scholarly journals CLUSTERING OF MULTI-TEMPORAL FULLY POLARIMETRIC L-BAND SAR DATA FOR AGRICULTURAL LAND COVER MAPPING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 701-705
Author(s):  
H. Tamiminia ◽  
S. Homayouni ◽  
A. Safari
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Dimensional Space ◽  
Feature Space ◽  
Kernel Functions ◽  
Complex Nature ◽  
Land Cover Mapping ◽  
Multi Temporal ◽  
L Band ◽  
Sar Data

Recently, the unique capabilities of Polarimetric Synthetic Aperture Radar (PolSAR) sensors make them an important and efficient tool for natural resources and environmental applications, such as land cover and crop classification. The aim of this paper is to classify multi-temporal full polarimetric SAR data using kernel-based fuzzy C-means clustering method, over an agricultural region. This method starts with transforming input data into the higher dimensional space using kernel functions and then clustering them in the feature space. Feature space, due to its inherent properties, has the ability to take in account the nonlinear and complex nature of polarimetric data. Several SAR polarimetric features extracted using target decomposition algorithms. Features from Cloude-Pottier, Freeman-Durden and Yamaguchi algorithms used as inputs for the clustering. This method was applied to multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Canada, during June and July in 2012. The results demonstrate the efficiency of this approach with respect to the classical methods. In addition, using multi-temporal data in the clustering process helped to investigate the phenological cycle of plants and significantly improved the performance of agricultural land cover mapping.

scholarly journals Multi-Temporal and Multi-Frequency SAR Analysis for Forest Land Cover Mapping of the Mai-Ndombe District (Democratic Republic of Congo)

2019 ◽  
Vol 11 (24) ◽  
pp. 2999
Author(s):  
Jörg Haarpaintner ◽  
Heidi Hindberg
Keyword(s):  
Land Cover ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Land Cover Mapping ◽  
Forest Land ◽  
Statistical Parameters ◽  
Republic Of Congo ◽  
Multi Temporal ◽  
L Band ◽  
Forest Land Cover

The European Space Agency’s (ESA) “SAR for REDD” project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C- band SAR can compensate for the L-band’s deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4–5 percent points and 1–2 percent points for C- and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C- and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program’s and JAXA’s ALOS-1/2 based global FNF maps.

scholarly journals LEM BENCHMARK DATABASE FOR TROPICAL AGRICULTURAL REMOTE SENSING APPLICATION

2018 ◽  
Vol XLII-1 ◽  
pp. 387-392 ◽  
Author(s):  
I. D. Sanches ◽  
R. Q. Feitosa ◽  
P. Achanccaray ◽  
B. Montibeller ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Agricultural Land ◽  
Reference Data ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
World Population ◽  
Benchmark Database ◽  
Multi Temporal ◽  
Sentinel 2

<p><strong>Abstract.</strong> The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.</p>

Polarimetric SAR Data in Land Cover Mapping in Boreal Zone

2010 ◽  
Vol 48 (10) ◽  
pp. 3652-3662 ◽  
Author(s):  
Anne Lonnqvist ◽  
Yrjö Rauste ◽  
Matthieu Molinier ◽  
Tuomas Hame
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Boreal Zone ◽  
Polarimetric Sar ◽  
Sar Data

Enhanced decision tree ensembles for land-cover mapping from fully polarimetric SAR data

2017 ◽  
Vol 38 (23) ◽  
pp. 7138-7160 ◽  
Author(s):  
Iman Khosravi ◽  
Abdolreza Safari ◽  
Saeid Homayouni ◽  
Heather McNairn
Keyword(s):  
Land Cover ◽  
Decision Tree ◽  
Land Cover Mapping ◽  
Polarimetric Sar ◽  
Sar Data

Land Use and Land Cover Mapping in the Brazilian Amazon Using Polarimetric Airborne P-Band SAR Data

2008 ◽  
Vol 46 (10) ◽  
pp. 2956-2970 ◽  
Author(s):  
Corina da Costa Freitas ◽  
Luciana de Souza Soler ◽  
Sidnei JoÃo Siqueira Sant'Anna ◽  
Luciano Vieira Dutra ◽  
JoÃo Roberto dos Santos ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Brazilian Amazon ◽  
Land Cover Mapping ◽  
Sar Data

scholarly journals Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests

2019 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Sarah Banks ◽  
Lori White ◽  
Amir Behnamian ◽  
Zhaohua Chen ◽  
Benoit Montpetit ◽  
...  
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Wetland Classification ◽  
Model Based ◽  
Angle Data ◽  
Significant Difference ◽  
Multi Temporal ◽  
Sar Data

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

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