Remote sensing analysis of forest damage by selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar

Win, Rosy Ne; Suzuki, Reiji; Takeda, Satoshi

doi:10.1007/s10310-011-0276-3

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…The trial and test approach was also used in forest management for clear-cuts and regrowth detection in temperate forest ecosys- tems (Banner & Lynham 1981, Hayes & Sader 2001, and for the identification of selective logging (Win et al 2012). Sepehry & Liu (2006) used symmetric threshold identification by testing threshold values ranging between 1·σ and 2·σ to monitor land cover change induced by flood, while Sarp (2012) used 1·σ threshold to detect vegetation change and main environmental effects of surface mining activities.…”

Section: Resultsmentioning

confidence: 99%

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Mancino¹,

Nolè²,

Ripullone³

et al. 2014

iForest

109

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© iForest -Biogeosciences and Forestry IntroductionAssessment of natural forest expansion represents a crucial issue to elucidate several processes, including biogeochemical cycles, atmospheric composition related to climate change, and forest carbon uptake, as well as socio-economic processes and issues. Anthropogenic and naturally induced land cover changes affect spatial and temporal distribution and availability of environmental resources, and alter ecosystem composition and productivity. Globally, these processes can be considered the primary catalysts for change in biogeochemical cycling, atmospheric composition, and climate (Pielke 2005, Metz et al. 2007, Turner et al. 2007). Forest land-use and land-cover change (LU-LCC) were recognised as key issues in greenhouse gas removal/emission processes as specified by the Good Practices Guidance for Land Use, Land Use Change, and Forestry (GPG-LULUCF) during the Intergovernmental Panel on Climate Change (IPCC) established at the Kyoto Protocol (Penman et al. 2003). Observation and assessment of forest cover changes are crucial to elucidate the complexities inherent in feedback processes between forest distribution and human activities in sustainable forest development, natural resource management, biodiversity conservation, ecosystem functioning, and biogeochemical cycling (IPCC 2007). In Mediterranean regions, natural forest expansion is primarily related to the abandonment of agricultural practices and cattle-raising in marginal areas representing the principal change in Italy's Mediterranean rural landscape over the past five decades (Piussi 2005). These processes generally vary in terms of the vegetation successional series and time scale, however the expansion dynamics are shared, beginning with an initial phase of spontaneous shrub dominance, followed by tree colonisation (Biondi et al. 2006).In recent decades, satellite-based high-resolution observations with multispectral scanners provided the scientific community with consistent data to implement detailed thematic mapping for local and regional scale land classification (Friedl et al. 2002, 2010, Lu & Weng 2007, Giri 2012. The near infrared wavebands on the Landsat Thematic Mapper (TM) facilitates advanced land classification analyses based on differences in spectral reflectance of different land cover types. In particular, specific foliar reflectance, pigment absorptions, and foliar moisture wavelength ranges represent the basis of vegetation class analyses. Furthermore, the availability of such land cover data at different spatial and temporal scales promotes the development and implementation of vegetation change detection techniques, which furthers our understanding on vegetation and ecosystem dynamics (Cohen & Fiorella 1998, Coppin et al. 2004, Lu et al. 2004, Martinez & Gilabert 2009).In forest ecosystems, land cover change dynamic detection based on visual and statistical approaches represents a challenge to the scientific community due to the difficulties in remotely sensed image acquisition err...

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Section: Resultsmentioning

confidence: 99%

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Mancino¹,

Nolè²,

Ripullone³

et al. 2014

iForest

109

View full text Add to dashboard Cite

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“…While there is no detailed estimate of the illegal logging intensity in this region, several studies implied that illegal logging activities caused severe forest losses [14,15]. Shifting cultivation has also been conducted by indigenous people since the 19th century [16]. [46] was used to define the country borders.…”

Section: Study Areamentioning

confidence: 99%

“…In 2016, in response to the forest loss, the Myanmar government banned logging activities for one year throughout the entire country and for 10 years in specific regions. However, it is unclear whether the ban will effectively reduce forest loss, as previous studies have shown that disturbance agents other than logging, such as dam construction and shifting cultivation, play an important role in deforestation in Myanmar [14][15][16][17]. Thus, the attribution of disturbance agents and the quantification of areas affected by disturbance agents must be characterized in a consistent manner to obtain accurate information.…”

Section: Introductionmentioning

confidence: 99%

Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar

Shimizu

Ahmed

Poncé-Hernandez

et al. 2017

Forests

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Abstract:In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include "logging", "plantation", "shifting cultivation", "urban expansion", "water invasion", "recovery", "other change", and "no change". The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.

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“…NDVI was one of the most successful of many attempts to simply and quickly identify vegetated areas and their »condition,« and it remains the best-known and most-used index for detecting live green plant canopies in multispectral remote sensing data (Fuller 2006;Milanović et al 2008;Campbell and Wynne 2011;Ne Win et al 2012). NDVI also has the advantage of allowing comparisons between images acquired at different times (Lillesand et al 2004).…”

Section: Introductionmentioning

confidence: 99%

The use of NDVI and CORINE Land Cover databases for forest management in Serbia

Jovanovic¹,

Milanović²,

Zorn³

2018

AGS

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A normalized difference vegetation index provides local forest managers with much essential annual information about the forest inventory. This article evaluates the possible use of NDVI and CORINE Land Cover databases for better forest management in the municipalities of Kursumlija and Topola in Serbia. The forest areas obtained using CLC were up to 11.5% larger than the official forest area estimates, whereas NDVI gave more precise results. This is of a crucial importance for preventing illegal logging, which is very prevalent in southern Serbian municipalities, which have substantial forested territory. NDVI is very promising for Serbia and also for countries that rarely carry out national forest inventories. This method can also easily be applied to other Balkan countries with a similar situation regarding local forest management.

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Remote sensing analysis of forest damage by selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar

Cited by 13 publications

References 25 publications

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar

The use of NDVI and CORINE Land Cover databases for forest management in Serbia

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