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Opportunities of Mapping Forest Carbon Stock and its Annual Increment Using Landsat Time-Series Data

Liangyun Liu
2016 Geoinformatics & Geostatistics An Overview  
inputs, moving from a single growing season composite to a set of multi-temporal metrics derived from Landsat 7 and 8 data, and they reported an overall mean absolute error (MAE) for tree height estimation  ...  [29] employed annual Landsat 7 growing season composite images and height data from the Geoscience Laser Altimeter System (GLAS) to estimate pan-tropical forest height [28] ; improved upon the Landsat  ... 
doi:10.4172/2327-4581.1000151 fatcat:i35smvrgangh5owdwh76syc4b4

Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam

Huong Nguyen Thi Thanh, Trung Minh Doan, Erkki Tomppo, Ronald E. McRoberts
2020 Remote Sensing  
of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam.  ...  Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory  ...  The authors would like to thank all of the people involved in collecting field data for classification and validation.  ... 
doi:10.3390/rs12091367 fatcat:7azjwshr7fbixlmb3sel5y25lm

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Wang Li, Zheng Niu, Rong Shang, Yuchu Qin, Li Wang, Hanyue Chen
2020 International Journal of Applied Earth Observation and Geoinformation  
related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height.  ...  available canopy height (H canopy ) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data.  ...  The co-variables were the composited median images during the growing seasons rather than singledate imagery between 2017 and 2019, which could not only be consistent with the ICESat-2 data on acquisition  ... 
doi:10.1016/j.jag.2020.102163 fatcat:oyktavb235e55eoddhd6ri7eka

Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information

Irene Chrysafis, Georgios Korakis, Apostolos P. Kyriazopoulos, Giorgos Mallinis
2020 Sustainability  
In this study, we used multispectral and multi-seasonal remotely sensed data from the Sentinel-2 satellite sensor along with geodiversity data for estimating local tree diversity in a Mediterranean forest  ...  Measuring and monitoring tree diversity is a prerequisite for altering biodiversity loss and the sustainable management of forest ecosystems.  ...  The authors are grateful to Athanasios Stampoulidis and the staff of the Northern Pindos National Park Management Body for their help during field sampling activities.  ... 
doi:10.3390/su12219250 fatcat:4xyfgzsgc5gjhfjteng434czae

Comparing Stability in Random Forest Models to Map Northern Great Plains Plant Communities Using 2015 and 2016 Pleiades Imagery

Jameson Brennan, Patricia Johnson, Niall Hanan
2019 Biogeosciences Discussions  
</strong> The use of high resolution imagery in remote sensing has the potential to improve understanding of patch level variability in plant structure and community composition that may be lost at coarser  ...  However, comparisons between the predicted plant community map using the 2015 imagery and one created with the 2016 imagery indicate 6.7&amp;thinsp;% of pixels on-town and 24.3&amp;thinsp;% of pixels off-town  ...  Acknowledgements 372 We would like to acknowledge and thank the U.S. Department of Agriculture (Grant  ... 
doi:10.5194/bg-2019-194 fatcat:qm67pqnhzbhvvgrpuycicizbui

Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua

Lisa C. Kelley, Lincoln Pitcher, Chris Bacon
2018 Remote Sensing  
The authors of [22] mapped larch plantations in China with 91.9% accuracy by combining multi-seasonal Landsat 8 imagery and bivariate textural features in a random forest (RF) classification model.  ...  imagery (30 m), and physiographic variables (temperature, topography, and precipitation).  ...  The authors thank Matt Jones and Sharon Gourdji for access to data that facilitated this analysis, and two anonymous reviewers for their generous feedback.  ... 
doi:10.3390/rs10060952 fatcat:5hbuuy6mwvcr7oyi7ut4pnshne

REMOTE SENSING OF AMAZONIAN FORESTS: MONITORING STRUCTURE, PHENOLOGY AND RESPONSES TO ENVIRONMENTAL CHANGES

João Roberto dos Santos, Lenio Soares Galvão, Luiz Eduardo Oliveira e Cruz de Aragão
2014 Revista Brasileira de Cartografia  
This review highlighted the importance of the combined use of optical and microwave data and of the integration of the remote sensing products with the fi eld-based information for understanding the functioning  ...  In this article, we overview recent advances in remote sensing for estimating tropical forest structure and biomass, for analyzing phenological patterns across tropical landscapes, and for quantifying  ...  During the 1997/1998 severe El Niño event, Alencar et al. (2006) estimated a total of 26,000 km 2 of forests burned forest using Landsat imagery.  ... 
doaj:7bdd666137b846cb88d0fb26739ebb73 fatcat:3xpjv45ynzgcxe2ddmktipdqxq

