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.

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.

Remote sensing and GIS forest landscape assessment for sustainable forest management

2021 ◽  
Vol 17 (1) ◽  
pp. 12-26
Author(s):  
A.F. Chukwuka ◽  
A. Alo ◽  
O.J. Aigbokhan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Markov Chain Model ◽  
Remote Sensing Data ◽  
Chain Model ◽  
Spatial Metrics ◽  
Forest Reserve ◽  
Land Cover Types

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environment

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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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