Forests are an important part of terrestrial ecosystems. As urbanization progresses and climate change intensifies, the world's forests are increasingly disturbed. Many forestry policies and regulations have been introduced around the world to increase forest area and focus on restoring and improving the structure and function of disturbed forest ecosystems. Assessment of forest restoration requires the use of indicators with ecological implications to meet the needs of forest restoration from area assessment to quality assessment.
Currently, assessing the functioning of forest ecosystems is a challenging undertaking. In order to develop comprehensive evaluation indicators with ecological significance and apply them to forest management, the Institute of Urban Environment, Chinese Academy of Sciences established a hybrid evaluation method based on ecosystem structure-function-habitat. The method is based on the normalized burn ratio (NBR) spectral index, combined with local forest resource survey data, Landsat and LiDAR data. The team's visual interpretation of historical images of Google Earth shows that the total accuracy of the hybrid method is 0.94, and the output of the hybrid model increases with the increase of the biodiversity index. In order to simplify the model and extend the application of spatiotemporal sequences, the team further established a random forest model based on tree age (R2 = 0.78, RMSE = 0.14), and the application of the forest ecological restoration index obtained by the random forest model in time is reflected in the fact that it can easily monitor the rate of forest recovery, so as to predict the time of regional ecological recovery, and then estimate the implementation cycle of the policy of closing mountains for afforestation. The spatial application is reflected in the fact that sufficient recovery resources can be allocated in areas where recovery is slow. This work provides the scientific basis for forest managers to develop more effective forest restoration plans.
The research results are titled An Approach Integrating Multi-Source Data with LandTrendr Algorithm for Refining Forest Recovery Detection, published in Remote sensing. The research work has been supported by the National Key Research Program, the National Natural Science Foundation of China, the National Social Science Foundation of China, the Science and Technology Department of Fujian Province, the Chinese Academy of Sciences and Xiamen City.
▲Figure 1. Hybrid approach technology roadmap
▲Figure 2. Distribution of forest restoration values for mixed methods. (a) and (b) the size and distribution of recovery values for the hybrid method under 30m and 300m resolution cells, respectively; (c) The three stages of restoration are shown, namely, initial, intermediate and restored forests. The latitude and longitude of the 15 images are the position of the center point.
Source: Institute of Urban Environment, Chinese Academy of Sciences