Monitoring Fire and Selective Logging Activities in Tropical Peat Swamp Forests

Franke, Jonas; Navratil, Peter; Keuck, Vanessa; Peterson, K. M.; Siegert, Florian

doi:10.1109/jstars.2012.2202638

Cited by 41 publications

(33 citation statements)

References 35 publications

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“…Typological classification of delineated wetland units: assignment of mapped wetland bodies into hydrological, geomorphological and ecological categories without detailed mapping of within-wetland cover [6,27,29,37,43,44,55,65,66,73,79,80]; (3). Classification of within-wetland cover types and/or vegetation: mapping within-wetland surface composition and vegetation types, sometimes targeting specific classes such as invasive plant species (e.g., [5,8,12,13,17,[23][24][25]28,30,32,34,41,45,[48][49][50]52,53,57,68,69,[74][75][76][77][79][80][81][82][83][84][85]); (4). Analysis of wetland change over a particular period of time...…”

Section: Research Objectives and Focusmentioning

confidence: 99%

“…It is important to note that among reviewed studies spectral attributes based on the image bandwidths were still the most common discriminating features [6,27,28,81]. Most analyses incorporated object-level values of image bands sensitive to surface moisture and vegetation greenness (e.g., red, green, near-infrared) and their mathematical combinations such as normalized difference vegetation index (NDVI) [6,7,39] and band ratios [32,45].…”

Section: Spectral Variablesmentioning

confidence: 99%

“…The most commonly used variables included proximity, adjacency, distance and relative border to "benchmark" classes affecting position of other surface types along the gradients of inundation, such as water bodies, channels, or streambeds [25,27,41,81]. To facilitate discrimination of riparian cover types in Australian tropical savannas [41], several contextual variables were utilized, including relative border to riparian vegetation and presence of and distance to streambed.…”

Section: Shape and Contextual Variablesmentioning

confidence: 99%

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Object-Based Image Analysis in Wetland Research: A Review

2015

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Abstract:The applications of object-based image analysis (OBIA) in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

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Section: Research Objectives and Focusmentioning

confidence: 99%

Section: Spectral Variablesmentioning

confidence: 99%

Section: Shape and Contextual Variablesmentioning

confidence: 99%

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Object-Based Image Analysis in Wetland Research: A Review

2015

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“…ETM+ data have outperformed the higher spatial resolution but lower spectral information IKONOS and SPOT-4 High-Resolution Visible and Infrared sensor (HRVIR) in LAI estimation (Soudani et al 2006), with ALS data being reported as alternatives (Zhao and Popescu 2009). Finally, managed forest disturbances, due to logging activities, have been monitored with Landsat time series (Kuemmerle et al 2009), whereas RapidEye VHR imagery has been used in logging trail detection (Franke et al 2012). …”

Section: Forestry Monitoringmentioning

confidence: 99%

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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“…High-resolution RapidEye imagery has been proven to detect small scale logging in peat swamp forests and was therefore chosen to monitor logging activities and fire impacts between both LiDAR acquisitions [43]. The RapidEye satellite system is a constellation of five identical satellites, which …”

Section: Multispectral Imagerymentioning

confidence: 99%

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

2013

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Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height

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Monitoring Fire and Selective Logging Activities in Tropical Peat Swamp Forests

Cited by 41 publications

References 35 publications

Object-Based Image Analysis in Wetland Research: A Review

Object-Based Image Analysis in Wetland Research: A Review

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

Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets

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