Spatiotemporal Changes of Vegetation Growth and Its Influencing Factors in the Huojitu Mining Area from 1999 to 2023 Based on kNDVI  
Zhichao Chen  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Yiqiang Cheng  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Zhenyao Zhu  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Zhenyao Zhu  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Zhou  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Zhenyao Zhu  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.C. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (Z.Z. ); [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (C.H.) Zhou  
School of Surveying and Engineering Information, Henan Polytechnic University (HPU), Jiaozuo 454003, China; [EMAIL_ADDRESS] (Y.  
scarcity. This exerts a detrimental effect on the ecosystem. Furthermore, the national coal mining strategy of relocating westward and concentrating operations, combined with high-intensity mining methods, has substantially disrupted the surface ecological environment in the western mining region. As a result, the already fragile ecological environment is increasingly deteriorating [2]. The negative impacts of coal resource development on the ecological environment continue to be of great concern to society. Large-scale coal mining causes phenomena such as surface subsidence and ground cracks and changes in topography, hydrology, and soil and thus leads to changes in vegetation cover [3,4]. Therefore, scientifically and accurately monitoring vegetation growth and exploring its influencing factors are urgent scientific problems that need to be addressed in order to achieve sustainable development in mining areas. Vegetation plays an important role in regulating ecosystems [5], prompting ground water recharge [6], nutrient cycling, and carbon regularization [7,8]. Vegetation monitoring is important to evaluate ecological and environmental changes and their dynamics [9, 10, 11, 12]. At present, the most commonly used methods to monitor the change of vegetation growth in mining areas are vegetation index and remote sensing [13, 14, 15]. NDVI is a dominant vegetation index commonly used to study Spatiotemporal changes of vegetation growth both in China and globally [16, 17, 18, 19]. However, the susceptibility of NDVI to soil background and its tendency to saturate in areas of high vegetation cover resulted in its reduced sensitivity to changes in vegetation density [20, 21, 22]. Therefore, [PERSON] et al. [23] used machine learning principles, applied the kernel method theory for the extraction and calculation of NDVI, and proposed a new vegetation index, namely the kNDVI.