Linking urban tree inventories to remote sensing data for individual tree mapping

2021 ◽  
Vol 61 ◽  
pp. 127106
Author(s):  
Luke Wallace ◽  
Qian (Chayn) Sun ◽  
Bryan Hally ◽  
Samuel Hillman ◽  
Alan Both ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Individual Tree ◽  
Urban Tree ◽  
Sensing Data

scholarly journals Mapping the Urban Atmospheric Carbon Stock by LiDAR and WorldView-3 Data

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 692
Author(s):  
MD Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Andrea Galli ◽  
Giuseppe Modica ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Tree Species ◽  
Carbon Stock ◽  
Dominant Species ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Lidar Data ◽  
Urban Tree ◽  
Sensing Data

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.

scholarly journals Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6227 ◽  
Author(s):  
Michele Dalponte ◽  
Lorenzo Frizzera ◽  
Damiano Gianelle
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Data Science ◽  
Remote Sensing Data ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Sensing Data ◽  
Tree Species Classification ◽  
Selection Of

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

scholarly journals A STUDY OF USING FULLY CONVOLUTIONAL NETWORK FOR TREETOP DETECTION ON REMOTE SENSING DATA

2018 ◽  
Vol IV-1 ◽  
pp. 163-169 ◽  
Author(s):  
C. Xiao ◽  
R. Qin ◽  
X. Huang ◽  
J. Li
Keyword(s):  
Remote Sensing ◽  
Inventory Management ◽  
Remote Sensing Data ◽  
Training Data ◽  
Surface Model ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Individual Tree ◽  
Fully Convolutional Network ◽  
Sensing Data

<p><strong>Abstract.</strong> Individual tree detection and counting are critical for the forest inventory management. In almost all of these methods that based on remote sensing data, the treetop detection is the most important and essential part. However, due to the diversities of the tree attributes, such as crown size and branch distribution, it is hard to find a universal treetop detector and most of the current detectors need to be carefully designed based on the heuristic or prior knowledge. Hence, to find an efficient and versatile detector, we apply deep neural network to extract and learn the high-level semantic treetop features. In contrast to using manually labelled training data, we innovatively train the network with the pseudo ones that come from the result of the conventional non-supervised treetop detectors which may be not robust in different scenarios. In this study, we use multi-view high-resolution satellite imagery derived DSM (Digital Surface Model) and multispectral orthophoto as data and apply the top-hat by reconstruction (THR) operation to find treetops as the pseudo labels. The FCN (fully convolutional network) is adopted as a pixel-level classification network to segment the input image into treetops and non-treetops pixels. Our experiments show that the FCN based treetop detector is able to achieve a detection accuracy of 99.7<span class="thinspace"></span>% at the prairie area and 66.3<span class="thinspace"></span>% at the complicated town area which shows better performance than THR in the various scenarios. This study demonstrates that without manual labels, the FCN treetop detector can be trained by the pseudo labels that generated using the non-supervised detector and achieve better and robust results in different scenarios.</p>

Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data

2017 ◽  
Vol 200 ◽  
pp. 170-182 ◽  
Author(s):  
Luxia Liu ◽  
Nicholas C. Coops ◽  
Neal W. Aven ◽  
Yong Pang
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Urban Tree ◽  
Sensing Data

scholarly journals A digital mapping method for linking high-resolution remote sensing images to individual tree crowns

2018 ◽  
Author(s):  
Sarah Graves ◽  
Justin Gearhart ◽  
T Trevor Caughlin ◽  
Stephanie Bohlman
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Ecological Studies ◽  
Ecological Data ◽  
Individual Tree ◽  
Field Mapping ◽  
Sensing Data ◽  
Individual Trees

Remote sensing data provides unique information about the Earth’s surface that can be used to address ecological questions. Linking high-resolution remote sensing data to field-based ecological data requires methods to identify objects of interest directly on georeferenced remote sensing digital images while in the field. Mapping individual trees with a GPS often has location error and is focused on the position of the tree stem rather than the crown, often creating a mismatch between field data and the pixel information. We describe a mapping process that uses a consumer-grade GPS and tablet computer to spatially match individual trees measured in the field directly to a digital image of their crowns taken from above the canopy. This paper outlines the reasons for using digital field mapping and a summary of the equipment and process, with supplemental material providing a detailed field protocol. As more remote sensing data with a resolution capable of resolving individual trees become available, the opportunities to leverage these data for ecological studies grow. We provide guidelines for those wanting to apply imagery to expand the spatial scale and extent of ecological studies.

scholarly journals A digital mapping method for linking high-resolution remote sensing images to individual tree crowns

2018 ◽  
Author(s):  
Sarah Graves ◽  
Justin Gearhart ◽  
T Trevor Caughlin ◽  
Stephanie Bohlman
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Ecological Studies ◽  
Ecological Data ◽  
Individual Tree ◽  
Field Mapping ◽  
Sensing Data ◽  
Individual Trees

Remote sensing data provides unique information about the Earth’s surface that can be used to address ecological questions. Linking high-resolution remote sensing data to field-based ecological data requires methods to identify objects of interest directly on georeferenced remote sensing digital images while in the field. Mapping individual trees with a GPS often has location error and is focused on the position of the tree stem rather than the crown, often creating a mismatch between field data and the pixel information. We describe a mapping process that uses a consumer-grade GPS and tablet computer to spatially match individual trees measured in the field directly to a digital image of their crowns taken from above the canopy. This paper outlines the reasons for using digital field mapping and a summary of the equipment and process, with supplemental material providing a detailed field protocol. As more remote sensing data with a resolution capable of resolving individual trees become available, the opportunities to leverage these data for ecological studies grow. We provide guidelines for those wanting to apply imagery to expand the spatial scale and extent of ecological studies.

scholarly journals CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 2629-2634
Author(s):  
Z. Wang ◽  
J. Wu ◽  
Y. Wang ◽  
X. Kong ◽  
H. Bao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Tree Crown ◽  
Species Classification ◽  
Individual Tree ◽  
Sensing Data ◽  
Tree Species Classification ◽  
Shape Characteristics

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3&amp;thinsp;&amp;times;&amp;thinsp;3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA&amp;thinsp;=&amp;thinsp;85.12&amp;thinsp;%, Kc&amp;thinsp;=&amp;thinsp;0.90) performed better than LiDAR-metrics method (OA&amp;thinsp;=&amp;thinsp;79.86&amp;thinsp;%, Kc&amp;thinsp;=&amp;thinsp;0.81) and spectral-metircs method (OA&amp;thinsp;=&amp;thinsp;71.26, Kc&amp;thinsp;=&amp;thinsp;0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.

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