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 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 Estimation of forest cover restoration damaged by Siberian silkmoth using remote sensing data and maps

2019 ◽  
Vol 75 ◽  
pp. 02005
Author(s):  
Elena Fedotova
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Tree Species ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Deciduous Forests ◽  
Sensing Data ◽  
Current State

The current state of the land cover has been estimated in the territories where in different years (1885, 1955, 1995) the forests were damaged by Siberian silkmoth. Dark-needle taiga is restored through the change of tree species. In 20 years in areas of dark-needle taiga there are graminoid communities, in 60 years we have deciduous forests there, and in 130 - dark needle forests, but not everywhere.

Spectral Variability Analysis of In-Situ Hyperspectral Remote Sensing at Leaf and Branch Scales for Tree Species at Tropical Urban Forest

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
W. C. Chew ◽  
A. M. S. Lau ◽  
K. D. Kanniah ◽  
N. H. Idris
Keyword(s):  
Remote Sensing ◽  
Spectral Range ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Spectral Variability ◽  
Variability Analysis ◽  
Sensing Data ◽  
Spectral Ranges

Spectral variability analysis has been carried out on in-situ hyperspectral remote sensing data for 20 tree species available in tropical forest in Malaysia. Five different spectral ranges have been tested to evaluate the influence of intra-species spectral variability at specific spectral range given by different spatial scales (i.e. leaf to branch scales). The degree of intra-species spectral variability was not constant among different spectral ranges where the influence of spatial scale towards intra-species spectral variability at these spectral ranges was found increasing from leaf to branch scale. The ratio of leaves to non-photosynthetic tissues has made branch scale significantly influent the intra-species spectral variability. Results have shown that a specific spectral range was species sensitive on the intra-species and inter-species spectral variability in this study. This study also suggested the use of species sensitive wavelengths extracted from specific spectral range in hyperspectral remote sensing data in order to achieve good accuracy in tree species classification.

scholarly journals Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 68716-68729 ◽  
Author(s):  
Wenzhi Liao ◽  
Frieke Van Coillie ◽  
Lianru Gao ◽  
Liwei Li ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Species Mapping ◽  
Full Waveform Lidar

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

Tree-Species Identification of Multisource Remote-Sensing Data using Improved 3D-CNN

2020 ◽  
Vol 57 (24) ◽  
pp. 242804
Author(s):  
栗旭升 Li Xusheng ◽  
陈冬花 Chen Donghua ◽  
刘赛赛 Liu Saisai ◽  
张乃明 Zhang Naiming ◽  
李虎 Li Hu
Keyword(s):  
Remote Sensing ◽  
Species Identification ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
3D Cnn

Utility of hyperspectral remote sensing data in estimating biomass and structure variables in boreal forest of Finland

2020 ◽  
Author(s):  
Eelis Halme ◽  
Petri Pellikka ◽  
Matti Mõttus
Keyword(s):  
Remote Sensing ◽  
Boreal Forest ◽  
Tree Species ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Stem Biomass ◽  
Forestry Industry ◽  
Multispectral Remote Sensing

<p>Three-quarters of Finland’s land surface area (22.8 million hectares) is filled with forests. The role of remote sensing in large area inventories is crucial. The forests of Finland serve as an important resource for the nation’s nature conservation as well as for the forestry industry. Furthermore, forests are significant carbon sinks and play a great role in climate change mitigation. Research on vegetation parameter retrieval is of special relevance in order to extend our knowledge about the vegetation dynamics and terrestrial carbon stocks at regional and global scales.</p><p>In future in addition to multispectral satellites, hyperspectral satellite missions will start to provide remote sensing data to support the needs of forestry and other natural resource management practices. We investigated the influence of spectral and spatial resolution of remote sensing data on retrieval of biomass and other forest properties. The study contributed to better information productivity on forest variables in boreal forest ecosystem.</p><p>We used the remote sensing data by Sentinel-2 (10 bands, resolution 10 m) and hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m). As reference data, we used new forest resource dataset provided by the Finnish Forest Centre and additional independent in situ measurements. We applied kernel-based regression methods to relate the forest variables of interest with the remotely sensed data. Based on recent studies, we selected Gaussian process regression (GPR) and support vector regression (SVR), which have proven to work well with hyperspectral and multispectral remote sensing data. Regression estimations were performed for stem biomass, basal area, mean height, leaf area index (LAI) and main tree species. The estimation accuracies were examined with absolute and relative root-mean-square errors.</p><p>Successful forest variable estimations showed that kernel-based regression algorithms are suitable tools for quantification of forest structure and assessment of its change. The estimation accuracies between the two algorithms were similar. However, the faster SVR algorithm was found to be more practical, especially considering large scale mapping and future near real-time applications. Based on the study results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as basal area or stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.</p>

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