Keiko Ioki

Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data

We evaluated the effectiveness of integrating discrete return light detection and ranging (LiDAR) data with high spatial resolution near-infrared digital imagery for object-based classification of land cover types and dominant tree species. In particular we adopted LiDAR ratio features based on pulse attributes that have not been used in past studies. Object-based classifications were performed first on land cover types, and subsequently on dominant tree species within the area classified as trees. In each classification stage, two different data combinations were examined: LiDAR data integrated with digital imagery or digital imagery only. We created basic image objects and calculated a number of spectral, textural, and LiDAR-based features for each image object. Decision tree analysis was performed and important features were investigated in each classification. In the land cover classification, the overall accuracy was improved to 0.975 when using the object-based method and integrating LiDAR data. The mean height value derived from the LiDAR data was effective in separating “trees” and “lawn” objects having different height. As for the tree species classification, the overall accuracy was also improved by object-based classification with LiDAR data although it remained up to 0.484 because spectral and textural signatures were similar among tree species. We revealed that the LiDAR ratio features associated with laser penetration proportion were important in the object-based classification as they can distinguish tree species having different canopy density. We concluded that integrating LiDAR data was effective in the object-based classifications of land cover and dominant tree species.

Estimation of leaf area index and gap fraction in two broad-leaved forests by using small-footprint airborne LiDAR

In this study, we evaluated methods for reliably estimating leaf area index (LAI) and gap fraction in two different types of broad-leaved forests by the use of airborne light detection and ranging (LiDAR) data. We evaluated 13 estimation variables related to laser height, laser penetration rate, and laser point attributes that were derived from LiDAR analyses. The relationships between LiDAR-derived estimates and field-based measurements taken from the forests were evaluated with simple linear regressions. The data from the two forests were analyzed separately and as an integrated dataset. Among the laser height variables, the coefficient of variation (CV) of all laser point heights had the highest level of accuracy for estimating both LAI and gap fraction. However, we recommend that more evaluations be conducted prior to the use of CV in forests with complex structures. The simplest laser penetration variable, which represents the ratio of the number of ground points to the total number of all points (PALL), also had a high level of accuracy for estimating LAI and gap fraction at the study sites regardless of whether the data were analyzed separately or as an integrated data set. Furthermore, PALL values showed near 1:1 relationships with the field-based gap fraction values. Hence, the use of PALL may be the most practical for estimating LAI and gap fraction in broad-leaved forests, even when the canopies are heavily closed.

Estimation of Above-Ground Biomass of a Tropical Forest in Northern Borneo Using High-resolution Satellite Image

Abstract Estimating above-ground biomass is important in establishing an applicable methodology of Measurement, Reporting and Verification (MRV) System for Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+). We developed an estimation model of diameter at breast height (DBH) from IKONOS-2 image that led to above-ground biomass estimation (AGB). The IKONOS image was preprocessed with dark object subtraction and topographic effect correction prior to watershed segmentation for tree crown delineation. Compared to the field observation, the overall segmentation accuracy was 64%. Crown detection percent had a strong negative correlation to tree density. In addition, satellite-based crown area had the highest correlation with the field measured DBH. We then developed the DBH allometric model that explained 74% of the data variance. In average, the estimated DBH was very similar to the measured DBH as well as for AGB. Overall, this method can potentially be applied to estimate AGB over a relatively large and remote tropical forest in Northern Borneo.Key Words: tree crown delineation, biomass estimation, IKONOS-2

Inter-annual growth of broad-leaved canopy estimated from the repeated LiDAR measurements

We estimated inter-annual growth of broad-leaved trees planted in the urban park, by using multi-temporal airborne LiDAR datasets acquired in 2004, 2008, and 2010. The annual changes in the LiDAR-estimated growth of average canopy heights were significantly correlated with one another over the study periods at the plot and individual-tree levels. A moderate and significant relation was shown with the increment of field-measured basal area. But the uncertainties remain in estimating short-term growth for small crown areas.