Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets

Fu, Hang; Sun, Guifan; Zhang, Li; Zhang, Aizhu; Ren, Jinchang; Jia, Xiuping; Li, Feng

doi:10.1016/j.isprsjprs.2023.07.013

Cited by 29 publications

(2 citation statements)

References 68 publications

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“…Lu et al established a new biomass calculation model to estimate the biomass of the Nan Da Gang Wetland Reserve through linear regression calculations of the NDVI and canopy height but did not make a detailed analysis of the distribution of the spatial pattern of the NDVI [26][27][28][29]. Zhang et al analyzed the relationship between spatial and temporal variations of the NDVI and climate change in Inner Mongolia using NDVI data and meteorological data using general least squares, trend analysis, correlation analysis, etc., with a lack of analysis of anthropogenic factors in the distribution of the NDVI [30][31][32][33]. Currently, research on and applications of the NDVI have become an active topic in remote sensing and an indispensable part of environmental research.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images

Liu,

Chen,

Chen

2023

Remote Sensing

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Vegetation is an important type of land cover. Long-term, large-scale, and high-precision vegetation monitoring is of great significance for ecological environment investigation and regional sustainable development in protected areas. This paper develops a long-term remote sensing monitoring method for vegetation by calculating the normalized difference vegetation index (NDVI) based on the Google Earth Engine (GEE) cloud platform and Landsat satellite remote sensing images. First, based on Landsat long-term satellite images and GEE, the spatiotemporal distribution map of the NDVI is accurately drawn. Subsequently, the NDVI is accurately classified, and the time trend analysis of the NDVI is conducted based on the NDVI mean trend graphs, transition matrices, etc. Then, combined with Moran’s I, high/low clusters, and other methods, the spatial pattern characteristics of the NDVI are analyzed. Finally, climate factors, terrain factors, and anthropologic factors are considered comprehensively. An analysis of the factors affecting the evolution of the NDVI is performed. Taking Zhoushan Island, China, as an example, an experiment is conducted, and the results reveal that (1) the average NDVI exhibits a decreasing trend from 1985 to 2022, decreasing from 0.53 in 1985 to 0.46 in 2022. (2) Regarding vegetation index transitions, the high NDVI areas (0.6–1) exhibit the most substantial shift toward moderately high NDVI values (0.4–0.6), covering an area of 83.10 km2. (3) There is an obvious spatial agglomeration phenomenon in the NDVI on Zhoushan Island. The high-high NDVI clusters and the significant hot spots are predominantly concentrated in the island’s interior regions, while the low-low NDVI clusters and the significant cold spots are mainly situated along the coastal areas. (4) The DEM, slope, and temperature have a greater influence among the single factors on the spatial pattern distribution of the NDVI in 2015. There are significant differences in the spatial pattern distribution of the NDVI between the temperature and DEM, temperature and slope, DEM and precipitation, slope and precipitation, aspect and population, and aspect and gross domestic product (GDP). The DEM and slope, DEM and temperature, and DEM and population are three sets of factors with a strong influence on spatial pattern interaction. This study provides data support for the scientific management of vegetation resources on Zhoushan Island and is of great significance to the sustainable development of the island region.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images

Liu,

Chen,

Chen

2023

Remote Sensing

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“…Surface water refers to the water that exists on the surface of the Earth's crust and is exposed to the atmosphere, including rivers, lakes, reservoirs, and ponds. It plays a crucial role in the balance between ecosystems and socio-economic development and provides essential resources for the survival of living organisms and the development of human society [1][2][3]. Surface water resources have a variety of ecosystem service values such as water storage, flood control, aquaculture, power generation, and transportation.…”

Section: Introductionmentioning

confidence: 99%

Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series

Jin,

Fang,

Chen

2023

Remote Sensing

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Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.

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Image Enhancement for UAV Visual SLAM Applications: Analysis and Evaluation

Tian,

Yue,

Ren

2024

Lecture Notes in Computer Science

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Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets

Cited by 29 publications

References 68 publications

Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images

Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images

Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series

Image Enhancement for UAV Visual SLAM Applications: Analysis and Evaluation

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