A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Spatiotemporal Pattern of Land Cover Types on Loess Plateau
[Objective] A long-term and high-precision land cover dataset was constructed for the Loess Plateau. The spatiotemporal pattern of land cover in 2001 and 2020 was analyzed in order to provide a scientific underpinning for initiatives concerning ecological environmental preservation and sustainable development within the region. [Methods] Training samples were constructed using multiple sources of land cover products and ground feature data from various time periods. The Google Earth Engine (GEE) platform and a random forest classification model were used to generate the land cover of Loess Plateau (LCLP) dataset. Spatial analysis and a univariate linear regression model were then used to analyze the spatiotemporal pattern of land cover types on the Loess Plateau. [Results] According to the validation set built using random forest, LCLP exhibited an overall accuracy and kappa coefficient greater than 90%. Moreover, based on the independent verification set, LCLP demonstrated an overall accuracy ranging from 0.58% to 20.23% higher than existing products. Additionally, the accuracy of the classification of various land cover types, including cultivated land, forest land, grassland, impervious surface, and bare land, was increased. [Conclusion] Compared with other datasets, LCLP significantly improved classification accuracy and is suitable for accurately reflecting land cover changes for the Loess Plateau region. During 2001—2020, there has been a decreasing trend in cultivated land and shrubs in the Loess Plateau region, while forest land, water bodies, and impervious surfaces have shown a significant increasing trend. From the perspective of land cover changes, cultivated land and grassland were the primary sources of newly added land cover types.
Spatiotemporal Pattern of Land Cover Types on Loess Plateau
[Objective] A long-term and high-precision land cover dataset was constructed for the Loess Plateau. The spatiotemporal pattern of land cover in 2001 and 2020 was analyzed in order to provide a scientific underpinning for initiatives concerning ecological environmental preservation and sustainable development within the region. [Methods] Training samples were constructed using multiple sources of land cover products and ground feature data from various time periods. The Google Earth Engine (GEE) platform and a random forest classification model were used to generate the land cover of Loess Plateau (LCLP) dataset. Spatial analysis and a univariate linear regression model were then used to analyze the spatiotemporal pattern of land cover types on the Loess Plateau. [Results] According to the validation set built using random forest, LCLP exhibited an overall accuracy and kappa coefficient greater than 90%. Moreover, based on the independent verification set, LCLP demonstrated an overall accuracy ranging from 0.58% to 20.23% higher than existing products. Additionally, the accuracy of the classification of various land cover types, including cultivated land, forest land, grassland, impervious surface, and bare land, was increased. [Conclusion] Compared with other datasets, LCLP significantly improved classification accuracy and is suitable for accurately reflecting land cover changes for the Loess Plateau region. During 2001—2020, there has been a decreasing trend in cultivated land and shrubs in the Loess Plateau region, while forest land, water bodies, and impervious surfaces have shown a significant increasing trend. From the perspective of land cover changes, cultivated land and grassland were the primary sources of newly added land cover types.
Spatiotemporal Pattern of Land Cover Types on Loess Plateau
Ma Hui (author) / Zhao Hongfei (author) / Yue Chao (author) / Zhao Jie (author) / Li Yu (author) / Wang Mengyu (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under ​CC BY-SA 1.0
| Elsevier | 2024