☆ 4.7 Article

Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2023)

Related references

Note: Only part of the references are listed.

Article Environmental Sciences

Continuous monitoring of forest change dynamics with satellite time series

Mathieu Decuyper et al.

Summary: This study presents a new change detection algorithm that can accurately track deforestation and regrowth of forests using satellite data. The algorithm performs well across different forest types and change conditions, and can be used to assess carbon accumulation and forest restoration targets of secondary forests.

REMOTE SENSING OF ENVIRONMENT (2022)

Add to Collection

Article Forestry

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Fardin Moradi et al.

Summary: This study used Sentinel-2 remote sensing data to estimate the aboveground biomass (AGB) of Carpinus betulus trees in the Hyrcanian forests of northern Iran. By comparing different machine learning methods, it was found that the ANN algorithm performed the best in AGB estimation.

FORESTS (2022)

Add to Collection

Article Environmental Sciences

Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images

Tesfaye Adugna et al.

Summary: The type of algorithm used for remote sensing image classification has a significant impact on accuracy. In this paper, the performance of random forest (RF) and support vector machine (SVM) algorithms in large area land cover mapping using coarse-resolution images was compared. The results showed that RF outperformed SVM, especially in mixed class classification and handling large input datasets.

REMOTE SENSING (2022)

Add to Collection

Article Agronomy

Remotely sensed carotenoid dynamics improve modelling photosynthetic phenology in conifer and deciduous forests

Christopher Y. S. Wong et al.

Summary: This study demonstrates the importance of using PRI and CCI indices to improve the remote monitoring of forest vegetation phenology, showing less variability than traditional meteorological models in estimating the start and end of the growing season.

AGRICULTURAL AND FOREST METEOROLOGY (2022)

Add to Collection

Article Computer Science, Information Systems

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

Vahid Nasiri et al.

Summary: This study examines the potential of integrating Sentinel-1 and Sentinel-2 data to map forest canopy cover in the Mediterranean oak forests of western Iran. The study finds that SVM produces the highest accuracy in mapping forest canopy cover, and the use of a three-year dataset improves the classification ability of all machine learning models.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2022)

Add to Collection

Article Remote Sensing

Estimating estuarine primary production using satellite data and machine learning

Min Xu et al.

Summary: This study makes the first attempt to estimate Net Primary Production (NPP) in an estuarine environment using machine learning algorithms and satellite measurements. By comparing the performance of different algorithms, the best algorithm was identified and used to establish a time-series NPP record for Tampa Bay from 2002 to 2020. The results show that using machine learning algorithms and satellite data can improve NPP estimation and reveal the impact of temperature variations and global warming on estuarine NPP.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2022)

Add to Collection

Review Forestry

Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (case study: Hyrcanian mixed forest)

Vahid Nasiri et al.

Summary: This research successfully demonstrated the potential of using low-cost UAV aerial images to estimate tree height and crown diameter, with high agreement between estimates and field measurements. The results confirmed the accuracy and feasibility of this approach for estimating tree heights and crown diameter.

CANADIAN JOURNAL OF FOREST RESEARCH (2021)

Add to Collection

Article Geography, Physical

Mapping dominant leaf type based on combined Sentinel-1/-2 data - Challenges for mountainous countries

Lars T. Waser et al.

Summary: This study utilized a variety of data sources and machine learning methods to accurately map the distribution of dominant leaf types at a national level in Switzerland, while exploring the impact of complex mountainous topography on model performance. The combined use of predictors from Sentinel-1/Sentinel-2 and digital terrain models reduced issues related to topography and shadows, resulting in higher accuracy.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

Add to Collection

Article Environmental Sciences

Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data

Haoming Wan et al.

Summary: The spatial distribution of forest stands plays a crucial role in understanding and managing forests. The fusion of multiple remote sensing data sources, including high-spatial-resolution images, time-series images, and LiDAR data, is essential for accurately identifying tree species for forest stand classification. The FSP algorithm, based on curve matching, has been developed to effectively fuse and analyze these data sources, outperforming traditional machine learning classification methods in terms of accuracy and stability.

REMOTE SENSING (2021)

Add to Collection

Article Environmental Sciences

Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

Christopher A. Ramezan et al.

Summary: The research explores the impact of training set size on classification algorithm performance. Random forests (RF) demonstrated the highest overall accuracy, while GBM was costly in terms of training time and resources. Other algorithms, such as NEU and SVM, were sensitive to training set size, while LVQ had lower overall accuracy but was insensitive to sample size.

REMOTE SENSING (2021)

Add to Collection

Article Environmental Sciences

Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction

Enoch Gyamfi-Ampadu et al.

Summary: This study evaluated the influence of spectral and spatial resolutions of different satellite images on predicting tree species diversity. The results showed that Sentinel 2 performed the best in diversity prediction, while Landsat 8 had the lowest accuracy.

REMOTE SENSING (2021)

Add to Collection

Article Forestry

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

Bo Xie et al.

Summary: Accurate information on tree species is crucial for forestry management and biodiversity investigations. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species and one residual class. The classification achieved a reliable accuracy, revealing that plantations outnumber natural forests and are mainly concentrated in the northern and southern regions.

FORESTS (2021)

Add to Collection

Article Geography, Physical

Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel

Dario Schulz et al.

