The accuracy assessment of land cover data is of significant value to accurately monitor and objectively reproduce spatio-temporal dynamic changes to land surface landscapes. In this study, the interpretation and applicability of CCI, MCD, and CGLS long time-series land cover data products for China were evaluated via consistency analysis and a confusion matrix system using NLUD-C periodic products as reference data. The results showed that CGLS had the highest overall accuracy, Kappa coefficient, and area consistency in the continuous time-series evaluation, followed by MCD, whereas CCI had the worst performance. For the accuracy assessment of subdivided land cover types, the three products could accurately describe the distribution of forest land in China with a high recognition level, but their recognition ability for water body and construction land was poor. Among the other types, CCI could better identify cropland, MCD for grassland, and CGLS for unused land. Based on these evaluation results and characteristics of the data products, we developed suitable selection schemes for users with different requirements. 

Accuracy assessment of land cover products in China from 2000 to 2020

Quantifying the change in land use and land cover (LULC) is critical for revealing the impact of human activities on the environment of the Earth’s surface. Although some studies were conducted on the change in LULC in rapidly urbanizing areas, conventional methods could not provide a systematic understanding of the changes and their underlying causes. This study adopted an enhanced Intensity Analysis and landscape matrices to deeply explore the change information and expansion modes of LULC in the Chang-Zhu-Tan Metropolitan Region (CZTMR). This exploration was based on remote sensing images from the past 40 years and GIS tools. The results show that the overall change in the LULC accelerated during the period 1980–2020, with its intensity expanding by 16 times. The Built gain and the Crop loss were steadily active. The Built gain was derived mainly from Crop and Forest, and its mode was dominated by edge expansion. It was detected that the Built gain steadily targeted Crop but avoided Forest despite Built gaining a large area from Forest. The reason for this is because Forest initially had the largest area. The measurement results contribute to the formulation of urban plans and land policies for sustainable development in the CZTMR. Our study explained the evolution of Intensity Analysis and its analytical thought, which could be employed in other regions for the detection of land change to help decision makers develop more targeted and sustainable land management strategies.

/pdf/intensity-analysis-to-communicate-detailed-detection-of-land-18i83840.pdf

Intensity Analysis to Communicate Detailed Detection of Land Use and Land Cover Change in Chang-Zhu-Tan Metropolitan Region, China

Use of intensity analysis and transfer matrix to characterize land conversion in the Aral Sea Basin under changing climate

Soil water content mediates the spatiotemporal nitrogen uptake by a dominant plant species in a subtropical wetland ecosystem

Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. Spatial analysis and dynamic degree were used to analyze the evolutionary process and spatiotemporal association of land cover from 1990 to 2020; the transfer matrix was used to analyze and reveal dynamic conversions of land cover from 1990 to 2000, 2000 to 2010, and 2010 to 2020; and the GM (1,1) model was used to forecast the changes in land cover by 2025 based on historical data. The results indicated that the dominance of forest and cropland did not change from 1990 to 2020, and the average area of forest and cropland was 512,713 km2 and 486,322 km2, respectively. The mutual conversion between cropland, forest, grassland, and bare areas was the most frequent. The area of cropland converted into forest and grassland was 14,167 km2 and 25,217 km2, respectively, and the area of forest and grassland converted into cropland was 27,682 km2 and 23,764 km2, respectively, from 1990 to 2000. A similar law of land cover change was also presented from 2000 to 2020. In addition, the predicted values of cropland, forest, grassland, shrubland, wetland, water bodies, impervious surfaces, and bare areas were 466,942 km2, 499,950 km2, 231,524 km2, 1329 km2, 11,775 km2, 18,453 km2, 30,549 km2, and 189,973 km2, respectively, by 2025. The maximum and minimum residuals between the predicted and actual values were 6241 km2 and −156 km2 from 1990 to 2020. The evaluation results of the GM (1,1) model showed that all of the evaluation indices were within an acceptable range, and that the posteriori error ratio and class ratio dispersion were both less than 0.25. Through comparison with other studies, this study is not only able to provide some experience for further analyzing the spatial and temporal changes in land cover and its future prediction but also provide a basis for comprehensive management in Northeast China.

/pdf/spatiotemporal-conflict-analysis-and-prediction-of-long-time-2i4gdo1h.pdf

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Density-dependent plant-soil feedbacks of two plant species affected by plant competition.

Human activities are considered as the main driving forces of land use/land cover (LULC) variation at city scales. Monitoring the dynamic variation of LULC and its socioeconomic driving forces helps to reveal the response of LULC change to human activities and land use policies. However, this issue remains poorly understood. In this study, the spatiotemporal transitions among different LULC types during nearly three decades in Wuhan, China, were modeled in detail using the transfer matrix method. Ten socioeconomic factors indicating the population level, economic condition and social development were selected to quantitatively explain LULC variation. Some typical policies were discussed for the LULC transitions. The results showed that construction land was detected to continuously increase, with the fastest change rate of 560.48% during the 29-year period. Farmland area significantly declined by 1855 km2, decreasing by 31.21%, contributing to 86.14% of the area increase in construction lands. To some extent, the net area increase in construction land was at the expense of farmland area. All 10 indicators considered in this study were positively correlated with the construction land area (R2 of 0.783~0.970) and negatively correlated with farmland area (R2 of 0.861~0.979). In general, social and economic development contributed considerably to urban expansion and cultivated land loss. The largest contributors were non-agricultural population and economic conditions (secondary industry output, primary industry output and local revenues). Governmental guidance and behavior were considered the original impetus for LULC transition, while the impact of land use policies and human activities on LULC transitions varied across the subperiods. These findings provide decision-making support for appropriate urban planning and efficient land use management.

/pdf/dynamic-evolution-of-land-use-land-cover-and-its-163bo0ge.pdf

Dynamic Evolution of Land Use/Land Cover and Its Socioeconomic Driving Forces in Wuhan, China

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Liming Zhang

Author Tools

Chat about Author

Papers

Density-dependent plant-soil feedbacks of two plant species affected by plant competition.

Dynamic Evolution of Land Use/Land Cover and Its Socioeconomic Driving Forces in Wuhan, China