Exploring Variation in α-Biodiversity in Mangrove Forests Following Long-Term Restoration Activities: A Remote Sensing Perspective

Abstract

Highlights: What are the main findings? Integration of LiDAR and Worldview-2 data with Gaussian process regression effectively predicts mangrove Shannon’s diversity index. Mangrove α-diversity increased with the age of forest restoration; mangrove areas with long-established plantations exhibit richer biodiversity. What are the implications of the main findings? This study provides a reliable remote sensing approach for long-term monitoring and assessment of mangrove biodiversity. Future mangrove restoration should emphasize multispecies planting to enhance ecosystem stability. Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R2 of 0.51, followed by SVM (R2 = 0.44) and DNN (R2 = 0.32); the accuracy of XGBoost (R2 = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km2, 0.13 km2, and 1.35 km2, respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community.