scholarly journals Forest Dynamics with Sentinel 2 in Antanambe between 2005 and 2016 with the Snap Tool

2021 ◽  
Vol 10 (03) ◽  
pp. 92-101
Author(s):  
Tsiorinantenaina René Rakotoarison ◽  
Aimé Richard Hajalalaina ◽  
Elysa Nambinintsoa Safidinirina
Keyword(s):  
Forest Dynamics ◽  
Sentinel 2

Simulating forest dynamics of a tropical montane forest in South Ecuador

Erdkunde ◽  
2009 ◽  
Vol 63 (4) ◽  
pp. 347-364 ◽  
Author(s):  
Claudia Dislich ◽  
Sven Günter ◽  
Jürgen Homeier ◽  
Boris Schröder ◽  
Andreas Huth
Keyword(s):  
Forest Dynamics ◽  
Montane Forest ◽  
Tropical Montane Forest

Camera trap survey of animals in Xishuangbanna Forest Dynamics Plot, Yunnan

2014 ◽  
Vol 22 (6) ◽  
pp. 830 ◽  
Author(s):  
Zhang Mingxia ◽  
Cao Lin ◽  
Quan Ruichang ◽  
Xiao Zhishu ◽  
Yang Xiaofei ◽  
...  
Keyword(s):  
Forest Dynamics ◽  
Camera Trap ◽  
Forest Dynamics Plot

Estimación del Factor C de la RUSLE en la microcuenca del rio Lodana, Ecuador, usando imágenes del satélite Sentinel 2

2020 ◽  
Vol ve2020 (2) ◽  
pp. 69-75
Author(s):  
Ángel Claudio Ruiz Vélez ◽  
◽  
Henry Antonio Pacheco Gil ◽  
Keyword(s):  
Del Rio ◽  
Factor C ◽  
Sentinel 2

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities

2021 ◽  
Vol 11 (6) ◽  
pp. 2937-2951
Author(s):  
Gunnar Petter ◽  
Gerhard Zotz ◽  
Holger Kreft ◽  
Juliano Sarmento Cabral
Keyword(s):  
Forest Dynamics ◽  
Fragment Size ◽  
Agent Based Modeling ◽  
Selective Logging ◽  
Agent Based ◽  
Epiphyte Communities

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

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