Assessing Land Cover and Ecological Quality Changes in the Forest-Steppe Ecotone of the Greater Khingan Mountains, Northeast China, from Landsat and MODIS Observations from 2000 to 2018

Abstract

Land cover changes are the main factors driving the evolution of regional ecological quality. These changes must be considered in the strategic formulation of regional or national ecological policies. The forest-steppe ecotone in the Greater Khingan Mountains is an important ecological barrier in northern China. To measure the effect of ecological protection in recent years, Landsat images, object-oriented image segmentation, and convolutional neural networks were used to create land cover datasets of the forest-steppe ecotone. The Carnegie–Ames–Stanford approach (CASA) and the dimidiate pixel model were used to derive net primary productivity (NPP) and fractional vegetation cover (FVC) to assess the ecological quality of this area. The results showed that only grassland and urban land increased, whereas saline–alkali land and desert areas initially increased and then decreased from 2010 to 2018, indicating that the desertification process was substantially curbed. Total NPP increased by 26.3% (2000–2010) and 10.8% (2010–2018). However, NPP decreased slightly in the center of the study area. FVC first decreased and then increased, and the increased areas were concentrated in the forest-steppe ecotone, saline–alkali land, and desert zone in Xin Barag Left Banner. These observations indicate that the ecological quality has gradually improved due to the strict protection of forest and grassland resources and the suppression of desertification. Our results provide potential insights for land use planning and the development of environmental protection measures in the forest-steppe ecotone.

Dynamic Changes in Vegetation Ecological Quality in the Tarim Basin and Its Response to Extreme Climate during 2000–2022

“…In fact, noise frequently impacts remote-sensing images, which makes it hard for them to be up to standard in real-world environments. In the calculation process, the gray value of the image element at the 5th and 95th percentiles is typically used to calculate the FVC in desert areas, taking into account the environmental characteristics of arid regions [54,55]. In this study, in combination with previous research experience, the first 5% of the NDVIV was chosen as the NDVI soil , while the NDVI value corresponding to the latter 95% was selected as NDVI veg .…”

Determining Changes in Mangrove Cover Using Remote Sensing with Landsat Images: a Review

“…That is why, lately, a new type of object-oriented classification is increasingly used for the classification of high-resolution imagery (Mokhtar et al, 2022). This method is able to extract an object from satellite images to differentiate the trees of the canopy according to their shape and species and to map the land cover of mangroves (Shi et al, 2022).…”

Yield estimation of summer maize based on multi‐source remote‐sensing data

“…The CASA can predict crop yield at different scales (Fang et al., 2021); its parameters are easy to obtain, and most of the parameters required in the model can be extracted from remote‐sensing data, which largely overcomes the drawback of the lack of information from ground stations. The multi‐temporal, high spatial resolution, and high temporal resolution of remote‐sensing products make the CASA model realize NPP dynamic monitoring (H. Yang, Zhong, et al., 2021), inversion of the spatial distribution of NPP, and analysis of vegetation or crop growth (F. Shi et al., 2022; Wan et al., 2022).…”