The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 OLI multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.

The Changtang Nature Reserve, located in the hinterland of the Qinghai-Tibet Plateau, plays a crucial role in researching ecological and environmental assessment on the plateau. However, the severe natural conditions in the Changtang Plateau have resulted in the absence of meteorological observation stations within the reserve, thereby leading to a lack of fundamental ecological and environmental research data. Remote sensing technology presents an opportunity for ecological monitoring in the Changtang Nature Reserve. In this study, remote sensing ecological indices (RSEI) were utilized to evaluate the ecological environment of the reserve from 2000 to 2020. The MODIS data reconstructed using the Savitzky-Golay filter on the Google Earth Engine (GEE) platform were employed. Principal component analysis was then conducted to construct the RSEI. The results reveal that the overall ecological environment quality in the Changtang Nature Reserve between 2000 and 2020 was relatively poor. Over the past two decades, the mean RSEI of the reserve exhibited a fluctuating trend of decrease and increase, indicating a deteriorating and subsequently improving ecological environment quality. Specifically, during the period of 2000–2010, the RSEI mean decreased from 0.3197 to 0.2269, suggesting degradation of the ecological environment, and the proportion of areas classified as fair and poor increased by 51.99%, while the proportion of areas classified as good and excellent decreased by 32.69%. However, from 2010 to 2020, it increased from 0.2269 to 0.3180, indicating an improvement in the ecological environment, and the proportion of areas classified as good and excellent increased by 6.11%, while the proportion of areas classified as fair and poor decreased by 2.91%. Spatially, the core zone demonstrated higher ecological environment quality compared to the experimental and buffer zones. The findings of this study provide comprehensive and accurate information about the ecological environment, which supports management, decision-making, and emergency response efforts in the Changtang Nature Reserve. Moreover, it offers a scientific basis for conservation and sustainable development strategies in the reserve. The quantitative assessment of the ecological environment dynamics contributes to the understanding of the reserve’s ecological dynamics and facilitates informed decision-making for effective conservation and management practices.