Seongjin Yoo

Estimating Crown Variables of Individual Trees Using Airborne and Terrestrial Laser Scanners

In this study, individual tree height (TH), crown base height (CBH), crown area (CA) and crown volume (CV), which were considered as essential parameters for individual stem volume and biomass estimation, were estimated by both an airborne laser scanner (ALS) and a terrestrial laser scanner (TLS). These ALS- and TLS-derived tree parameters were compared because TLS has been introduced as an instrument to measure objects more precisely. ALS-estimated TH was extracted from the highest value within a crown boundary delineated with the crown height model (CHM). The ALS-derived CBH of individual trees was estimated by k-means clustering method using the ALS data within the boundary. The ALS-derived CA was calculated simply with the crown boundary, after which CV was computed automatically using the crown geometric volume (CGV). On the other hand, all TLS-derived parameters were detected manually and precisely except the TLS-derived CGV. As a result, the ALS-extracted TH, CA, and CGV values were underestimated whereas CBH was overestimated when compared with the TLS-derived parameters. The coefficients of determination (R2) from the regression analysis between the ALS and TLS estimations were approximately 0.94, 0.75, 0.69 and 0.58, and root mean square errors (RMSEs) were approximately 0.0184 m, 0.4929 m, 2.3216 m2 and 13.2087 m3 for TH, CBH, CA and CGV, respectively. Thereby, the error rate decreased to 0.0089, 0.0727 and 0.0875 for TH, CA and CGV via the combination of ALS and TLS data.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.

Changes in the distribution of South Korean forest vegetation simulated using thermal gradient indices.

To predict changes in South Korean vegetation distribution, the Warmth Index (WI) and the Minimum Temperature of the Coldest Month Index (MTCI) were used. Historical climate data of the past 30 years, from 1971 to 2000, was obtained from the Korea Meteorological Administration. The Fifth-Generation National Center for Atmospheric Research (NCAR) /Penn State Mesoscale Model (MM5) was used as a source for future climatic data under the A1B scenario from the Special Report on Emission Scenario (SRES) of the Intergovernmental Panel on Climate Change (IPCC). To simulate future vegetation distribution due to climate change, the optimal habitat ranges of Korean tree species were delimited by the thermal gradient indices, such as WI and MTCI. To categorize the Thermal Analogy Groups (TAGs) for the tree species, the WI and MTCI were orthogonally plotted on a two-dimensional grid map. The TAGs were then designated by the analogue composition of tree species belonging to the optimal WI and MTCI ranges. As a result of the clustering process, 22 TAGs were generated to explain the forest vegetation distribution in Korea. The primary change in distribution for these TAGs will likely be in the shrinkage of areas for the TAGs related to Pinus densiflora and P. koraiensis, and in the expansion of the other TAG areas, mainly occupied by evergreen broad-leaved trees, such as Camellia japonica, Cyclobalanopsis glauca, and Schima superba. Using the TAGs to explain the effects of climate change on vegetation distribution on a more regional scale resulted in greater detail than previously used global or continental scale vegetation models.

Assessment of land-cover change using GIS and remotely-sensed data: A case study in Ain Snoussi area of northern Tunisia

Understanding the patterns of land-cover change for biodiversity and ecology system function has been important in landscape ecology. The objective of this study was to analyze land-cover change in the Ain Snoussi area of northern Tunisia. Landsat MSS/4 and SPOT HRV/3 images were used for the analysis. To classify land-cover type into forest and non-forest area, pixel-based classification and maximum likelihood algorithm were applied to two imageries using supervised classification algorithm. After classification of images, each changed area was calculated. Thereby, analysis of distance roads and topographic factors such as elevation, slope, aspect, and Topographic Wetness Index (TWI) were performed. The results showed that the area changed into non-forest was slightly larger than that into forest. Moreover, most of the changed areas, approximately half of the total changed area, were distributed near the roads. In addition, the change from forest to non-forest area tends to have a negative and positive relationship respectively with elevation and slope. On the other hand, the change from non-forest to forest area showed the tendency to be negative in elevation, slope, and TWI. However, the slope aspect of study area did not have any particular relationship with change tendency. In conclusion, spatial pattern of land-cover change was influenced by the distance from roads and topographic characteristics of target area.