Quantifying deforestation in the Brazilian Amazon using Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) and Shuttle Imaging Radar (SIR)-C data

2012 ◽  
Vol 27 (6) ◽  
pp. 463-478 ◽  
Author(s):  
M. Mahmudur Rahman ◽  
Josaphat Tetuko Sri Sumantyo
2021 ◽  
Vol 42 (13) ◽  
pp. 4989-5013
Author(s):  
Henrique Luis Godinho Cassol ◽  
Luiz Eduardo De Oliveira E Cruz De Aragão ◽  
Elisabete Caria Moraes ◽  
João Manuel De Brito Carreiras ◽  
Yosio Edemir Shimabukuro

2016 ◽  
pp. 71 ◽  
Author(s):  
E. Méndez ◽  
J. J. Valés ◽  
I. Pino ◽  
L. Granado ◽  
G. Montoya ◽  
...  

<p>La biomasa es un recurso forestal de gran importancia en Andalucía. Por ejemplo, la aplicación WEB“ Biomasa Forestal en Andalucía” (Consejería de Medio Ambiente y Ordenación del Territorio, 2014) informa al usuario acerca de la ubicación y existencias de biomasa de las principales especies forestales de pino. No obstante, se trata de una información que demanda ser actualizada periódicamente, con rapidez, eficacia y a bajo coste, requisitos que podría cubrir la tecnología basada en Observación de la Tierra. En este artículo se presenta un estudio piloto donde se ha evaluado la tecnología radar, concretamente el sensor ALOS-PALSAR (Advanced Land Observing Satellite – Phased Array type L-band Synthetic Aperture Radar), para medir la biomasa forestal de dos montes públicos de Huelva en Junio de 2008 y de 2010. El objetivo ha sido desarrollar una metodología para la estimación de volúmenes maderables a partir de la correlación estadística de la señal radar con datos coetáneos de volumen maderable, variable extraída de planes de ordenación forestal. Como resultado, se han logrado correlaciones en torno a 0,8 y 0,7 en pino y eucalipto respectivamente. Para la obtención de biomasa a partir de los volúmenes estimados se han usado ecuaciones alométricas específicas para cada especie. Tres son las fuentes de información claves: una muestra de parcelas homogéneamente distribuidas, un modelo digital del terreno preciso y un mapa forestal actual. Por otro lado, se ha llevado a cabo el estudio de la variabilidad de volúmenes estimados entre las fechas mencionadas. La metodología obtenida podría extrapolarse a todo el territorio regional.</p>


2018 ◽  
Vol 10 (8) ◽  
pp. 1304 ◽  
Author(s):  
Yusupujiang Aimaiti ◽  
Fumio Yamazaki ◽  
Wen Liu

In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.


2019 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Jolanda Patruno ◽  
Magdalena Fitrzyk ◽  
Jose Manuel Delgado Blasco

In remote sensing for archaeology, an unequivocal method capable of automatic detection of archaeological features still does not exists. Applications of Synthetic Aperture Radar (SAR) remote sensing for archaeology mainly focus on high spatial resolution SAR sensors, which allow the recognition of structures of small dimension and give information of the surface topography of sites. In this study we investigated the potential of combined dual and fully polarized SAR data and performed polarimetric multi-frequency and multi-incidence angle analysis of C-band Sentinel-1, L-band Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and of C-band Radar Satellite-2 (RADARSAT-2) datasets for the detection of surface and subsurface archaeological structures over the United Nations Educational, Scientific and Cultural Organization (UNESCO) site of Gebel Barkal (Sudan). While PALSAR offers a good historical reference, Sentinel-1 time series provide recent and systematic monitoring opportunities. RADARSAT-2 polarimetric data have been specifically acquired in 2012/2013, and have been scheduled to achieve a multi-temporal observation of the archaeological area under study. This work demonstrated how to exploit a complex but significant dataset composed of SAR full polarimetric and dual polarimetric acquisitions, with the purpose of identifying the most suitable earth observation technique for the preservation and identification of archaeological features. The scientific potential of the illustrated analysis fits perfectly with the current delicate needs of cultural heritage; such analysis demonstrates how multi-temporal and multi-data cultural heritage monitoring can be applied not only for documentation purposes, but can be addressed especially to those areas exposed to threats of different nature that require a constant and prompt intervention plans.


2021 ◽  
Vol 13 (2) ◽  
pp. 174
Author(s):  
Wei Chen ◽  
Qihui Zheng ◽  
Haibing Xiang ◽  
Xu Chen ◽  
Tetsuro Sakai

Forest canopy height is a basic metric characterizing forest growth and carbon sink capacity. Based on full-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) data, this study used Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology to estimate forest canopy height. In total the four methods of differential DEM (digital elevation model) algorithm, coherent amplitude algorithm, coherent phase-amplitude algorithm and three-stage random volume over ground algorithm (RVoG_3) were proposed to obtain canopy height and their accuracy was compared in consideration of the impacts of coherence coefficient and range slope levels. The influence of the statistical window size on the coherence coefficient was analyzed to improve the estimation accuracy. On the basis of traditional algorithms, time decoherence was performed on ALOS/PALSAR data by introducing the change rate of Landsat NDVI (Normalized Difference Vegetation Index). The slope in range direction was calculated based on SRTM (Shuttle Radar Topography Mission) DEM data and then introduced into the s-RVoG (sloped-Random Volume over Ground) model to optimize the canopy height estimation model and improve the accuracy. The results indicated that the differential DEM algorithm underestimated the canopy height significantly, while the coherent amplitude algorithm overestimated the canopy height. After removing the systematic coherence, the overestimation of the RVoG_3 model was restrained, and the absolute error decreased from 23.68 m to 4.86 m. With further time decoherence, the determination coefficient increased to 0.2439. With the introduction of range slope, the s-RVoG model shows improvement compared to the RVoG model. Our results will provide a reference for the appropriate algorithm selection and optimization for forest canopy height estimation using full-polarized L-band synthetic aperture radar (SAR) data for forest ecosystem monitoring and management.


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