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 Research database of burnt areas over Russia based on high resolution remote sensing data

2021 ◽  
Author(s):  
A.V. Kashnitskii ◽  
I.V. Balashov ◽  
I.A. Saigin ◽  
F.V. Stytsenko ◽  
E.A. Loupian
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Forest Fires ◽  
Method Development ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Research Database ◽  
Sensing Data ◽  
Fully Automatic ◽  
Burnt Areas

The paper presents the sample database of vegetation cover damaged by wildfires, obtained from high spatial resolution remote sensing data (up to 10 meters per pixel). At the time of publication, more than 6 thousand fires with a total area of more than 12 million ha were mapped and confirmed with the focus on forest fires. The database covers the period from 2009 to 2020 and is constantly being updated. The presented database may be of interest for various scientific wildfire researches and can be used as training basis for a fully automatic high-resolution fire mapping method development.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

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.

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