scholarly journals Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-26
Author(s):  
Ruiliang Pu
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Method Development ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Airborne Lidar ◽  
Accurate Information ◽  
Natural Forests ◽  
Future Directions ◽  
The Status

Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.

scholarly journals Hyperspectral data in water stress detection

2014 ◽  
Vol 20 (3-4) ◽  
Author(s):  
P. Riczu ◽  
A. Nagy ◽  
J. Tamás
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Tree Species ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Hyperspectral Data ◽  
Reflectance Spectra ◽  
Stress Detection ◽  
The Status ◽  
Remote Sensing Methods

Remote sensing methods are applied widespread to investigate large land fields. Within these methods the status of certain vegetation can be determined based on the reflectance spectra of the chlorophyll, in order to support agriculture, forestry and the evaluation of soil pollution. The main aims of our study were to determine and validate the reflectance spectra of fruit tree species, in order to facilitate the identification and evaluation of stressed fruit trees in orchards.

Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral Data

2016 ◽  
Vol 44 (4) ◽  
pp. 595-603 ◽  
Author(s):  
Yuanyong Dian ◽  
Yong Pang ◽  
Yanfang Dong ◽  
Zengyuan Li
Keyword(s):  
Tree Species ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Urban Tree ◽  
Species Mapping

scholarly journals Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data

2020 ◽  
Vol 12 (5) ◽  
pp. 2144
Author(s):  
Jeroen Degerickx ◽  
Martin Hermy ◽  
Ben Somers
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Green Infrastructure ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Green Spaces ◽  
Human Society ◽  
Urban Green ◽  
Sensing Data

Urban green spaces are known to provide ample benefits to human society and hence play a vital role in safeguarding the quality of life in our cities. In order to optimize the design and management of green spaces with regard to the provisioning of these ecosystem services, there is a clear need for uniform and spatially explicit datasets on the existing urban green infrastructure. Current mapping approaches, however, largely focus on large land use units (e.g., park, garden), or broad land cover classes (e.g., tree, grass), not providing sufficient thematic detail to model urban ecosystem service supply. We therefore proposed a functional urban green typology and explored the potential of both passive (2 m-hyperspectral and 0.5 m-multispectral optical imagery) and active (airborne LiDAR) remote sensing technology for mapping the proposed types using object-based image analysis and machine learning. Airborne LiDAR data was found to be the most valuable dataset overall, while fusion with hyperspectral data was essential for mapping the most detailed classes. High spectral similarities, along with adjacency and shadow effects still caused severe confusion, resulting in class-wise accuracies <50% for some detailed functional types. Further research should focus on the use of multi-temporal image analysis to fully unlock the potential of remote sensing data for detailed urban green mapping.

scholarly journals Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

2021 ◽  
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Barjeece Bashir ◽  
Yiyu Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Learning Algorithm ◽  
Google Earth ◽  
Accurate Information ◽  
Natural Forests ◽  
Source Information ◽  
Forestry Management ◽  
Google Earth Engine ◽  
Total Forest Area

Abstract Background Accurate information on tree species is much in demand for forestry management and further investigations on biodiversity and forest ecosystem services. Over regional or large areas, discriminating tree species at high resolution is deemed challenging by lack of representative features and computational power. Methods A novel methodology to delineate the explicit spatial distribution of dominated six tree species (Pinus, Quercus, Betula, Populus, Larch, and Apricot) and one residual class using the analysis-ready large volume multi-sensor imagery within Google Earth Engine (GEE) platform is demonstrated and used to map a 10 m classification with detail analysis of spatial pattern for an area covering over 90,000 km 2 between 41° N and 45° N. Random Forest (RF) algorithm built into GEE was used for tree species mapping, together with the multi-temporal features extracted from Sentinel-1/2 and topographic imagery data. The composition of tree species in natural forests and plantations in city and county-level were performed in detail afterwards. Results The proposed model achieved a reliable overall agreement (77.5%, 0.71 kappa), and the detailed analysis on the spatial distributing of targeted species indicated that the plantations (Pinus, Populus, Larch, and Apricot) outnumber natural forests (Quercus and Betula) by 6%, and they were mainly grown in the northern and southern regions, respectively. Moreover, Arhorchin had the largest total forest area of over 4,500 km 2 , while Hexingten and Aohan ranked first in natural forest and plantation area, and the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. Conclusions It is our belief that combined multi-source information of the machine learning algorithm within cloud platforms is beneficial to map a reliable spatial tree species over large areas on a fine scale. High-resolution tree species information based on online tools could be more easily considered for practical forestry management and further studies on forest ecosystems.

scholarly journals Two Decades Progress on The Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 739
Author(s):  
Enoch Gyamfi-Ampadu ◽  
Michael Gebreslasie
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aboveground Biomass ◽  
Tree Species ◽  
Forest Cover ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Natural Forests ◽  
Sensing Applications ◽  
Remote Sensing Applications

Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

A Model for Management of Large-Area Crop Rotation

2014 ◽  
Vol 496-500 ◽  
pp. 2775-2778
Author(s):  
Ling Sun ◽  
Ze Sheng Zhu
Keyword(s):  
Remote Sensing ◽  
Crop Rotation ◽  
Satellite Remote Sensing ◽  
Jiangsu Province ◽  
Accurate Information ◽  
Remote Sensing Images ◽  
Large Area ◽  
Model Based ◽  
The Status

A model to describe an effective statistic index of crop rotation level is particularly appealing for management of large-area crop rotation, because the statistic index of crop rotation level is easy to provide fast and accurate information of large-area crop rotation. We experimentally realized such a statistic index of crop rotation level using a model based on data from satellite remote sensing images. The statistic index of crop rotation level describes the status of large-area crop rotation with a statistic period or frequency. Our analysis indicated that the statistic index of crop rotation level was mediated by processing the remote sensing images of rice and cotton. Taken together with the demonstrated computation of the statistic index of crop rotation level of XingHua City, Jiangsu Province, China, our results establish the application feasibility of the statistic index of crop rotation level in management of large-area crop rotation.

scholarly journals Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 565
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Robert Shea Duerler ◽  
Xinwei Yang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Tree Species ◽  
Regional Distribution ◽  
Google Earth ◽  
Pinus Tabulaeformis ◽  
Accurate Information ◽  
Natural Forests ◽  
Distribution Models ◽  
Google Earth Engine ◽  
Populus Spp

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

scholarly journals Individual Tree Classification Using Airborne LiDAR and Hyperspectral Data in a Natural Mixed Forest of Northeast China

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 303 ◽  
Author(s):  
Dan Zhao ◽  
Yong Pang ◽  
Lijuan Liu ◽  
Zengyuan Li
Keyword(s):  
Tree Species ◽  
Northeast China ◽  
Dominant Species ◽  
Mixed Forest ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Support Vector ◽  
Individual Tree ◽  
Individual Trees ◽  
Better Than

This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction (“crown-based ITC” for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results (“pixel-based ITC” for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%.

scholarly journals A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

2021 ◽  
Vol 13 (3) ◽  
pp. 353
Author(s):  
Maja Michałowska ◽  
Jacek Rapiński
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Laser Scanning ◽  
Discrimination Performance ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Support Vector ◽  
Species Discrimination ◽  
Species Classification ◽  
Waveform Data

Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.

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