Improving tropical deforestation detection by fusing multiple SAR change measures

Dong Xichao; Quegan Shaun; Wei Liu; Kai Cui; Xin Lv

Improving tropical deforestation detection by fusing multiple SAR change measures

Dong Xichao, Quegan Shaun, Wei Liu^*, Kai Cui, Xin Lv

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to conference › Paper › peer-review

Abstract

The paper studies tropical deforestation detection in Riau province, Indonesia with L-band ALOS PALSAR and C-band ENVISAT ASAR data. Multiple change measures as SAR image intensity, texture, and temporal variations of ScanSAR time series are extracted and employed for deforestation detection. These measures are then combined for improving the detection with subsequent performance evaluation by comparing with the World Wildlife Fund's land cover maps as reference data. When applied on the FBD scene overlaid by both the PALSAR and ASAR data, the detection rate achieves up to 83.2%(at a false alarm rate of 20%) with a significant improvement of over 18% compared with the detection based on the single measure.

Original language	English
Publication status	Published - 2015
Event	IET International Radar Conference 2015 - Hangzhou, China Duration: 14 Oct 2015 → 16 Oct 2015

Conference

Conference	IET International Radar Conference 2015
Country/Territory	China
City	Hangzhou
Period	14/10/15 → 16/10/15

Keywords

Change detection
Forest monitoring
Synthetic aperture radar
Tropical deforestation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@conference{d2098b15d39e494fa4a61e68b4cd21bd,

title = "Improving tropical deforestation detection by fusing multiple SAR change measures",

abstract = "The paper studies tropical deforestation detection in Riau province, Indonesia with L-band ALOS PALSAR and C-band ENVISAT ASAR data. Multiple change measures as SAR image intensity, texture, and temporal variations of ScanSAR time series are extracted and employed for deforestation detection. These measures are then combined for improving the detection with subsequent performance evaluation by comparing with the World Wildlife Fund's land cover maps as reference data. When applied on the FBD scene overlaid by both the PALSAR and ASAR data, the detection rate achieves up to 83.2%(at a false alarm rate of 20%) with a significant improvement of over 18% compared with the detection based on the single measure.",

keywords = "Change detection, Forest monitoring, Synthetic aperture radar, Tropical deforestation",

author = "Dong Xichao and Quegan Shaun and Wei Liu and Kai Cui and Xin Lv",

year = "2015",

language = "English",

note = "IET International Radar Conference 2015 ; Conference date: 14-10-2015 Through 16-10-2015",

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TY - CONF

T1 - Improving tropical deforestation detection by fusing multiple SAR change measures

AU - Xichao, Dong

AU - Shaun, Quegan

AU - Liu, Wei

AU - Cui, Kai

AU - Lv, Xin

PY - 2015

Y1 - 2015

N2 - The paper studies tropical deforestation detection in Riau province, Indonesia with L-band ALOS PALSAR and C-band ENVISAT ASAR data. Multiple change measures as SAR image intensity, texture, and temporal variations of ScanSAR time series are extracted and employed for deforestation detection. These measures are then combined for improving the detection with subsequent performance evaluation by comparing with the World Wildlife Fund's land cover maps as reference data. When applied on the FBD scene overlaid by both the PALSAR and ASAR data, the detection rate achieves up to 83.2%(at a false alarm rate of 20%) with a significant improvement of over 18% compared with the detection based on the single measure.

AB - The paper studies tropical deforestation detection in Riau province, Indonesia with L-band ALOS PALSAR and C-band ENVISAT ASAR data. Multiple change measures as SAR image intensity, texture, and temporal variations of ScanSAR time series are extracted and employed for deforestation detection. These measures are then combined for improving the detection with subsequent performance evaluation by comparing with the World Wildlife Fund's land cover maps as reference data. When applied on the FBD scene overlaid by both the PALSAR and ASAR data, the detection rate achieves up to 83.2%(at a false alarm rate of 20%) with a significant improvement of over 18% compared with the detection based on the single measure.

KW - Change detection

KW - Forest monitoring

KW - Synthetic aperture radar

KW - Tropical deforestation

UR - http://www.scopus.com/inward/record.url?scp=84973542367&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:84973542367

T2 - IET International Radar Conference 2015

Y2 - 14 October 2015 through 16 October 2015

ER -

Improving tropical deforestation detection by fusing multiple SAR change measures

Abstract

Conference

Keywords

UN SDGs

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