An alternative approach for detecting and monitoring selectively logged forests in the Amazon

Souza, Camilla Mariane Menezes; Barreto, Paulo

doi:10.1080/014311600211064

Cited by 81 publications

(73 citation statements)

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“…Different bands and vegetation indices of the CHRIS sensor have been investigated for their correlation with above ground biomass in the Amazon [22]. Promising results were achieved by deriving cover-type fraction images using Spectral Mixture Analysis (SMA) [23]. In subsequent studies, the SMA fraction images were combined with contextual analysis, which takes into account that logging is spatially bound to either logging decks [14] or skid trails [24].…”

Section: State-of-the-artmentioning

confidence: 99%

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

et al. 2014

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Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi-or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods.

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Section: State-of-the-artmentioning

confidence: 99%

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

et al. 2014

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“…Images obtained with SMA that show detailed fractional cover of soils, non-photosynthetic vegetation (NPV) and green vegetation (GV) enhance our ability to detect logging infrastructure and canopy damage. For example, log landings and logging roads have higher levels of exposed bare soil with detection facilitated by Soil Fraction (Souza Jr. & Barreto, 2000). The brown vegetation component, including trunks and tree branches, increases with canopy damage, making NPV fraction useful for detecting this type of area Cochrane & Souza Jr., 1998) and the green vegetation (GV) fraction is sensitive to canopy gaps (Asner et al, 2004).…”

Section: Remote Sensing Techniques To Enhance and Detect Timber Harvementioning

confidence: 99%

“…In terms of mapping objective, some image processing algorithms were proposed for the total logged area, including roads, log landings, forest canopy damaged and undisturbed forest islands, while others were focused only on the mapping of forest canopy damage. Techniques to map total logged area were based on visual interpretation (e.g., Matricardi et al, 2001;Stone & Lefebvre, 1998), combination of automated detections of log landings with buffer applications defined by logging extraction reach (Monteiro et al, 2003;Souza Jr. & Barreto, 2000), and textural filtering (Matricardi et al, 2007). More automated techniques are mostly based on SMA (spectral mixture analysis) approaches combined with spatial pattern recognition algorithms (Asner et al, 2005;.…”

Section: Remote Sensing Techniques To Enhance and Detect Timber Harvementioning

confidence: 99%

“…However, the high cost of these images, and additional computational challenges in extracting information, requiring a combination of object-oriented classification with spectral information, severely limit the operational use of Ikonos and similar imagery. Over the last two decades, the Brazilian Amazon has been a great laboratory for testing remote sensing techniques to detect and map forest impacts of selective logging (Asner et al, 2005;Matricardi et al, 2001;Read et al, 2003;Souza Jr. & Barreto, 2000;Stone & Lefebvre, 1998;). These techniques differ in terms of mapping objectives, image processing techniques, geographic extent, and overall accuracy.…”

Section: Remote Sensing Techniques To Enhance and Detect Timber Harvementioning

confidence: 99%

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Remote Monitoring for Forest Management in the Brazilian Amazon

Monteiro¹,

Souza²

2012

Sustainable Forest Management - Current Research

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“…Land-cover classification using medium spatial resolution data such as Landsat is often difficult because of limitations in spatial resolution of the data and the heterogeneity of features on the ground especially for successional stages in a degraded tropical forest region (Souza, 2000;Stone, 1998;Eraldo, 2010). This problem often produces poor classification accuracy when conventional algorithms such as the maximum likelihood classifier are used.…”

Section: Introductionmentioning

confidence: 99%

Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia

Sakti

Tsuyuki

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction, Pelalawan District. ABSTRACT:Indonesian peatland, one of the world's largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation (peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.

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An alternative approach for detecting and monitoring selectively logged forests in the Amazon

Cited by 81 publications

References 0 publications

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Remote Monitoring for Forest Management in the Brazilian Amazon

Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia

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