Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks

Abstract

As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of~65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach.

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

“…This statement is derived from one of the most used approaches to define the accuracy of the classified data, expressed in the form of the confusion matrix and/or contingency matrix [36]. Through this analysis, it is possible to generate information on overall accuracy, user's and producer's accuracy, and Kappa coefficient (k) [24,59,60]. The confusion matrix is a cross-tabulation, which correlates the LCLU classes in rows and columns, with reference or test data (usually represented by columns) being compared with classified or training data (usually represented by lines) [58].…”

Mapping Forest Vertical Structure in Sogwang-ri Forest from Full-Waveform Lidar Point Clouds Using Deep Neural Network

“…Remote sensing imagery, including optical aerial photography and satellite images, enables time-series forest mapping for areas where fieldwork cannot be conducted [14]. Forest vertical structures in a wide area have been classified mainly using superficial information with in situ data [15,16]. Nevertheless, it has the limitation of obtaining information only about the canopy layer and is unable to directly acquire data about the internal structure of the forest because of sensor characteristics that cannot penetrate through forest cover [17].…”

“…In order to apply the models, research based on remote sensing data including spatial characteristics is mainly being conducted, and various types of images such as radar and optics are being used [35,36]. However, most general images contain only the surface information of the forest and accordingly, a study based on FW LiDAR data is required to estimate the internal property information of the forest [15,23]. Therefore, in this study, a deep neural network (DNN), one of well-known deep learning methods was adopted based on FW LiDAR data to estimate and classify the spatial distribution of the forest vertical structure.…”

The Development and Application of a GIS-Based Tool to Assess Forest Landscape Restoration Effects on Water Conservation Capacity

“…Forest restoration has been widely applied worldwide to recover ecosystem functions of damaged or degraded land. Given that water conservation is one of the vital ecosystem services provided by forests, improving the forest water conservation capacity is often one of the key objectives in forest landscape restoration [1][2][3][4][5][6][7]. A quick, accurate assessment of the effects of different forest restoration measures on water conservation capacity can be used to identify the key drivers for the recovery of targeted forests and can help forest managers to develop the best management practices for forest restoration.…”