Spatial Analysis for Radar Remote Sensing of Tropical Forests

2021 ◽  
Author(s):  
Gianfranco (Frank) De Grandi ◽  
Elsa Carla De Grandi
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Tropical Forests ◽  
Radar Remote Sensing

scholarly journals Factors influencing the estimation of aboveground biomass (AGB) in tropical forests using RADAR remote sensing.

2018 ◽  
Author(s):  
Victoria E Espinoza-Mendoza
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Aboveground Biomass ◽  
Field Data ◽  
Spatial Information ◽  
Alos Palsar ◽  
Radar Remote Sensing ◽  
Radar Sensors ◽  
Factors Influencing ◽  
Optical Images

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

scholarly journals Factors influencing the estimation of aboveground biomass (AGB) in tropical forests using RADAR remote sensing.

2018 ◽  
Author(s):  
Victoria E Espinoza-Mendoza
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Aboveground Biomass ◽  
Field Data ◽  
Spatial Information ◽  
Alos Palsar ◽  
Radar Remote Sensing ◽  
Radar Sensors ◽  
Factors Influencing ◽  
Optical Images

Despite the large amount of accessible spatial information, the issue of estimating aboveground biomass through remote sensing, especially radar, remains a challenge in complex ecosystems such as tropical forests. One of the advantages of radar sensors is that of "crossing clouds" (capacity that does not have optical images like Landsat), facilitating their use in areas with permanent cloud cover. This work defines, from several studies conducted in tropical forests using ALOS PALSAR, which are the factors with the most influence on the signal of the radar. This can be useful in the development and/or improvement of methodologies to estimate aboveground biomass in tropical forests, combining field data and satellite imagery of radar.

ANALISIS KESESUAIAN LAHAN DAN EVALUASI JENIS TANAH DALAM BUDIDAYA TANAMAN TEBU UNTUK PENGEMBANGAN DAERAH KABUPATEN TEGAL

2013 ◽  
Vol 13 (2) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Spatial Analysis ◽  
Geographical Information System ◽  
Plant Productivity ◽  
Saccharum Officinarum ◽  
Geographical Information ◽  
Sugarcane Plant ◽  
Plant Cultivation ◽  
Suitability Map

The objectives of the research were to make land suitability map for sugarcane plant (Saccharum officinarum), to give recommendation of location including area for sugarcane plant cultivation and to increase sugarcane plant productivity. The research used maps overlay and Geographical Information System (GIS) which used Arch-View Spatial Analysis version 2,0 A in Remote Sensing Laboratory, Agency for the Assessment and Application of Technology (BPPT), Jakarta. The research was carried out in Tegal Regency starting from June to October 2004.The results of the research showed that the suitable, conditionally suitable, and not suitable land for sugarcane cultivation in Tegal Regency reached to a high of 20,227 ha, 144 ha, and 81,599 ha respectively. There were six most dominant kind of soil: alluvial (32,735 ha), grumosol 5,760 ha), mediteran (17,067 ha), latosol   (18,595 ha), glei humus (596 ha), and regosol (22,721 ha).

scholarly journals Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

2021 ◽  
Vol 205 ◽  
pp. 76-92
Author(s):  
Clara Simón de Blas ◽  
Rubén Valcarce-Diñeiro ◽  
Ana E. Sipols ◽  
Nilda Sánchez Martín ◽  
Benjamín Arias-Pérez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Panel Data ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery
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