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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Dection and Health Analysis of Individual Tree in Urban Environment with Multi-Sensor Platform
摘要： With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.
关键词： point cloud,hyperspectral,spectrum,LiDAR,individual tree detection,health monitoring
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Individual Tree Detection from Multi-View Satellite Images
摘要： Individual tree detection is critical in forest monitoring and inventory. In this paper, we propose a novel method to use multi-view satellite images to detect individual trees and delineate their crowns. As compared to previous methods that only use image information, we generate the DSM from the multi-view high-resolution satellite images and combine it with the spectral information to detect the trees. Firstly, the vegetation areas are extracted to remove the non-vegetation objects while terrain areas are extracted to help estimate the tree height. Then, we utilize top-hat morphological operation to efficiently find the local maximal points as tree tops and further refine them by checking their heights and doing non-maximum suppression. Finally, we use a revised superpixel segmentation algorithm to delineate the tree crowns which considered both 2D spectral and 3D structure similarities. To effectively assess the performance, we rigorously match and evaluate the detected and reference trees in a one-to-one relationship. A quantitative evaluation at three different sites shows that the proposed method is able to detect individual trees at different regions with high accuracy.
关键词： Remote Sensing,DSM,Individual Tree Detection,Superpixel,Multi-view Satellite Image
更新于2025-09-23 15:22:29
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Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning
摘要： Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.
关键词： norway spruce,GEOMON,allometric equation,tree height,individual tree detection,diameter at breast height,LiDAR
更新于2025-09-16 10:30:52
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A Single-Tree Processing Framework Using Terrestrial Laser Scanning Data for Detecting Forest Regeneration
摘要： Direct assessment of forest regeneration from remote sensing data is a previously little-explored problem. This is due to several factors which complicate object detection of small trees in the understory. Most existing studies are based on airborne laser scanning (ALS) data, which often has insufficient point densities in the understory forest layers. The present study uses plot-based terrestrial laser scanning (TLS) and the survey design was similar to traditional forest inventory practices. Furthermore, a framework of methods was developed to solve the difficulties of detecting understory trees for quantifying regeneration in temperate montane forest. Regeneration is of special importance in our montane study area, since large parts are declared as protection forest against alpine natural hazards. Close to nature forest structures were tackled by separating 3D tree stem detection from overall tree segmentation. In support, techniques from 3D mathematical morphology, Hough transformation and state-of-the-art machine learning were applied. The methodical framework consisted of four major steps. These were the extraction of the tree stems, the estimation of the stem diameters at breast height (DBH), the image segmentation into individual trees and finally, the separation of two groups of regeneration. All methods were fully automated and utilized volumetric 3D image information which was derived from the original point cloud. The total amount of regeneration was split into established regeneration, consisting of trees with a height > 130 cm in combination with a DBH < 12 cm and unestablished regeneration, consisting of trees with a height < 130 cm. Validation was carried out against field-based expert estimates of percentage ground cover, differentiating seven classes that were similar to those used by forest inventory. The mean absolute error (MAE) of our method for established regeneration was 1.11 classes and for unestablished regeneration only 0.27 classes. Considering the metrical distances between the class centres, the MAE amounted 8.08% for established regeneration and 2.23% for unestablished regeneration.
关键词： TLS,forestry,3D image segmentation,understory,tree detection,tree stems,DBH
更新于2025-09-11 14:15:04
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Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi-spectral satellite data
摘要： There is an increasing demand for clove products, mainly dried buds and essential oil on global markets. Consequently, the importance of clove trees as a provisioning service is increasing at the local level, particularly for smallholders cultivating clove trees as cash crops. Due to limited availability of data on local production, using remote sensing-based methods to quantify today's clove production is of key interest. We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems, including pasture and clove, clove plantation and agroforestry systems. We implemented an individual tree detection method based on two machine learning approaches. Specifically, we proposed using a circular Hough transform (CHT) for the automated detection of individual clove trees. Subsequently, we implemented a tree species classification method using a random forests (RF) classifier based on a set of features extracted for relevant trees in the above production systems. Finally, we classified and mapped different production systems. Based on the number of detected clove trees growing in a clove production system, we estimated the production system-dependent clove bud yield. Our results show that 97.9% of all reference clove trees were detected using a CHT. Classifying clove and non-clove trees resulted in a producer accuracy of 70.7% and a user accuracy of 59.2% for clove trees. The classification of the clove production systems resulted in an overall accuracy of 77.9%. By averaging different clove tree yield estimates obtained from the literature, we estimated an average total yield of approximately 575 tons/year for our 25,600 ha study area. With this approach, we demonstrate a first step towards large-scale clove bud yield estimation using remote sensing data and methodologies.
关键词： Random forest,Tree species classification,Very high-resolution satellite image,Pléiades satellite,LULC classification,Single tree detection,Circular Hough transform,Clove bud yield estimation
更新于2025-09-09 09:28:46