Accurate monitoring of aboveground biomass (AGB) is crucial in preventing grassland degradation and achieving carbon neutrality. Remote sensing data and machine learning-based methods have been widely used to estimate the grassland AGB from national to regional scales due to their unique advantages of low cost and high efficiency. However, in the context of significant spatial heterogeneity, the estimation process for AGB in this category usually has inherent uncertainty. Existing statistical validation methods are unable to characterize the spatial distribution of uncertainty and generally lack consideration of potential uncertainty in grassland AGB. To address this issue, we developed a framework to map the spatial distribution of uncertainty based on the quantile regression forest model. Furthermore, the framework explored the driving factors of uncertainty using the geographical detector model. The research results show that the quantile regression forests model in the framework well-estimated the grassland AGB and characterized its spatial distribution. Also, the spatial pattern of uncertainty was closely related to the AGB and affected the amount of sampling points. Among the multiple factors, the soil-adjusted vegetation indices were the primary driving force of the uncertainty. This research presents an approach for mapping uncertainty in grassland AGB estimation and spatializing estimation error, which could be an effective complement to existing AGB estimation methods and thus facilitate the accurate management of grassland resources.
In the agricultural field, optical remote sensing technology plays an important role in crop monitoring or production estimation. However, the widespread distribution of clouds and rain limits the application of optical remote sensing. Synthetic aperture radar (SAR) has been widely used for studies of oceans, atmosphere, land, and space exploration, as well as by the military due to its all-weather nature, penetration to surface and cloud layers, and diversity of information carriers. However, it is difficult to classify ground objects with high accuracy based on SAR data. Considering the features of these two datasets, we proposed a framework to improve crop classifications in cloudy and rainy areas based on the optical-SAR response mechanism. Specifically, this method is designed to train a parametric analytic model in the area using both kinds of datasets and applied in the area with only SAR data to obtain the optical time-series features. Then crops from the second area were classified by the long-short-term memory network. As an example, the parametric analytic model in Lixian County was studied and was applied to Xifeng County to classify the crops with the OA of 61%, which had proved the robustness of the method.
Feature-based change detection technologies using multitemporal remote sensing images are widely applied to find newly increased built-up areas (NIBUA) during the period of observation. This paper proposes an automatic object-based NIBUA extraction method using high-resolution remote sensing images, which is based on the integration of spectrum feature, edge-derived line-density-based visual saliency (LDVS) feature, and texture-derived built-up presence index (PanTex) feature. In the proposed method, image segmentation is first employed to obtain objects as basic units of detection. Next, due to the complexity of built-up areas in high-resolution images, LDVS images and PanTex images are produced for each temporal image, respectively. Then, to highlight built-up areas in complex scenes, a comprehensive measure for each object is calculated by integrating the newly increased measures from spectrum, LDVS, and PanTex features via a manner of Dempster–Shafer evidence fusion. Finally, the object-based NIBUA can be extracted by conducting binarization on the newly increased fused measure image. Comparison studies and experimental results demonstrate that our method can achieve a robust extraction of NIBUA from high-resolution remote sensing images with a higher detection accuracy. We conclude that this automatic way can play a positive role in reducing the artificial workload of the interpreters and the cost of monitoring a large-region area. It is encouraged to employ this method in a variety of applications, such as illegal construction land monitoring, land use/cover map update, and city planning.
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