Application of high-resolution airborne data using individual tree crowns in Japanese conifer plantations

The original publication is available at www.springerlink.comArticleJOURNAL OF FOREST RESEARCH. 14(1):10-19 (2009)journal articl

Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images

International audienceLocal tree density may vary in young Eucalyptus plantations under the effects of environmental conditions or inadequate management, and these variations need to be mapped over large areas as they have a significant impact on the final biomass harvested. High spatial resolution optical satellite images have the potential to provide crucial information on tree density at an affordable cost for forest management. Here, we test the capacity of this promising technique to map the local density of young and small Eucalyptus trees in a large plantation in Brazil. We use three Worldview panchromatic images acquired at a 50 cm resolution on different dates corresponding to trees aged 6, 9 and 13 months and define an overall accuracy index to evaluate the quality of the detection results. The best agreement between the local densities obtained by visual detection and by marked point process modeling was found at 9 months, with only small omission and commission errors and a stable 4% underestimation of the number of trees across the density gradient. We validated the capability of the MPP approach to detect trees aged 9 months by making a comparison with local densities recorded on 112 plots of ~590 m² and ranging between 1360 and 1700 trees per hectare. We obtained a good correlation (r²=0.88) with a root mean square error of 31 trees/ha. We generalized detection by computing a consistent map over the whole plantation. Our results showed that local tree density was not uniformly distributed even in a well-controlled intensively managed Eucalyptus plantation and therefore needed to be monitored and mapped. Use of the marked point process approach is then discussed with respect to stand characteristics (canopy closure), acquisition dates and recommendations for algorithm parameterization

Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial

Effect of airborne laser scanning accuracy on forest stock and yield estimates

The main objective of the study was to assess the magnitude of uncertainty of airborne laser scanning (ALS) -based forest inventory data in forest net present value (NPV) computations. A starting point was the current state of change in operative forest-planning in which traditional standwise field inventories (SWFI) are being replaced by area-based ALS inventories (A_ALS). The more detailed objectives were as follows: 1) to investigate the significance of the accuracy of current (SWFI, A_ALS) and future (ALS individual tree detection (ITD)) forest inventory methodologies applied in the timing of simulated loggings and in NPV computations, 2) to compare the forest-planning inventory methods currently applied with respect to the accuracy of the timber assortment information derived, 3) to investigate the sources of uncertainty related to the estimation of timber assortment volumes and economic values in forest management-planning simulations and 4) to compare the uncertainty related to inventory accuracy, growth models and timber price development in NPV computations at the stand- and forest property-level, using various interest rates. The study was carried out, using empirical and simulated forest inventory data, forest management-planning calculations and Monte Carlo simulations. It was shown that forest inventory errors led to significant mistiming of simulated loggings and subsequent prominent losses in simulated NPV. The most significant source of error in the prediction of timber assortment outturns was SWFI and A_ALS inventory error. The errors related to stem distribution generation, stem form prediction and bucking simulation were significant but considerably lower in magnitude than the inventory error. A_ALS interpretation led to accuracy levels similar to or better than that of SWFI. At the stand-level the growth models used in forest-planning simulation computations were the greatest source of uncertainty with respect to NPVs computed throughout the rotation period. Uncertainty almost as great was caused by A_ALS and SWFI data uncertainty, while the uncertainty caused by fluctuation in timber prices was considerably lower in magnitude. Forest property level deals with a considerably lesser degree of NPV deviation than does stand-level: A_ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1-7.5% relative deviation in property-level NPV when an interest rate of 3% was applied. A_ALS inventory error-related uncertainty resulted in significant bias in property-level NPV estimates. The study forms a basis for developing practical methodologies for taking uncertainty into account in forest property valuation