scholarly journals QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon

2012 ◽  
Author(s):  
Lee Spangler ◽  
Lee A. Vierling ◽  
Eva K. Stand ◽  
Andrew T. Hudak ◽  
Jan U.H. Eitel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Monitoring ◽  
Carbon Pools ◽  
Field Validation ◽  
Tree Biomass ◽  
Validation Test ◽  
Aboveground Carbon ◽  
Gis Integration ◽  
Multi Temporal ◽  
Aboveground Tree Biomass

scholarly journals Application of GIS and Remote Sensing Technology to Detect Landscape Change in Hollongapar Gibbon Wildlife Sanctuary, India

2020 ◽  
Vol 9 (6) ◽  
pp. 806-810
Keyword(s):  
Remote Sensing ◽  
Landscape Change ◽  
Land Conversion ◽  
Wildlife Sanctuary ◽  
Study Region ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Gis Integration ◽  
Multi Temporal

The present study stated on an evaluation into the use of remote sensing technology and geographic information systems (GIS) integration to detect land use/cover trajectories in Hollongapar gibbon wildlife sanctuary in Assam, India. Remote sensing technology was used to utilize multi-temporal satellite imagery including Landsat TM (Themetic Mapper) and Landsat OLI (Operational Land Imager) data to perform LU/C change detection from the year 1986 to 2018. The results revealed significant and unequal land conversion in the region of study. The paddy fields and tea gardens in and around the 4 km buffer of the wildlife sanctuary had increased sharply during the period 1986-2018. Remote sensing and GIS integration has been found to be effective in tracking and analyzing trends of LU/C trajectories and assessing the effects of land conversion on biodiversity of the study region.

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 604
Author(s):  
Donato Amitrano ◽  
Gerardo Di Martino ◽  
Raffaella Guida ◽  
Pasquale Iervolino ◽  
Antonio Iodice ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Special Focus ◽  
Arid Environments ◽  
Synthetic Aperture Radar Data ◽  
Multi Temporal ◽  
Aperture Radar

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

scholarly journals Estimating Aboveground Tree Biomass for Beetle-Killed Lodgepole Pine in the Rocky Mountains of Northern Colorado

2017 ◽  
Vol 63 (4) ◽  
pp. 413-419 ◽  
Author(s):  
Woodam Chung ◽  
Paul Evangelista ◽  
Nathaniel Anderson ◽  
Anthony Vorster ◽  
Hee Han ◽  
...  
Keyword(s):  
Rocky Mountains ◽  
Lodgepole Pine ◽  
Tree Biomass ◽  
Northern Colorado ◽  
Aboveground Tree Biomass

scholarly journals Implications of increased deciduous cover on stand structure and aboveground carbon pools of Alaskan boreal forests

Ecosphere ◽  
2012 ◽  
Vol 3 (5) ◽  
pp. art45 ◽  
Author(s):  
Heather D. Alexander ◽  
Michelle C. Mack ◽  
Scott Goetz ◽  
Pieter S. A. Beck ◽  
E. Fay Belshe
Keyword(s):  
Boreal Forests ◽  
Stand Structure ◽  
Carbon Pools ◽  
Aboveground Carbon
Sign in / Sign up

Export Citation Format

Share Document