Tree species classification for clarification of forest inventory data using Sentinel-2 images

2019 ◽  
Author(s):  
Anna Denisova ◽  
Ludmila Kavelenova ◽  
Evgeniy Korchikov ◽  
Nataly Prokhorova ◽  
Daria Terentyeva ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Inventory Data ◽  
Species Classification ◽  
Forest Inventory Data ◽  
Tree Species Classification ◽  
Sentinel 2

Forest Biomass Estimation Based on Forest Inventory Data in Middle and Last Scales

2011 ◽  
Vol 183-185 ◽  
pp. 220-224
Author(s):  
Ming Ze Li ◽  
Wen Yi Fan ◽  
Ying Yu
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Forest Biomass ◽  
Biomass Estimation ◽  
Total Biomass ◽  
Inventory Data ◽  
Biomass Equation ◽  
Tree Species Composition ◽  
Forest Inventory Data ◽  
Ecosystem Carbon

The forest biomass (which is referred to the arbor aboveground biomass in this research) is one of the most primary factors to determine the forest ecosystem carbon storages. There are many kinds of estimating methods adapted to various scales. It is a suitable method to estimate forest biomass of the farm or the forestry bureau in middle and last scales. First each subcompartment forest biomass should be estimated, and then the farm or the forestry bureau forest biomass was estimated. In this research, based on maoershan farm region, first the single tree biomass equation of main tree species was established or collected. The biomass of each specie was calculated according to the materials of tally, such as height, diameter and so on in the forest inventory data. Secondly, each specie’s biomass and total biomass in subcompartment were calculated according to the tree species composition in forest management investigation data. Thus the forest biomass spatial distribution was obtained by taking subcompartment as a unit. And last the forest total biomass was estimated.

scholarly journals Significant differences and curvilinearity in the self-thinning relationships of 11 temperate tree species assessed from forest inventory data

2011 ◽  
Vol 69 (2) ◽  
pp. 195-205 ◽  
Author(s):  
Marie Charru ◽  
Ingrid Seynave ◽  
François Morneau ◽  
Michaël Rivoire ◽  
Jean-Daniel Bontemps
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
The Self ◽  
Inventory Data ◽  
Forest Inventory Data

scholarly journals Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

2019 ◽  
Vol 8 (3) ◽  
pp. 150 ◽  
Author(s):  
Joongbin Lim ◽  
Kyoung-Min Kim ◽  
Ri Jin
Keyword(s):  
Machine Learning ◽  
Tree Species ◽  
North Korea ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Species Classification ◽  
Tree Species Classification ◽  
Hyperion Data ◽  
Study Sites ◽  
Sentinel 2

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

Single tree species classification from Terrestrial Laser Scanning data for forest inventory

2013 ◽  
Vol 34 (16) ◽  
pp. 2144-2150 ◽  
Author(s):  
Ahlem Othmani ◽  
Lew F.C. Lew Yan Voon ◽  
Christophe Stolz ◽  
Alexandre Piboule
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Species Classification ◽  
Single Tree ◽  
Tree Species Classification

Selecting tree species for testing climate change migration hypotheses using forest inventory data

2010 ◽  
Vol 259 (4) ◽  
pp. 778-785 ◽  
Author(s):  
C.W. Woodall ◽  
C.M. Oswalt ◽  
J.A. Westfall ◽  
C.H. Perry ◽  
M.D. Nelson ◽  
...  
Keyword(s):  
Climate Change ◽  
Tree Species ◽  
Forest Inventory ◽  
Inventory Data ◽  
Forest Inventory Data
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