Estimating Long-Term Fractional Vegetation Cover Using an Improved Dimidiate Pixel Method With UAV-Assisted Satellite Data: A Case Study in a Mining Region

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-17 DOI:10.1109/JSTARS.2025.3531439

Shuang Wu;Lei Deng;Qinghua Qiao

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引用次数: 0

Abstract

Accurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) combined with satellite data (UCS) inadequately leverage the high spatial resolution of UAV imagery to address spatial heterogeneity and are seldom applied to long-term FVC monitoring. To overcome spatial challenges, an improved dimidiate pixel method (IDPM) is proposed here, utilizing 2021 Landsat imagery to generate FVC_DPM via DPM and upscaled UAV imagery for FVC_UAV as ground references. The IDPM uses the pruned exact linear time method to segment the normalized difference vegetation index (NDVI) into intervals, within which DPM performance is evaluated for potential improvements. Specifically, if the difference (D) between FVC_DPM and FVC_UAV is nonzero, NDVI-derived texture features are incorporated into FVC_DPM through multiple linear regression to enhance accuracy. To address temporal challenges and ensure consistency across years, the 2021 NDVI serves as a reference for inter-year NDVI calibration, employing least squares regression (LSR) and histogram matching (HM) to identify the most effective method for extending the IDPM to other years. Results demonstrate that 1) the IDPM, by developing distinct DPM improvement models for different NDVI intervals, considerably improves UAV and satellite data integration, with a 48.51% increase in R² and a 56.47% reduction in root mean square error (RMSE) compared to the DPM and UCS and 2) HM is found to be more suitable for mining areas, increasing R² by 25.00% and reducing RMSE by 54.05% compared to LSR. This method provides an efficient, rapid solution for mitigating spatial heterogeneity and advancing long-term FVC estimation.

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.