Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

Roy, P. S.; Ravan, Shirish

doi:10.1007/bf02703218

Cited by 206 publications

(134 citation statements)

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“…Anthropogenic induced deforestation and natural forest degradation has led to the release of some 1-2 billion tons of carbon into the atmosphere, accounting for approximately 15%-20% of the global greenhouse gas (GHG) emissions [2]. By quantifying AGB through forest inventories, it is possible to understand the changes in the carbon pool and productivity of tropical forests [3,4]. Reliable AGB reporting is dependent on accurate field inventory, with regular updates, which is usually unavailable in most developing countries.…”

Section: Introductionmentioning

confidence: 99%

“…Such relationships are therefore prone to errors associated with models used in their development [5,8]. Forest impenetrability, and time and financial constraints, have led to localized field inventories with few tree species [9], which are not representative over large areas [3,10]. Remotely sensed data are more suitable for biomass assessments over wider spatial coverages.…”

Section: Introductionmentioning

confidence: 99%

“…Studies by [3] attributed these relationships to seasonal variations in phenological conditions, seen in visible and infrared bands of optical remote sensing datasets. Airborne laser scanners (ALS), light detection and ranging (Lidar) [11,12], and terrestrial laser scanners (TLS) can solve the problems associated with cloud contamination in imagery.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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Abstract:The use of optical remote sensing data for savanna vegetation structure mapping is hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity to clouds addresses some of these problems, but many space borne studies are still limited by low quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date, few studies have explored the strength of TLS for vegetation structural mapping, and particularly few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR) within a 9 km 2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height, and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area. The results from model validation showed a significant linear relationship between TLS-derived predictors with field biomass, p < 0.05 and adjusted R 2 ranging between 0.56 for SAR to 0.93 for the TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics compared with non-transformed AGB. An assessment of the backscatter based on root mean square error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can be used in the detection of small changes in savanna vegetation over time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Odipo

Nickless

Berger

et al. 2016

Forests

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“…This spectral reflectance is either measured in situ (using spectrometers) or via airborne or spaceborne sensors. Several studies have found strong correlations between forest biomass or LAI and spectral reflectance values at different wavelengths [7,8]. Eckert [9] and Ahamed et al [10] summarized different studies that analysed the levels of vegetation greenness in terms of vegetation indices derived from remotely-sensed data for estimating biomass.…”

Section: Introductionmentioning

confidence: 99%

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

2016

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Abstract:Mangroves are one of the most productive coastal communities in the world. Although we acknowledge the significance of ecosystems, mangroves are under natural and anthropogenic pressures at various scales. Therefore, understanding biophysical variations of mangrove forests is important. An extensive field survey is impossible within mangroves. WorldView-2 multi-spectral images having a 2-m spatial resolution were used to quantify above ground biomass (AGB) and leaf area index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements, vegetation indices derived from WorldView-2 images and a partial least squares regression algorithm were incorporated to produce LAI and AGB maps. LAI maps with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of 0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs obtained for AGB maps were 2.2 kg/m 2 and 2.0 kg/m 2 for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.

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“…Although various methods for biomass estimation have been tested (Gillespie et al 1992, Overman et al 1994, Roy and Ravan 1996, rarely has research successfully been conducted in a large area in moist tropical regions due to the complex tropical forest stand structure and abundant tree species. Previous research mainly used spectral signatures to establish biomass estimation models (Nelson et al 1988, Franklin and Hiernaux 1991, Leblon et al 1993, Nelson et al 2000, Steininger 2000, but ignored spatial information largely due to a lack of understanding about the relationships between biomass and spatial signatures.…”

Section: Introductionmentioning

confidence: 99%

Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin

Mausel

Brondı́zio

et al. 2002

Advances in Spatial Data Handling

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Above-ground biomass estimation of successional and mature forests in moist tropical regions is attracting increasing attention. Because of complex stand structure and abundant vegetation species, rarely has remote-sensing research been successfully conducted in biomass estimation for moist tropical areas. In this paper, two study areas in the Brazilian Amazon basin-Altamira and Bragantina-with different biophysical characteristics were selected. Atmospherically corrected Thematic Mapper (TM) images and field vegetation inventory data were used in the analysis, and different vegetation indices and texture measures were explored. Multiple regression models were developed through integration of image data (including TM bands, different vegetation indices, and texture measures) and vegetation inventory data. These models were used for biomass estimation in both selected study areas. This study concludes that neither TM spectral bands nor vegetation indices alone are sufficient to establish an effective model for biomass estimation, but multiple regression models that consist of spectral and textural signatures improve biomass estimation performance. The models developed are especially suitable for above-ground biomass estimation of dense vegetation areas.

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Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest

Cited by 206 publications

References 22 publications

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Assessment of Aboveground Woody Biomass Dynamics Using Terrestrial Laser Scanner and L-Band ALOS PALSAR Data in South African Savanna

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

Above-Ground Biomass Estimation of Successional and Mature Forests Using TM Images in the Amazon Basin

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