Identification and delineation of individual tree crowns using Lidar and multispectral data fusion

Gulbe, Linda

doi:10.1109/igarss.2015.7326522

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The template matching method developed in this study involves generating templates from the target data. In other studies on ITD using template matching, templates were created using simulation models (Gulbe and Mednieks 2013;Holmgren and Lindberg 2013;Nyström et al 2014;Pirotti 2010) or by selecting sample trees manually in the image (Gulbe and Mednieks 2013). Gulbe and Mednieks reported detection rates of 56% and 71%, respectively, using these two types of templates.…”

Section: Discussionmentioning

confidence: 99%

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

Huo

Lindberg

2020

International Journal of Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 99%

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

Huo

Lindberg

2020

International Journal of Remote Sensing

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“…A 2 m buffer around mature trees (>17 m) was incorporated to keep pixels that are part of the same crown but that have smaller heights; those less than 7 m in height were eliminated and used as a mask for further processing [16,29]. Preprocessed CHM is usually utilized for ITC segmentation [15,21,[91][92][93]. Various spatial filters with different window sizes (two or four pixels) and shapes (rectangular or circular) were tested, and the one that best fit the tree crowns visually was selected.…”

Section: Tree Crown Segmentation From Fused Datamentioning

confidence: 99%

“…The first technique generally gives good results, but it is time-consuming, complex and requires advanced LiDAR sensors [17]. The second technique has been studied much more, both at the stand level [18,19] and at the tree level [20][21][22], as there are a variety of algorithms that provide rapid ITC segmentation, which gives satisfactory results [14,16,23].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

2020

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Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

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“…The assess procedure for recognizable proof and outline of individual tree crowns, utilizing Lidar and multispectral information combination. Linda G [6] developed techniques actualizing information combination depend on picture binarization (thresholding) what's more, area developing division calculation. Results were contrasted and format coordinating strategy for multispectral information and locale developing calculation utilizing only one information source.…”

Section: Literature Surveymentioning

confidence: 99%

Extraction of Texture Features from Panchromatic Images

P.¹,

Krishna²

2019

IJRTE

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A Framework for Delineation of Trees is used to detect the trees of the urban area and it is a desirable framework in urban are management. Delineation and detection of trees from satellite images is important for the estimation of the trees of the urban and rural area. In existing literature, most of the studies only deal with particular tree crown or tree species detection. There is no particular framework for detecting major tree species of urban area collectively from visually distinguishable satellite images and also there is a major problem of distinguishing trees from their shadows. In this paper we used the texture-based feature extraction methods and proposed new algorithms to extract simple texture features from the panchromatic images. The proposed methodology is divided into three phases. Pre-processing was done in first phase to minimize the unnecessary deformations. Quality of Image is increased by eliminating the dark spots and shadows etc. in second phase image segmentation was done to identify the image boundaries and objects in the image. Finally, in the third phase, a framework for delineation of trees was done using image enhancement and segmentation algorithms. The ultimate purpose that the knowledge obtained from the study is to developing a framework that can process shadows and then effectively detect and extract trees from satellite images.

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Identification and delineation of individual tree crowns using Lidar and multispectral data fusion

Cited by 4 publications

References 7 publications

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

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

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Extraction of Texture Features from Panchromatic Images

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