Dynamic Characteristics of Vegetation Change Based on Re-constructed Heterogenous NDVI in Seismic Regions

The need to protect forests and enhance the capacity of mountain ecosystems is highlighted in the U.N.’s Sustainable Development Goal (SDG) 15 (https;//UNSTATS.UN.ORG/SDGS/REPORT/2022). The worst-hit areas of the 2008 Wenchuan Earthquake in southwest China are mountainous regions with high biodiversity and the impacted area is typical of other montane regions in the need for detecting vegetation changes following the impacts of catastrophes. While the widely used remotely sensed vegetation indicator, NDVI is available from various satellite data sources, , but these satellites are available for different moni-toring periods and durations Combining these datasets has been challenging to make a continuous characterization of vegetation change over an extended time period. In this study, compared with linear regression, multiple linear regression, random forest, Convolutional Neural Networks (CNNs) performed best with the average R2 of 0.819 (leave-one-out cross-validation). Thus, the CNNs model was selected to establish the map of the overlapping periods of two remote sensing products: SPOT-VGT NDVI and PROBA-V NDVI, to reconstruct a SPOT-VGT NDVI for the peri-od from June 2014 to December 2018 in the worst-hit areas of the Wenchuan earthquake. . We analyzed the original and reconstructed SPOT-VGT NDVI in the hard-hit areas of the Wenchuan earthquake from 1999 to 2018, and we concluded that NDVI showed an overall upward trend throughout the study period, but experienced a sharp decline in 2008 and reached its lowest value a year later (2009). Vegetation recovery was rapid from 2009 until 2011 after which, returned to pattern of slower natural growth (2012 to 2018). The Longmenshan fault zone experienced the greatest vegetation damage and initiation of recovery there has lagged the overall regional aver-age recovery by 1 to 2 years. In areas where the land was denuded of vegetation (i.e., effectively all vegetation was stripped from the surface) after the earthquake, the damage exceeded what was experienced anywhere else in the entire study area, and by 2018, it remained unrestored. In the 15 years since the earthquake, the areas that were denuded were expected to recover to the level of restoration equivalent with the NDVI of 2007 , as was the case in other earthquake dam-aged regions. In addition to the earthquake and the immediate loss of vegetation, the Chinese government’s Grain for Green Policy, the elevation ranges within the region, the forest’s pheno-logical conditions and human activities all had an impact on vegetation recovery and restoration. The reconstructed NDVI provides a long-term continuous record, which are contributing to the identifying changes that are improving predictive forest recovery models and to better vegetation management following catastrophic disturbances, such as earthquakes.