*Article* **Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery**

**Chunying Ren 1, Hailing Jiang 2, Yanbiao Xi 1,\*, Pan Liu <sup>1</sup> and Huiying Li <sup>3</sup>**


**\*** Correspondence: xiyb@smail.nju.edu.cn; Tel.: +86-(209)-50270604

**Abstract:** Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (*λ*), Shannon (*H* ), and Pielou (*J* ), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R<sup>2</sup> = 0.76) models outperformed the other models (R<sup>2</sup> of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.

**Keywords:** forest diversity; GEDI LiDAR; Sentinel-2; machine Learning

Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery. *Remote Sens.* **2023**, *15*, 375. https://doi.org/10.3390/ rs15020375

**Citation:** Ren, C.; Jiang, H.; Xi, Y.; Liu, P.; Li, H. Quantifying Temperate

Academic Editors: Kenji Omasa, Shan Lu and Jie Wang

Received: 20 November 2022 Revised: 18 December 2022 Accepted: 4 January 2023 Published: 7 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
