Jong Yeol Lee

Unconstrained approach for isolating individual trees using high-resolution aerial imagery

This study outlines an algorithm that can be used for individual tree detection and crown delineation; it was applied to coniferous forest using aerial imagery. This article explains the assumptions and processes involved in the algorithm, presents the results of the applications, and discusses possible limitations. The algorithm, which adopts contextual analysis that excludes the need to specify window size, was applied to detect and delineate individual trees based on morphological and reflective characteristics. The preprocessing steps included suppression of the non-coniferous area (i.e. non-forest and leaf-off deciduous forest) and the creation of appropriately smoothed imagery using an optimal smoothing level based on accuracy index (AI); thereafter, unconstrained directional peak- and edge-finding algorithms were processed separately. To assess the tree detection and crown delineation processes, the results of the algorithms were evaluated carefully against visually interpreted crowns in six square plots using several statistical measures based on tree top correspondence, positional difference of tree top, directional crown width, and crown area assessment. The average tree top correspondence had an AI of 88.83%. The positional difference between detected and visually interpreted tree tops was measured and its average was 0.6 m. For our 0.5 m/pixel aerial imagery, the average root mean square error (RMSE) of crown width in six sample plots was found to be 2.8 m, and crown area estimation resulted in RMSE of approximately 9.23 m2 (23.25%). In general, this study highlights the potentiality of the proposed algorithm to efficiently and automatically acquire forest information such as tree numbers, crown width, and crown area.

Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

The importance of estimating forest volume has been emphasized by increasing interest on carbon sequestration and storage which can be converted from volume estimates. With importance of forest volume, there are growing needs for developing efficient and unbiased estimation methods for forest volume using reliable data sources such as the National Forest Inventory (NFI) and supplementary information. Therefore, this study aimed to develop a forest plot volume model using selected explanatory variables from each data type (only Forest Type Map (FTM), only airborne LiDAR and both datasets), and verify the developed models with forest plot volumes in 60 test plots with the help of the NFI dataset. In linear regression modeling, three variables (LiDAR height sum, age, and crown density class) except diameter class were selected as explanatory independent variables. These variables generated the four forest plot volume models by combining the variables of each data type. To select an optimal forest plot volume model, a statistical comparing process was performed between four models. In verification, Model no. 3 constructed by both FTM and airborne LiDAR was selected as an optimal forest plot volume model through comparing root mean square error (RMSE) and coefficient of determination (R 2). The selected best performance model can predict the plot volume derived from NFI with RMSE and R 2 at 50.41 (m3) and 0.48, respectively.

RGB-NDVI color composites for monitoring the change in mangrove area at the Maubesi Nature Reserve, Indonesia

The Maubesi Nature Reserve (MNR) is a protected lowland area in eastern Indonesia that mainly consists of mangrove forest. The objective of this paper was to demonstrate a simple technique to visualize and quantify the change in mangrove area using a 3-year dataset of Landsat TM images acquired in 1989, 2003 and 2009. The normalized difference vegetation index (NDVI) was calculated to determine high and low vegetation biomass in each image. Each NDVI extracted by Landsat image in 1989, 2003 and 2009 was assigned to red, green and blue (RGB) color, respectively, and then combined to make color composites. Additive color theory was applied to interpret mangrove changes within the MNR area on the RGB-NDVI color composite. Changed areas were quantified by performing an unsupervised classification on the RGB-NDVI image with 45 classes that were grouped into eight major mangrove change categories. An Indonesian land cover map was used to assess the accuracy of the classified image. The result showed that 77.13% of the MNR area was unchanged and 22.87% of the MNR area changed over 20 years (1989–2009).