scholarly journals Detecting tropical selective logging with SAR data requires a time series approach

2020 ◽  
Author(s):  
MG Hethcoat ◽  
JMB Carreiras ◽  
DP Edwards ◽  
RG Bryant ◽  
S Quegan
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Forest Degradation ◽  
Selective Logging ◽  
Forest Monitoring ◽  
Free Access ◽  
Alos Palsar ◽  
Lowland Tropical Forest ◽  
Time Series Analyses ◽  
Sar Data

AbstractSelective logging is the primary driver of forest degradation in the tropics and reduces the capacity of forests to harbour biodiversity, maintain key ecosystem processes, sequester carbon, and support human livelihoods. While the preceding decade has seen a tremendous improvement in the ability to monitor forest disturbances from space, advances in forest monitoring have almost universally relied on optical satellite data from the Landsat program, whose effectiveness is limited in tropical regions with frequent cloud cover. Synthetic aperture radar (SAR) data can penetrate clouds and have been utilized in forest mapping applications since the early 1990s, but no study has exclusively used SAR data to map tropical selective logging. A detailed selective logging dataset from three lowland tropical forest regions in the Brazilian Amazon was used to assess the effectiveness of SAR data from Sentinel-1, RADARSAT-2 and PALSAR-2 for monitoring tropical selective logging. We built Random Forest models in an effort to classify pixel-based differences in logged and unlogged areas. In addition, we used the BFAST algorithm to assess if a dense time series of Sentinel-1 imagery displayed recognizable shifts in pixel values after selective logging. Random Forest classification with SAR data (Sentinel-1, RADARSAT-2, and ALOS-2 PALSAR-2) performed poorly, having high commission and omission errors for logged observations. This suggests little to no difference in pixel-based metrics between logged and unlogged areas for these sensors. In contrast, the Sentinel-1 time series analyses indicated that areas under higher intensity selective logging (> 20 m3 ha−1) show a distinct spike in the number of pixels that included a breakpoint during the logging season. BFAST detected breakpoints in 50% of logged pixels and exhibited a false alarm rate of approximately 10% in unlogged forest. Overall our results suggest that SAR data can be used in time series analyses to detect tropical selective logging at high intensity logging locations within the Amazon (> 20 m3 ha−1). These results have important implications for current and future abilities to detect selective logging with freely available SAR data from SAOCOM 1A, the planned continuation missions of Sentinel-1 (C and D), ALOS PALSAR-1 archives (expected to be opened for free access in 2020), and the upcoming launch of NISAR.

scholarly journals Time-Series InSAR Analysis of the Small Baseline Subset to Estimate Surface Deformation at Mt. Bromo Indonesia

Teknik ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 126
Author(s):  
Arliandy Pratama Arbad ◽  
Wataru Takeuchi ◽  
Yosuke Aoki ◽  
Achmad Ardy ◽  
Mutiara Jamilah
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Alos Palsar ◽  
Small Baseline Subset ◽  
Sar Data ◽  
Small Baseline

Penginderaan jauh kini memainkan peranan penting dalam pengamatan perilaku gunung api. Penelitian ini bertujuan untuk mengamati deformasi permukaan Gunung Bromo, yang terletak di Jawa bagian Timur, Indonesia, yang masuk dalam rangkaian sistem volkanik di Taman Nasional Bukit Tengger Semeru (TNBTS). Penggunaan algoritma SAR Interferometry (InSAR) yang disebut sebagai pendekatan Small Baseline Subset (SBAS) memungkinkan perancangan peta kecepatan deformasi rata-rata dan and peta time series displacement di wilayah kajian. Teknik SBAS yang biasa menghasilkan rangkaian observasi tahap interferometrik. Ini tercatat sebagai kombinasi linear dari nilai fase SAR  scene untuk setiap pixel secara tersendiri. Analisis yang dilakukan terutama berdasarkan 22 data SAR data yang diperoleh melalui sensor ALOS/PALSAR selama kurun waktu 2007–2011. Beberapa penelitian menunjukkan bahwa kemampuan analisis InSAR dalam menyelidiki siklus gunung api, terutama Gunung Bromo yang memiliki karakteristik erupsi stratovolcano dalam satu hingga lima tahun. Analisis hasil memperlihatkan adanya kemajuan dari kajian sebelumnya akan InSAR wilayah tersebut, yang lebih fokus  kepada deformasi yang berpengaruh kepada kaldera. Hal ini menunjukkan bahwa penelitian ini bisa diimplementasikan pada manajemen risiko atau manajemen infrastruktur

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Coeruptive and posteruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption based on PALSAR-2 time-series analysis

2020 ◽  
Author(s):  
Yuji Himematsu ◽  
Taku Ozawa ◽  
Yosuke Aoki
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Hydrothermal System ◽  
Phreatic Eruption ◽  
Physical Processes ◽  
Cumulative Deformation ◽  
Time Series Analyses ◽  
L Band ◽  
Phreatic Eruptions ◽  
Sar Data

Abstract Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data is a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes.

scholarly journals On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping

2018 ◽  
Vol 11 (1) ◽  
pp. 39 ◽  
Author(s):  
Christopher Marston ◽  
Patrick Giraudoux
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Small Mammal ◽  
Disease Transmission ◽  
Agricultural Land ◽  
Limited Resource ◽  
Optical Imagery ◽  
Sar Data ◽  
Land Cover Maps

(1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.

scholarly journals Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

2014 ◽  
Vol 6 (1) ◽  
pp. 756-775 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Martin Steinegger ◽  
Heinz Gallaun ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Case Studies ◽  
Central Africa ◽  
Forest Degradation ◽  
Selective Logging ◽  
Series Analysis

scholarly journals MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 13-20 ◽  
Author(s):  
Reginald Jay Labadisos Argamosa ◽  
Ariel Conferido Blanco ◽  
Alvin Balidoy Baloloy ◽  
Christian Gumbao Candido ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Random Forest ◽  
Principal Components ◽  
Mangrove Forest ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Ground Biomass ◽  
Sar Data ◽  
Volumetric Objects ◽  
Occurrence Matrix

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&amp;thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&amp;thinsp;cm to 7.5&amp;thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&amp;minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&amp;thinsp;Mg using only four features, namely, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM variance, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.

scholarly journals Coeruptive and posteruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption based on PALSAR-2 time-series analysis

2020 ◽  
Author(s):  
Yuji Himematsu ◽  
Taku Ozawa ◽  
Yosuke Aoki
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Hydrothermal System ◽  
Phreatic Eruption ◽  
Physical Processes ◽  
Cumulative Deformation ◽  
Time Series Analyses ◽  
L Band ◽  
Phreatic Eruptions ◽  
Sar Data

Abstract Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data is a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes.

scholarly journals Coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption based on PALSAR-2 time-series analysis

2020 ◽  
Author(s):  
Yuji Himematsu ◽  
Taku Ozawa ◽  
Yosuke Aoki
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Hydrothermal System ◽  
Fault Plane ◽  
Phreatic Eruption ◽  
Physical Processes ◽  
Time Series Analyses ◽  
L Band ◽  
Phreatic Eruptions ◽  
Sar Data

Abstract Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Coeruptive deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted coeruptive deformation events are well explained by normal faulting with a left-lateral slip component along a southwest-dipping fault plane rather than by a point source deflation. The inferred fault plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface. We reconfirmed that SAR data is a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes.

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