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

Peter Surový, Karel Kuželka
2019 Forests  
We examined modern remote sensing techniques used to obtain forest data that are directly applicable to decision making issues, and we provided a general overview of the types of data that can be obtained  ...  With satellite images that can obtain sub-meter spatial resolution, and new hardware, particularly unmanned aerial vehicles and systems, there are many emerging opportunities for improved data acquisition  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/f10030273 fatcat:fzowinbsvfdwtnpe45f6yfzu6a

Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest

Martin Karlson, Madelene Ostwald, Heather Reese, Josias Sanou, Boalidioa Tankoano, Eskil Mattsson
2015 Remote Sensing  
Spectral, texture, and phenology predictor variables were extracted from Landsat 8 imagery and used as input to Random Forest (RF) models.  ...  The most accurate models were created using multi-temporal imagery and variable selection, for both TCC (five predictor variables) and AGB (four predictor variables).  ...  Huges Bazié is thanked for arranging the practicalities of the field campaign, and the farmers in Saponé are thanked for their permission to perform the tree inventory.  ... 
doi:10.3390/rs70810017 fatcat:oz4y2mkj7rbvbpiaxftuvkpoa4

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

Xun Li, Wendy Y. Chen, Giovanni Sanesi, Raffaele Lafortezza
2019 Remote Sensing  
For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality.  ...  Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency.  ...  Acknowledgments: The authors also wish to acknowledge Yole DeBellis for contributing to the review of this work and for editing the language of the manuscript.  ... 
doi:10.3390/rs11101144 fatcat:7kf7tknxmfbp7ouwya4mzu7q7u

Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota

Peter T. Wolter, Philip A. Townsend
2011 Remote Sensing of Environment  
derived from SPOT-5 sensor data (Wolter et al., 2009) , to determine the best combination of sensor data for estimating near species-level proportional forest composition (12 types: 10 species and 2 genera  ...  We also demonstrate that PLS regression is an effective data fusion strategy for mapping composition of heterogeneous forests using satellite sensor data.  ...  Finally, thanks to Peter Crump and Aditya Singh for programming support, and to the Natural Resources Research Institute of the University of Minnesota-Duluth for providing supplemental forest plot data  ... 
doi:10.1016/j.rse.2010.10.010 fatcat:2pfud6vox5bijfno3lw7v2szxa

Mississippi CRP Forest Remote Sensing with Preliminary Global Ecosystem and Dynamics (GEDI) Mission Derived Data Products

Rebecca Degagne, declan pizzino, mike gough, hannah friedrich, charlotte smith, Gladwin Joseph, Rich Iovanna, James Strittholt
2022 figshare.com  
CBI initially developed predictive maps of tree height, tree density, biomass, basal area, and forest type using Random Forest machine learning models.  ...  Numerous satellite-derived indices from the European Space Agency's (ESA) Sentinel-1 and Sentinel-2 sensors, in addition to soils and topography data, were used as predictor inputs.  ...  Google Earth Engine also offers a rich, multi-petabyte catalog of satellite imagery, which would readily allow us to test additional value added by different sensors and higher resolution imagery.  ... 
doi:10.6084/m9.figshare.19142147.v2 fatcat:gvgoyy6egzdd5mvgj235ihevzi

Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada

Harold S.J. Zald, Michael A. Wulder, Joanne C. White, Thomas Hilker, Txomin Hermosilla, Geordie W. Hobart, Nicholas C. Coops
2016 Remote Sensing of Environment  
Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation.  ...  Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM+ pixel-based image composites.  ...  funded by the Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) and the Canadian Forest Service (CFS) of Natural Resources Canada.  ... 
doi:10.1016/j.rse.2016.01.015 fatcat:btxtbudmlrc2nnpuwvn7zg2f7u

Spatial fuel data products of the LANDFIRE Project

Matthew C. Reeves, Kevin C. Ryan, Matthew G. Rollins, Thomas G. Thompson
2009 International journal of wildland fire  
Canopy fuels are mapped using regression trees relating field-referenced estimates of canopy base height and canopy bulk density to satellite imagery, biophysical gradients and vegetation structure and  ...  Here we focus on the methods and data used to create the fuel data products, discuss problems encountered with the data, provide an accuracy assessment, demonstrate recent use of the data during the 2007  ...  John Szymoniak, retired USDA, provided the facts needed to discuss the use of the Wildland Fire Decision Support System for the 2007 fire season.  ... 
doi:10.1071/wf08086 fatcat:f43jha2rije57b3noadtcjc3de

Satellite Remote Sensing Technologies for Biodiversity Monitoring and Its Conservation

Khare S., Ghosh S.K.
2016 International Journal of Advanced Earth Science and Engineering  
and fine-scale disturbances of forests.  ...  This review paper incorporates the categorization of all important and advanced sensors with respect to the essential biodiversity variables required for its monitoring and conservation.  ...  High temporal resolution data (Multi season data or images corresponding to specific seasons) Information on invasion species and other species of interest (e.g. using images acquired corresponding to  ... 
doi:10.23953/cloud.ijaese.213 fatcat:tmqepre5sffzxm7b2xl7ypqily
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