Implementation of Forest Canopy Density Model to Monitor Tropical Deforestation

Deka, Jyotishman; Tripathi, Om Prakash; Khan, M. L.

doi:10.1007/s12524-012-0224-5

Cited by 32 publications

(19 citation statements)

References 13 publications

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“…This index is based on a combination of the NDVI and the normalised difference built-up index (NDBI) [32]. The BSI has mainly been used in forest research to differentiate between bare soil and other land cover types [34][35][36], but was also used for the mapping and monitoring of bare soil areas [37,38]. The original BSI used SWIR1 reflectance, but we found SWIR2 to be more sensitive in terms of classification accuracy with our field sites (results not shown).…”

Section: Bare Soil Indexmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

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Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

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Section: Bare Soil Indexmentioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

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“…It has also reported that most of the results from previous research have claimed high correlation between the reflectance and field measurement of FCD. Deka et al, (2012) reported that the vegetation indices reduce the effects of bias and assist in the extraction of the significant features of a specific ground object.…”

Section: Determination Of Vegetation Densitymentioning

confidence: 99%

“…Panta and Kim (2006) have investigated the spatial-temporal dynamic alterations of FCD across with site associated factors such as biophysical, physical and human interferences in tropical region of Nepal from 1988 to 2001 using Landsat TM and ETM+. Deka et al (2012) found the ability of FCD model to detect the temporal change of tropical deforestation in North East India with overall accuracy of 84.0 % and kappa coefficient of 0.77. Mon et al (2012) revealed that FCD Mapper can be applied to monitor tropical mixed deciduous vegetation over time at lower cost than alternative methods in Myanmar.…”

Section: Introductionmentioning

confidence: 97%

FCD Application of Landsat for Monitoring Mangrove in Central Kalimantan

Sukarna

Sahid²

2016

IJG

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A large amount of tropical mangrove forest in Indonesia has been lost due to rapid development in coastal areas, such as, aquaculture, industry, housing, and etc. Assessment of mangrove still mostly used conventional methods. It involves labor intensive, time consuming, high costs and impractical for use in large area. To answer these problems, this study aims to study accuracy and effectiveness of forest canopy density (FCD) model of Landsat for monitoring mangrove changes with large area ±2.600 hectares during periods 2002 and 2014 in Central Kalimantan. The result showed that FCD is capable to classified mangrove changes with overall accuracy 89.75%, and known that mangrove changes during approximately 12 years divided into four groups, i.e. deforested areas 11.11%, degraded areas 12.98%, regrowth areas 23.29% and not change areas 52.62%. Concluded that FCD model is quite accurate and effective used to monitor mangrove changes such as deforestation, degradation and regrowth. Abstrak Sebagian besar hutan mangrove di Indonesia telah berkurang akibat pembangunan kawasan pesisir seperti tambak, industry, pemukiman dll. Kajian mangrove masih banyak menggunakan metode konvensional. Hal ini tentu membutuhkan banyak tenaga, waktu, dan biaya serta kurang praktis digunakan untuk kawasan hutan yang luas. Untuk menjawab persoalan tersebut, penelitian ini bertujuan untuk mempelajari tingkat ketelitian dan efektivitas model kerapatan kanopi hutan citra Landsat dalam memantau perubahan hutan mangrove dengan luas ±2.600 hektar antara tahun 2002-2014 di

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“…Researches focusing on remote sensing data maximize the opportunities for ensuring long-term preservation of endangered species, better understanding of how fauna make use of maximum space occupied by vegetation, and to predict the future impacts of climate change and land management on species distributions. Satellite remote sensing has been proved to be the most cost-effective means of mapping and monitoring environmental changes in terms of vegetation and other ecological issues (Deka et al 2013). Biodiversity monitoring by remote sensing enables us to scale up the understanding and knowledge on biodiversity.…”

Section: Application Of Geoinformatics In Biodiversity and Climate Chmentioning

confidence: 99%

Biodiversity Status and Climate Change Scenario in Northeast India

Saikia

Kumar

Khan

2016

Climate Change Challenge (3C) and Social-Economic-Ecological Interface-Building

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Conservation of biodiversity and impacts of climate change are perhaps the most critical challenges faced by all sectors of the society. The patterns and processes related to climate change and biodiversity are so complex that corrective measures are often taken with an imperfect scientific knowledge base. This article addresses the biodiversity status and climate change scenario in Northeast India. The normalized difference vegetation index (NDVI) is used to explain the vegetation pattern of Northeast India and majority of area (58 %) are under tropical climatic zone, followed by subtropical (23 %), alpine (10 %), and temperate (9 %) zones. Although the Northeastern India is very susceptible to climate change because of its ecological fragility, the forest cover is reported increasing in the past few years because of the successful implementation of the different plantation and community development schemes. In order to avoid rapid and irreversible change in biodiversity, conservation strategies needed to focus on supporting the species' natural capacity to adapt to climate change.

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Implementation of Forest Canopy Density Model to Monitor Tropical Deforestation

Cited by 32 publications

References 13 publications

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

FCD Application of Landsat for Monitoring Mangrove in Central Kalimantan

Biodiversity Status and Climate Change Scenario in Northeast India

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