The Experts below are selected from a list of 39222 Experts worldwide ranked by ideXlab platform
Francisco J Escobedo - One of the best experts on this subject based on the ideXlab platform.
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a community based urban Forest Inventory using online mapping services and consumer grade digital images
International Journal of Applied Earth Observation and Geoinformation, 2010Co-Authors: Amr Abdelrahman, Mary E Thornhill, Michael G Andreu, Francisco J EscobedoAbstract:Abstract Community involvement in gathering and submitting spatially referenced data via web mapping applications has recently been gaining momentum. Urban Forest Inventory data analyzed by programs such as the i-Tree ECO Inventory method is a good candidate for such an approach. In this research, we tested the feasibility of using spatially referenced data gathered and submitted by non-professional individuals through a web application to augment urban Forest Inventory data. We examined the use of close range photogrammetry solutions of images taken by consumer-grade cameras to extract quantitative metric information such as crown diameter, tree heights and trunk diameters. Several tests were performed to evaluate the accuracy of the photogrammetric solutions and to examine their use in addition to existing aerial image data to supplement or partially substitute for standard i-Tree ECO field measurements. Digital images of three sample sites were acquired using different consumer-grade cameras. Several photogrammetric solutions were performed using the acquired image sets. Each model was carried out using a relative orientation process followed by baseline model scaling. Several distances obtained through this solution were compared to the corresponding distances obtained through direct measurements in order to assess the quality of the model scaling approach. Measured i-Tree ECO field plot Inventory data, online aerial image measurements and photogrammetric observations were compared. The results demonstrate the potential for using aerial image digitizing in addition to ground images to assist in participatory urban Forest Inventory efforts.
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A community-based urban Forest Inventory using online mapping services and consumer-grade digital images.
International Journal of Applied Earth Observation and Geoinformation, 2010Co-Authors: Amr Abd-elrahman, Michael G Andreu, Mary E Thornhill, Francisco J EscobedoAbstract:Community involvement in gathering and submitting spatially referenced data via web mapping appli- cations has recently been gaining momentum. Urban Forest Inventory data analyzed by programs such as the i-Tree ECO Inventory method is a good candidate for such an approach. In this research, we tested the feasibility of using spatially referenced data gathered and submitted by non-professional individuals through a web application to augment urban Forest Inventory data. We examined the use of close range photogrammetry solutions of images taken by consumer-grade cameras to extract quantitative metric information such as crown diameter, tree heights and trunk diameters. Several tests were performed to evaluate the accuracy of the photogrammetric solutions and to examine their use in addition to existing aerial image data to supplement or partially substitute for standard i-Tree ECO field measurements. Digital images of three sample sites were acquired using different consumer-grade cameras. Several photogrammetric solutions were performed using the acquired image sets. Each model was carried out using a relative orientation process followed by baseline model scaling. Several distances obtained through this solution were compared to the corresponding distances obtained through direct measure- ments in order to assess the quality of the model scaling approach. Measured i-Tree ECO field plot Inventory data, online aerial image measurements and photogrammetric observations were compared. The results demonstrate the potential for using aerial image digitizing in addition to ground images to assist in participatory urban Forest Inventory efforts. Published by Elsevier B.V.
Alexandre Piboule - One of the best experts on this subject based on the ideXlab platform.
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single tree species classification from terrestrial laser scanning data for Forest Inventory
Pattern Recognition Letters, 2013Co-Authors: Ahlem Othmani, Lew Lew Yan F C Voon, Christophe Stolz, Alexandre PibouleAbstract:Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the Forestry domain for Forest Inventory, the development of software tools for the automatic measurement of Forest Inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.
Fang Qiu - One of the best experts on this subject based on the ideXlab platform.
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individual tree segmentation from lidar point clouds for urban Forest Inventory
Remote Sensing, 2015Co-Authors: Caiyun Zhang, Yuhong Zhou, Fang QiuAbstract:The objective of this study is to develop new algorithms for automated urban Forest Inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban Forest Inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban Forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.
Ahlem Othmani - One of the best experts on this subject based on the ideXlab platform.
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single tree species classification from terrestrial laser scanning data for Forest Inventory
Pattern Recognition Letters, 2013Co-Authors: Ahlem Othmani, Lew Lew Yan F C Voon, Christophe Stolz, Alexandre PibouleAbstract:Due to the increasing use of Terrestrial Laser Scanning (TLS) systems in the Forestry domain for Forest Inventory, the development of software tools for the automatic measurement of Forest Inventory attributes from TLS data has become a major research field. Numerous research work on the measurement of attributes such as the localization of the trees, the Diameter at Breast Height (DBH), the height of the trees, and the volume of wood has been reported in the literature. However, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. Most of the research work uses Airborne Laser Scanning (ALS) data and measures tree species attributes on large scales. In this paper we propose a method for individual tree species classification of five different species based on the analysis of the 3D geometric texture of the bark. The texture features are computed using a combination of the Complex Wavelet Transforms (CWT) and the Contourlet Transform (CT), and classification is done using the Random Forest (RF) classifier. The method has been tested using a dataset composed of 230 samples. The results obtained are very encouraging and promising.
Steen Magnussen - One of the best experts on this subject based on the ideXlab platform.
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Forest Inventory inference with spatial model strata
Scandinavian Journal of Forest Research, 2020Co-Authors: Steen Magnussen, Thomas Nord-larsenAbstract:In design-based model assisted inference from data gathered in a large area Forest Inventory under a probability sampling design, one should anticipate spatial heterogeneity in the regression coeff...
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spatially explicit large area biomass estimation three approaches using Forest Inventory and remotely sensed imagery in a gis
Sensors, 2008Co-Authors: Michael A. Wulder, Joanne C White, Richard A Fournier, Joan E Luther, Steen MagnussenAbstract:Forest Inventory data often provide the required base data to enable the large area mapping of biomass over a range of scales. However, spatially explicit estimates of above-ground biomass (AGB) over large areas may be limited by the spatial extent of the Forest Inventory relative to the area of interest (i.e., inventories not spatially exhaustive), or by the omission of Inventory attributes required for biomass estimation. These spatial and attributional gaps in the Forest Inventory may result in an underestimation of large area AGB. The continuous nature and synoptic coverage of remotely sensed data have led to their increased application for AGB estimation over large areas, although the use of these data remains challenging in complex Forest environments. In this paper, we present an approach to generating spatially explicit estimates of large area AGB by integrating AGB estimates from multiple data sources; 1. using a lookup table of conversion factors applied to a non-spatially exhaustive Forest Inventory dataset (R 2 = 0.64; RMSE = 16.95 t/ha), 2. applying a lookup table to unique combinations of land cover and vegetation density outputs derived from remotely sensed data (R 2 = 0.52; RMSE = 19.97 t/ha), and 3. hybrid mapping by augmenting Forest Inventory AGB estimates with remotely sensed AGB
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sampling methods remote sensing and gis multiresource Forest Inventory
2006Co-Authors: Michael Kohl, Steen Magnussen, Marco MarchettiAbstract:Forest Inventories - an Overview.- Forest Mensuration.- Sampling in Forest Surveys.- Remote Sensing.- Geographic and Forest Information Systems.- Multiresource Forest Inventory.
