Early-Stage Deforestation Detection in the Tropics With L-band SAR

Autor: Manabu Watanabe, Christian N. Koyama, Masato Hayashi, Izumi Nagatani, Masanobu Shimada

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 11, Iss 6, Pp 2127-2133 (2018)

Polarimetric characteristics of early-stage deforestation areas were examined with L-band synthetic aperture radar (SAR) to develop a forest early warning system. Time series of PALSAR-2/ScanSAR data and Landsat data were used to examine the differen

Externí odkaz: https://doaj.org/article/b30c7a14447b448abebd9838ea1c7f69

Refined algorithm for forest early warning system with ALOS-2/PALSAR-2 ScanSAR data in tropical forest regions

Autor: Christian N. Koyama, Masanobu Shimada, Manabu Watanabe, Izumi Nagatani, Takeo Tadono, Masato Hayashi

Publikováno v: Remote Sensing of Environment. 265:1-16

形態: カラー図版あり
Physical characteristics: Original contains color illustrations
Accepted: 2021-08-07
資料番号: PA2210062000

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46e8dd5a5153692b5ade06c388b17123
http://id.nii.ac.jp/1696/00049333/

Comparative Analysis of the Global Forest/Non-Forest Maps Derived from SAR and Optical Sensors. Case Studies from Brazilian Amazon and Cerrado Biomes

Autor: Manabu Watanabe, Marcos Adami, Gustavo Bayma, Christian N. Koyama, Paola Rizzoli, Edson Eyji Sano, Daniel Moraes de Freitas, Yosio Edemir Shimabukuro

Publikováno v: Remote Sensing; Volume 13; Issue 3; Pages: 367
Remote Sensing, Vol 13, Iss 367, p 367 (2021)
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice)
Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA

Global-scale forest/non-forest (FNF) maps are of crucial importance for applications like biomass estimation and deforestation monitoring. Global FNF maps based on optical remote sensing data have been produced by the wall-to-wall satellite image ana

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::283454e6ec57570859b5f9eb4e2ee83b

Trial of Detection Accuracies Improvement for JJ-FAST Deforestation Detection Algorithm Using Deep Learning

Autor: Masanobu Shimada, Manabu Watanabe, Masato Hayashi, Christian N. Koyama, Izumi Nagatani, Takeo Tadono

Publikováno v: IGARSS

JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) monitors tropical forest in 77 countries by using PALSAR-2/ScanSAR time-series data. The deforestation detection algorithm used in JJ-FAST shows overall user's accuracies of 71.1%, but th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0c8db5393e7a09a1cb98bcea2b9808de
https://doi.org/10.1109/igarss47720.2021.9553211

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

Autor: Christian N. Koyama, Hai Liu, Kazunori Takahashi, Masanobu Shimada, Manabu Watanabe, Tseedulam Khuut, Motoyuki Sato

Publikováno v: Remote Sensing, Vol 9, Iss 6, p 580 (2017)

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate qua

Externí odkaz: https://doaj.org/article/ea252f6a383e4a15b3d13a7314fd023a