Comprehensive Representations of Subpixel Land Use and Cover Shares by Fusing Multiple Geospatial Datasets and Statistical Data with Machine-Learning Methods

Yuxuan Chen, Rongping Li (), Yuwei Tu, Xiaochen Lu and Guangsheng Chen ()
Additional contact information
Yuxuan Chen: State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
Rongping Li: Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
Yuwei Tu: State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
Xiaochen Lu: State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
Guangsheng Chen: State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China

Land, 2024, vol. 13, issue 11, 1-23

Abstract: Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 10 4 km 2 , 15.19 × 10 4 km 2 and 2.85 × 10 4 km 2 , respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 10 4 km 2 , 4.24 × 10 4 km 2 , 3.97 × 10 4 km 2 and 0.92 × 10 4 km 2 , respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies.

Keywords: fractional land cover share; machine-learning method; northeastern China; land use and cover change (LUCC); NDVI (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/11/1814/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/11/1814/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:11:p:1814-:d:1512447

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().