Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

Laurin, Gaia Vaglio; Liesenberg, Veraldo; Chen, Qi; Guerriero, L.; Frate, Fabio Del; Bartolini, Antonio; Coomes, David A.; Wilebore, Beccy; Lindsell, Jeremy A.; Валентини, Р.

doi:10.1016/j.jag.2012.08.002

Cited by 120 publications

(61 citation statements)

References 44 publications

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“…Other optical data include: the similar spatial resolution (i) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Reiche et al 2012;Lucas et al 2011) and (ii) Linear Imaging Self Scanning Sensor 3 (LISS-III) (Lucas et al 2011); the higher resolution (iii) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) (Vaglio Laurin et al 2013) and (iv) High Resolution Geometric (HRG) instrument (Lucas et al 2011); and the very high resolution (VHR) (v) QuickBird and (vi) WorldView-2 (Petrou et al 2014;Adamo et al 2014) sensors. Although of lower spatial resolution, data time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) have proven successful in mapping dry savanna vegetation, capturing phenological properties with inter-annual classification average user's and producer's accuracies reaching 94.86% and 97.73% for 12 classes, respectively (Hüttich et al 2009).…”

Section: Terrestrial Mappingmentioning

confidence: 99%

“…Furthermore, besides their ability in providing vegetation structure information, as discussed in next paragraphs, active data have been increasingly used in mapping applications. Phased Array type L-band SAR (PALSAR) and RADARSAT-2 data have provided high potential in LC and forest characterization, used either individually (Walker et al 2010;Maghsoudi et al 2012;Longépé et al 2011) or in synergy with optical data (Liesenberg and Gloaguen 2013;Vaglio Laurin et al 2013). In a study by Vaglio Laurin et al (2013), PALSAR coupled with AVNIR-2 identified eight LC classes in a tropical rainforest and fragmented area with accuracy 97.5%, outperforming PALSAR synergy with Landsat TM.…”

Section: Terrestrial Mappingmentioning

confidence: 99%

“…The latter has outperformed Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classifiers in a study by Forzieri et al (2013). Artificial Neural Networks (ANN) have shown advantages over SVM, SAM, or MLC in different studies (Vaglio Laurin et al 2013;Vyas et al 2011). It is worth noting, though, that ANN implementation proved incapable to build a model when 165 features were employed by Vyas et al (2011), probably due to the very complicated resulting network of nodes; on the contrary, SVM and SAM classifiers could handle this size of feature sets and perform the classification.…”

Section: Terrestrial Mappingmentioning

confidence: 99%

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Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Terrestrial Mappingmentioning

confidence: 99%

Section: Terrestrial Mappingmentioning

confidence: 99%

Section: Terrestrial Mappingmentioning

confidence: 99%

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Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…As boundary of the monitoring project is generally needed, categorical (land use or land cover) maps are primarily required and have been a focus for scientific experiments. Mapping tropical rain forests and their surrounding environment have been demonstrated [Longepe et al, 2011;Laurin et al, 2013;Reiche et al, 2013;Thapa et al, 2014;Jebur et al, 2015;Trisasongko, 2015], which suggested that L-band wavelength plays a significant role in the provision of up-to-date Earth surface condition. To support carbon accounting programs and conventions, L-band SAR data were also found valuable to estimate tropical forest biomass [Hamdan et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

Trisasongko

2017

European Journal of Remote Sensing

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This paper presents an evaluation on strategies for rubber plantation mapping employing SAR data coupled with Random Forest (RF) and Support Vector Machine (SVM). Linear backscatter coefficients achieved saturation point at about 10 years, making this form of polarimetric data being robust only for young to mature stands. This research found that the performance of both algorithms was comparable. The addition of texture features gave substantial impact to the overall accuracy. As indicated by the analysis of variable importance, only some texture features have contributed to higher overall accuracy. Classification using a subset of texture features pointed out that accuracy could be improved using dual polarimetric data, while trivial enhancement was seen in combined HH, HV and VV backscatter intensities. The research showed that classification accuracy could be further augmented by setting proper classification parameters. Nonetheless, it is argued that the level of improvement would greatly depend on selecting a proper dataset fed into classifier, rather than tuning classifier parameters. ARTICLE HISTORY

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“…The altitude of the park is 70-410 m. Other vegetation types within the park include moist semi-deciduous forest, freshwater inland swamp forest, secondary and disturbed forest, farmbush, herbaceous swamps, and floodplains [47]. Land cover outside of the park includes secondary and disturbed forest, farmbush and shrubland/savannah, plantation, and agriculture [48].…”

Section: Study Areamentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

Cited by 120 publications

References 44 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

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