barren land. For the LULC second-degree class, the main LULC was grassland, followed by broadleaf shrub and marsh. (3) In the first-degree class of changes in ecosystem types, the largest area of progressive succession (positive) was grassland–shrubland (451.13 km²), whereas the largest area of retrogressive succession (negative) was grassland–barren (395.91 km²). In the second-degree class, the largest areas of progressive succession (positive) were grassland–broadleaf shrub (344.68 km²) and desert land–grassland (302.02 km²), whereas the largest areas of retrogressive succession (negative) were broadleaf shrubland–grassland (309.08 km²) and grassland–bare rock (193.89 km²). The northern and southwestern parts of the study area showed a trend towards positive succession, whereas the south-central Huangnan, northeastern Gannan, and central Aba Prefectures showed signs of retrogressive succession in their changes in ecosystem types. The purpose of this study was to provide basis data for basin-scale ecosystem monitoring and analysis with more detailed categories and reliable accuracy. Full article

(This article belongs to the Special Issue Advances in Mapping Land Cover and Land Use Based on Remotely Sensed Data)

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