Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Located in the Mount Tai state-owned forest farm, this study adopted Landsat multispectral remote sensing data in 2000 and 2016 on the GEE (Google Earth Engine) platform and selected four phases of images each year according to the phenological period. By dealing with the current situation map of forestry resources in 2000 and the field survey data in 2016, the samples of tree species distribution in 2000 and 2016 were obtained. On the basis of topographic correction with the empirical rotation model, this study used the random forest (RF) classifier to classify tree species from remote sensing images in 2000 and 2016, achieving high classification accuracy. The results showed that, after 16 years of evolution, the percentage of pine species in the forest decreased from 55.69% to 50.22%, with a percentage decrease as high as 5.47%. The percentage of black locust (Robinia pseudoacacia) increased from 10.15% in 2000 to 13.75% in 2016, with an increase of 3.60%. Quercus also had a positive growth in the area. This result reflected the expansion of black locust.

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

Forests ◽

10.3390/f12060692 ◽

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.

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Do species distribution models explain spatial structure within tree species ranges?

Global Ecology and Biogeography ◽

10.1111/j.1466-8238.2009.00478.x ◽

2009 ◽

Vol 18 (6) ◽

pp. 662-673 ◽

Cited By ~ 21

Author(s):

Daniel Montoya ◽

Drew W. Purves ◽

Itziar R. Urbieta ◽

Miguel A. Zavala

Keyword(s):

Spatial Structure ◽

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Distribution Models ◽

Species Ranges

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Floristic Relationships of Seasonally Dry Forests of Eastern South America Based on Tree Species Distribution Patterns

Neotropical Savannas and Seasonally Dry Forests - Systematics Association Special Volumes ◽

10.1201/9781420004496.ch7 ◽

2006 ◽

pp. 159-192 ◽

Cited By ~ 1

Author(s):

Maria Jesus Nogueira Rodal ◽

João AndréJarenkow ◽

Ary Oliveira-Filho

Keyword(s):

South America ◽

Species Distribution ◽

Tree Species ◽

Distribution Patterns ◽

Dry Forests ◽

Seasonally Dry

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Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques

Transactions of the ASABE ◽

10.13031/trans.14419 ◽

2021 ◽

Vol 64 (5) ◽

pp. 1611-1624

Author(s):

Worasit Sangjan ◽

Sindhuja Sankaran

Keyword(s):

Remote Sensing ◽

Structure From Motion ◽

Tree Species ◽

Tree Height ◽

Tree Canopy ◽

System Structure ◽

List Type ◽

Canopy Architecture ◽

Chemical Application ◽

Canopy Volume

HighlightsTree canopy architecture traits are associated with its productivity and management.Understanding these traits is important for both precision agriculture and phenomics applications.Remote sensing platforms (satellite, UAV, etc.) and multiple approaches (SfM, LiDAR) have been used to assess these traits.3D reconstruction of tree canopies allows the measurement of tree height, crown area, and canopy volume.Abstract. Tree canopy architecture is associated with light use efficiency and thus productivity. Given the modern training systems in orchard tree fruit systems, modification of tree architecture is becoming important for easier management of crops (e.g., pruning, thinning, chemical application, harvesting, etc.) while maintaining fruit quality and quantity. Similarly, in forest environments, architecture can influence the competitiveness and balance between tree species in the ecosystem. This article reviews the literature related to sensing approaches used for assessing architecture traits and the factors that influence such evaluation processes. Digital imagery integrated with structure from motion analysis and both terrestrial and aerial light detection and ranging (LiDAR) systems have been commonly used. In addition, satellite imagery and other techniques have been explored. Some of the major findings and some critical considerations for such measurement methods are summarized here. Keywords: Canopy volume, LiDAR system, Structure from motion, Tree height, UAV.

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Forest vegetation in western Romania in relation to climate variables: Does community composition reflect modelled tree species distribution?

Annals of Forest Research ◽

10.15287/afr.2016.692 ◽

2016 ◽

Vol 59 (2) ◽

Cited By ~ 1

Author(s):

Steffi Heinrichs ◽

Helge Walentowski ◽

Erwin Bergmeier ◽

Karl Heinz Mellert ◽

Adrian Indreica ◽

...

Keyword(s):

Community Composition ◽

Species Distribution ◽

Tree Species ◽

Forest Vegetation ◽

Climate Variables ◽

Western Romania

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

PeerJ ◽

10.7717/peerj.6227 ◽

2019 ◽

Vol 6 ◽

pp. e6227 ◽

Cited By ~ 12

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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