With the advent of the remote sensing big data era, the approach to extracting remote sensing information has shifted from single-image processing to an integrated method that combines spatio-temporal-spectral fusion. In other words, this method emphasizes a "large-scale and fine-grained" processing strategy. At the large-scale level, the complexity of the geographical environment presents challenges for remote sensing imaging, such as "same spectrum, different objects" and "same object, different spectra". Proper zoning can effectively reduce the heterogeneity within regional units, thereby improving the accuracy of remote sensing image classification. At the fine-grained level, remote sensing imaging captures subtle changes in surface features, but the inherent "macro pattern, micro complexity" of geographical environments, lacking top-down global regulation, often leads to significant uncertainty and cognitive biases when relying solely on bottom-up classification from remote sensing data. To address this, the paper proposes a multi-scale remote sensing geographical zoning framework designed to resolve the differences in scale and representation between geographical patterns and remote sensing imaging across macro, meso, and micro levels. Through application examples at these three levels, the study demonstrates that appropriate zoning not only enhances the accuracy of remote sensing information extraction but also enriches the variety of extracted attributes, thereby improving the precision of industry-specific remote sensing applications.

Forests play a key role in maintaining the carbon balance of terrestrial ecosystems, protecting biodiversity, and conserving soil and water. Monitoring forest cover and dynamics is one of the crucial tasks under the "UN Decade on Ecosystem Restoration" and the "UN Sustainable Development Goals". Because of the significant differences in the ecological effects of woody and herbaceous vegetation in forests, distinguishing the area fraction between tree and grass is important. However, it is challenging to quantify the slow and cumulative increase in tree cover during forest recovery. Using China's National Forest Inventory (NFI) as a reference, this study compares 14 types of remote sensing data to analyze the most effective ones in depicting China's forest cover and its changes, including 8 types of hard classification data on land cover and 6 types of tree cover. Results show that high variability existed among various types of forest data in spatial distribution. High uncertainty regions in detecting forest cover among all the 14 datasets were concentrated in regions with tree cover less than 30%, such as western China and the North China Plain. The coefficients of variation in about 96% of pixels in these regions were greater than 0.56. Compared to hard classification data, tree cover data is more consistent with China's NFI. Only a weak gain in China's forests since 2000 is detected by the hard classification data, whereas both NFI and tree cover data indicated significant gains. Among the tree cover data we compared, GLOBMAP data agreed best with provincial-level NFI in detecting forest change (k = 0.78). The findings suggest that tree cover data is able to serve as a reliable data in depicting forest recovery.