The effect of topographic correction (TOC) has a profound influence on the quantitative application of remote sensing image. With regard to the invalid evaluation of the TOC model with such a single topographic correction assessment (TCA) method, we have selected five TCA indexes from five different perspectives: the difference in mean radiance radiometry between sunlit and shaded slopes, the cosine empirical relationship, stability, heterogeneity, and outlier ratio. The entropy weight method was used to assign weight to each TCA indexes, and the comprehensive evaluation value (CEV) of TOC for each band of remote sensing image was obtained by weighted superposition. After that, the weight of each band of the remote sensing image is determined by the entropy weight method, and the CEV of the TOC of the remote sensing image is obtained by weighting and superposition, so as to realize the effect evaluation of the six TOC models of C, SCS + C, VECA, Teillet, Minnaert, and Minnaert + SCS. The results indicate that the proposed method can effectively evaluate the correction effect of the TOC model. Results indicate that the SCS + C model has the best correction effect, while the Minnaert model performs the worst. The results generated from the Minnaert + SCS, Teillet, and Minnaert models typically show inferior quality. The SCS + C, VECA, and C models are better suited for generating images with high spectral fidelity, and these three correction models are recommended for TOCs over mountainous areas.
Carbon emissions and consequent climate change directly affect the sustainable development of ecological environment systems and human society, which is a pertinent issue of concern for all countries globally. The construction of a carbon emission inversion model has significant theoretical importance and practical significance for carbon emission accounting and control. Established carbon emission models usually adopt socio-economic parameters or energy statistics to calculate carbon emissions. However, high-precision estimates of carbon emissions in administrative regions lacking energy statistics are difficult. This problem is especially prominent in small-scale regions. Methods to accurately estimate carbon emissions in small-scale regions are needed. Based on nighttime light remote-sensing data and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, combined with the environmental Kuznets curve, this paper proposes an ISTIRPAT (Improved Stochastic Impacts by Regression on Population, Affluence, and Technology) model. Through the improved STIRPAT model (ISTIRPAT) and panel data regression, provincial carbon emission inventory data were downscaled to the municipal level, and municipal scale carbon emission inventories were obtained. This study took the 17 cities and prefectures of Hubei Province, China, as an example to verify the accuracy of the model. Carbon emissions for 17 cities and prefectures from 2012 to 2018 calculated from the original STIRPAT model and the ISTIRPAT model were compared with real values. The results show that using the ISTIRPAT model to downscale the provincial carbon emission inventory to the municipal level, the inversion accuracy reached 0.9, which was higher than that of the original model. Overall, carbon emissions in Hubei Province showed an upward trend. Regarding the spatial distribution, the main carbon emission area was formed in the central part of Hubei Province as a ring-shaped mountain peak. The lowest carbon emissions in the central area expanded outward, increased, and gradually decreased to the edge of the province. The overall composition of carbon emissions in eastern Hubei was higher than those in western Hubei.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.