Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

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Factors Influencing Activity of Xiwang Road Ground Fissure Based on Time-series Interferometric Synthetic Aperture Radar and Detection of Land Cover Changes Using Optical Imaging

Sensors and Materials ◽

10.18494/sam.2020.3151 ◽

2020 ◽

Vol 32 (12) ◽

pp. 4635

Author(s):

Xi Liu ◽

Jing Ma ◽

Shan Su

Keyword(s):

Time Series ◽

Land Cover ◽

Synthetic Aperture Radar ◽

Optical Imaging ◽

Synthetic Aperture ◽

Land Cover Changes ◽

Interferometric Synthetic Aperture Radar ◽

Ground Fissure ◽

Factors Influencing ◽

Aperture Radar

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

10.32920/14637897.v1 ◽

2021 ◽

Author(s):

Adam Collingwood ◽

Paul Treitz ◽

Francois Charbonneau ◽

David M. Atkinson

Keyword(s):

Synthetic Aperture Radar ◽

High Arctic ◽

Synthetic Aperture ◽

The Arctic ◽

Optical Data ◽

Neural Network Modeling ◽

Artificial Neural ◽

Sar Data ◽

The Relationship ◽

Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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An extended GeoNode-Based Platform for Detailed Analysis of the Spatial/Temporal DInSAR Information Contents

10.5194/egusphere-egu2020-17569 ◽

2020 ◽

Author(s):

Adele Fusco ◽

Sabatino Buonanno ◽

Giovanni Zeni ◽

Michele Manunta ◽

Maria Marsella ◽

...

Keyword(s):

Time Series ◽

Synthetic Aperture Radar ◽

Spatial Data ◽

High Performance ◽

Surface Deformation ◽

Surface Displacement ◽

Synthetic Aperture ◽

Sar Data ◽

Informative Content ◽

Aperture Radar

We present an efficient tool for managing, visualizing, analysing, and integrating with other data sources, Earth Observation (EO) data for the analysis of surface deformation phenomena. In particular, we focused on specific EO data that are those obtained by an advanced-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, we refer to the SAR technique called advanced differential interferometric synthetic aperture radar (DInSAR) that have demonstrated its capabilities to detect, to map and to analyse the on-going surface displacement phenomena, both spatially and temporally, with centimetre to millimetre accuracy thanks to the generation of deformation maps and time-series. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, now with a massive and ever-increasing data flow supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program.Considering this big picture, the Spatial Data Infrastructures (SDI) becomes a fundamental tool to implement a framework to handle the informative content of geographic data. Indeed, an SDI represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data. We implemented an SDI, extending the functionalities of GeoNode, which is a web-based platform, providing an open-source framework based on the Open Geospatial Consortium (OGC) standards. OGC makes easier interoperability functionalities, that represent an extremely important aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our implemented GeoNode-Based Platform extends a Geographic Information System (GIS) to a web-accessible resource and adapts the SDI tools to DInSAR-related requirements. Our efforts have been dedicated to enabling the GeoNode platform to effectively analyze and visualize the spatial/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. We demonstrate the high performance of implemented GeoNode-Based Platform, showing DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 over Italy.This work is supported by the 2019-2021 IREA CNR and Italian Civil Protection Department agreement; the H2020 EPOS-SP project (GA 871121); the I-AMICA (PONa3_00363) project; and the IREA-CNR/DGSUNMIG agreement.&#160;&#160;

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SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-1187-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 1187-1193 ◽

Cited By ~ 5

Author(s):

G. Suresh ◽

R. Gehrke ◽

T. Wiatr ◽

M. Hovenbitzer

Keyword(s):

Land Cover ◽

Synthetic Aperture Radar ◽

Land Cover Classification ◽

Classification Systems ◽

Synthetic Aperture ◽

Optical Data ◽

Sar Images ◽

Sentinel 2 ◽

Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

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A multiscale approach for detection and mapping differential subsidence using multi-platform InSAR products

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-382-173-2020 ◽

2020 ◽

Vol 382 ◽

pp. 173-177

Author(s):

Dario E. Solano-Rojas ◽

Shimon Wdowinski ◽

Enrique Cabral-Cano ◽

Batuhan Osmanoglu ◽

Emre Havazli ◽

...

Keyword(s):

Synthetic Aperture Radar ◽

Data Availability ◽

Synthetic Aperture ◽

Multiscale Approach ◽

Interferometric Synthetic Aperture Radar ◽

Case Examples ◽

X Band ◽

Data Coverage ◽

Sar Data ◽

Aperture Radar

Abstract. Detecting and mapping subsidence is currently supported by interferometric synthetic aperture radar (InSAR) products. However, several factors, such as band-dependent processing, noise presence, and strong subsidence limit the use of InSAR for assessing differential subsidence, which can lead to ground instability and damage to infrastructure. In this work, we propose an approach for measuring and mapping differential subsidence using InSAR products. We consider synthetic aperture radar (SAR) data availability, data coverage over time and space, and the region's subsidence rates to evaluate the need of post-processing, and only then we interpret the results. We illustrate our approach with two case-examples in Central Mexico, where we process SAR data from the Japanese ALOS (L-band), the German TerraSAR-X (X-band), the Italian COSMO-SkyMed (X-band) and the European Sentinel-1 (C-band) satellites. We find good agreement between our results on differential subsidence and field data of existing faulting and find potential to map yet-to-develop faults.

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Measuring the state and temporal evolution of glaciers in Alaska and Yukon using synthetic-aperture-radar-derived (SAR-derived) 3D time series of glacier surface flow

The Cryosphere ◽

10.5194/tc-15-4221-2021 ◽

2021 ◽

Vol 15 (9) ◽

pp. 4221-4239

Author(s):

Sergey Samsonov ◽

Kristy Tiampo ◽

Ryan Cassotto

Keyword(s):

Time Series ◽

Synthetic Aperture Radar ◽

Flow Velocity ◽

Temporal Evolution ◽

Surface Flow ◽

Synthetic Aperture ◽

Surface Geometry ◽

Warming Climate ◽

Sar Data ◽

Aperture Radar

Abstract. Climate change has reduced global ice mass over the last 2 decades as enhanced warming has accelerated surface melt and runoff rates. Glaciers have undergone dynamic processes in response to a warming climate that impacts the surface geometry and mass distribution of glacial ice. Until recently no single technique could consistently measure the evolution of surface flow for an entire glaciated region in three dimensions with high temporal and spatial resolution. We have improved upon earlier methods by developing a technique for mapping, in unprecedented detail, the temporal evolution of glaciers. Our software computes north, east, and vertical flow velocity and/or displacement time series from the synthetic aperture radar (SAR) ascending and descending range and azimuth speckle offsets. The software can handle large volumes of satellite data and is designed to work on high-performance computers (HPCs) as well as workstations by utilizing multiple parallelization methods. We then compute flow velocity–displacement time series for glaciers in southeastern Alaska during 2016–2021 and observe seasonal and interannual variations in flow velocities at Seward and Malaspina glaciers as well as culminating phases of surging at Klutlan, Walsh, and Kluane glaciers. On a broader scale, this technique can be used for reconstructing the response of worldwide glaciers to the warming climate using archived SAR data and for near-real-time monitoring of these glaciers using rapid revisit SAR data from satellites, such as Sentinel-1 (6 or 12 d revisit period) and the forthcoming NISAR mission (12 d revisit period).

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