Radar remote sensing for the delineation of forest cover maps and the detection of deforestation

Thiel, Christian; Drezet, P.M.L.; Weise, C.; Quegan, S.; Schmullius, C.

doi:10.1093/forestry/cpl036

Cited by 29 publications

(16 citation statements)

References 3 publications

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“…Previous studies [39,40] highlighted the good capability of S-band SAR in mapping clear-cuts and forested areas in the boreal forest of Canada and Sweden. Based on the results from the temperate forest site at Savernake forest presented here, the S-band SAR backscatter coefficient at all polarisations has shown suitable for monitoring forest cover change but lower than L-band SAR data [21,22]. The S-band SAR sensor on the NovaSAR satellite that is being built in the UK for launch in 2017 will provide image data that can be used for operational forest cover change detection and be used in regions with persistent cloud cover such as the tropics [37,55] and in the boreal winter with low sun angles [39,40].…”

Section: Forest Cover Change Analysismentioning

confidence: 86%

“…For an operational forest cover monitoring system at high spatial and temporal resolutions, Synthetic Aperture Radar (SAR) systems have emerged as robust tools which can penetrate clouds and record signals which originate from scattering within the vertical canopy layer [21,22]. For example, Shimada, Itoh, Motohka, Watanabe, Shiraishi, Thapa and Lucas [22] have generated global F/NF cover maps (2007-2010) at 25 m resolution from the Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HV gamma-nought (γ 0 ) reporting around 85% overall accuracy.…”

Section: Forest/non-forest Mapping Using Sarmentioning

confidence: 99%

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Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

et al. 2016

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Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C-and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH-and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes.

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Section: Forest Cover Change Analysismentioning

confidence: 86%

Section: Forest/non-forest Mapping Using Sarmentioning

confidence: 99%

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

et al. 2016

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“…However, optical imagery presents certain important limits in an equatorial and tropical context due to the cloud cover. In contrast, synthetic aperture radar (SAR) sensors using low-frequency microwaves enable the easy differentiation of the forest and others LULC types with their cloud-penetrating capacity and day and night measurements [31][32][33][34][35][36]. For example, the Japan Aerospace Exploitation Agency (JAXA) realized the 25 m global forest vs. non-forest maps using PALSAR/PALSAR-2 images, which include three land cover types (i.e., forest, non-forest and hydrography) [35,36].…”

Section: Discussionmentioning

confidence: 99%

Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

Catry

Dessay

et al. 2017

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Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale.

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“…[45]) prepends the extraction of non-overlapping adjacent homogeneous image objects before the virtual image restoration. The extraction of image objects enables the reduction of the influence of speckle [46]. Furthermore, an object oriented approach would allow for the compensation of spatial scale dependencies due to divergent spatial resolutions of the input data.…”

Section: Is Cloud Removal Feasible With Sar Data?mentioning

confidence: 99%

Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

et al. 2013

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This study presents a method for the reconstruction of pixels contaminated by optical thick clouds in multi-spectral Landsat images using multi-frequency SAR data. A number of reconstruction techniques have already been proposed in the scientific literature. However, all of the existing techniques have certain limitations. In order to overcome these limitations, we expose the Closest Spectral Fit (CSF) method proposed by Meng et al. to a new, synergistic approach using optical and SAR data. Therefore, the term Closest Feature Vector (CFV) is introduced. The technique facilitates an elegant way to avoid radiometric distortions in the course of image reconstruction. Furthermore the cloud cover removal is independent from underlying land cover types and assumptions on seasonality, etc. The methodology is applied to mono-temporal, multi-frequency SAR data from TerraSAR-X (X-Band), ERS (C-Band) and ALOS Palsar (L-Band). This represents a way of thinking about Radar data not as foreign, but as additional data source in multi-spectral remote sensing. For the assessment of the image restoration performance, an experimental framework is established and a statistical evaluation protocol is designed. The results show the potential of a synergistic usage of multi-spectral and SAR data to overcome the loss of data due to cloud cover.

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Radar remote sensing for the delineation of forest cover maps and the detection of deforestation

Cited by 29 publications

References 3 publications

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar

Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

Removal of Optically Thick Clouds from Multi-Spectral Satellite Images Using Multi-Frequency SAR Data

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