Summary: Land use maps are important planning tools for decision makers, especially in challenging sectors like food security and urban planning in regions like the Sahel. Remote sensing imageries are essential for producing such maps, but heterogeneous landscapes in the Sahel can make classification difficult. This study utilized Sentinel-1 and Sentinel-2 archives to create a high resolution land use map for Niamey, Niger and surrounding areas, showcasing the potential of combining multi-modal imageries and multiple classifiers for accurate mapping. The classifier ensemble map showed superior accuracy compared to existing global land cover products, highlighting the benefits of using different classifiers for mapping a heterogenous environment.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

Add to Collection

Review Forestry

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Pablo Torres et al.

Summary: The review paper aims to identify and analyze studies that addressed forest health issues using remote sensing techniques. The study found that the number of papers addressing this issue have consistently increased, most studies placed their study area in North America and Europe, and satellite-borne multispectral sensors are the most commonly used technology.

FORESTS (2021)

Add to Collection

Article Environmental Sciences

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Anita Sabat-Tomala et al.

REMOTE SENSING (2020)

Add to Collection

Article Green & Sustainable Science & Technology

Impacts of Chilean forest subsidies on forest cover, carbon and biodiversity

Robert Heilmayr et al.

NATURE SUSTAINABILITY (2020)

Add to Collection

Article Environmental Sciences

Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example

Lana L. Narine et al.

REMOTE SENSING (2020)

Add to Collection

Review Meteorology & Atmospheric Sciences

Forest Contribution to Climate Change Mitigation: Management Oriented to Carbon Capture and Storage

Leonel J. R. Nunes et al.

CLIMATE (2020)

Add to Collection

Review Engineering, Electrical & Electronic

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Mohammadreza Sheykhmousa et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2020)

Add to Collection

Article Environmental Sciences

Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey

Dirk Pflugmacher et al.

REMOTE SENSING OF ENVIRONMENT (2019)

Add to Collection

Article Environmental Sciences

Forest Stand Species Mapping Using the Sentinel-2 Time Series

Ewa Grabska et al.

REMOTE SENSING (2019)

Add to Collection

Article Environmental Sciences

Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data

Agata Hoscilo et al.

REMOTE SENSING (2019)

Add to Collection

Article Remote Sensing

Mapping bamboo with regional phenological characteristics derived from dense Landsat time series using Google Earth Engine

Meinan Zhang et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2019)

Add to Collection

Article Engineering, Aerospace

Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data

Yaotong Cai et al.

ADVANCES IN SPACE RESEARCH (2019)

Add to Collection

Article Environmental Sciences

Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4-8 and Sentinel-2 imagery

Shi Qiu et al.

REMOTE SENSING OF ENVIRONMENT (2019)

Add to Collection

Article Environmental Sciences

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

Markus Immitzer et al.

REMOTE SENSING (2019)

Add to Collection

Article Forestry

European Forest Types: toward an automated classification

Francesca Giannetti et al.

ANNALS OF FOREST SCIENCE (2018)

Add to Collection

Article Remote Sensing

Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series

Txomin Hermosilla et al.

CANADIAN JOURNAL OF REMOTE SENSING (2018)

Add to Collection

Article Environmental Sciences

An Efficient Maximum Likelihood Estimation Approach of Multi-Baseline SAR Interferometry for Refined Topographic Mapping in Mountainous Areas

Yuting Dong et al.

REMOTE SENSING (2018)

Add to Collection

Article Remote Sensing

Disturbance-Informed Annual Land Cover Classification Maps of Canada's Forested Ecosystems for a 29-Year Landsat Time Series

Txomin Hermosilla et al.

CANADIAN JOURNAL OF REMOTE SENSING (2018)

Add to Collection

Article Environmental Sciences

Tree Species Classification with Multi-Temporal Sentinel-2 Data

Magnus Persson et al.

REMOTE SENSING (2018)

Add to Collection

Article Economics

Mapping certified forests for sustainable management - A global tool for information improvement through participatory and collaborative mapping

Florian Kraxner et al.

FOREST POLICY AND ECONOMICS (2017)

Add to Collection

Article Ecology

The relationship of tropical bird communities to tree species composition and vegetation structure along an Andean elevational gradient

Jill E. Jankowski et al.

JOURNAL OF BIOGEOGRAPHY (2013)

Add to Collection

Article Forestry

Does mixing tree species enhance stand resistance against natural hazards? A case study for spruce

Verena C. Griess et al.

FOREST ECOLOGY AND MANAGEMENT (2012)

Add to Collection

Article Geography, Physical

Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points

Yang Shao et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2012)

Add to Collection

Article Ecology

Detection of relative differences in phenology of forest species using Landsat and MODIS

Bernard N. Isaacson et al.

LANDSCAPE ECOLOGY (2012)

Add to Collection

Article Environmental Sciences

Accuracy of forest mapping based on Landsat TM data and a kNN-based method

Amt Kristian Gjertsen

REMOTE SENSING OF ENVIRONMENT (2007)

Add to Collection

Article Ecology

Tree species composition and diversity gradients in white-water forests across the Amazon Basin

Florian Wittmann et al.

JOURNAL OF BIOGEOGRAPHY (2006)

Add to Collection

Article Remote Sensing

Support vector machines for classification in remote sensing

M Pal et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)

Add to Collection

Article Environmental Sciences

Overview of the radiometric and biophysical performance of the MODIS vegetation indices

A Huete et al.

REMOTE SENSING OF ENVIRONMENT (2002)

Add to Collection

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Add to Collection

© Peeref 2019-2024. All rights reserved.