Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

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Estimating forest carbon stocks in tropical dry forests of Zimbabwe: exploring the performance of high and medium spatial-resolution multispectral sensors

Southern Forests a Journal of Forest Science ◽

10.2989/20702620.2016.1233751 ◽

2016 ◽

Vol 79 (1) ◽

pp. 31-40 ◽

Cited By ~ 2

Author(s):

Tawanda W Gara ◽

Amon Murwira ◽

Timothy Dube ◽

Mbulisi Sibanda ◽

Donald T Rwasoka ◽

...

Keyword(s):

Spatial Resolution ◽

Carbon Stocks ◽

Forest Carbon ◽

Tropical Dry Forests ◽

Dry Forests ◽

Multispectral Sensors

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Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Remote Sensing ◽

10.3390/rs8070540 ◽

2016 ◽

Vol 8 (7) ◽

pp. 540 ◽

Cited By ~ 19

Author(s):

Paul Schumacher ◽

Bunafsha Mislimshoeva ◽

Alexander Brenning ◽

Harald Zandler ◽

Martin Brandt ◽

...

Keyword(s):

High Resolution ◽

Satellite Data ◽

Volume Estimation ◽

Mountainous Region ◽

Wood Volume ◽

Red Edge ◽

Texture Attributes ◽

Semi Arid

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High-resolution forest carbon stocks and emissions in the Amazon

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1004875107 ◽

2010 ◽

Vol 107 (38) ◽

pp. 16738-16742 ◽

Cited By ~ 419

Author(s):

G. P. Asner ◽

G. V. N. Powell ◽

J. Mascaro ◽

D. E. Knapp ◽

J. K. Clark ◽

...

Keyword(s):

High Resolution ◽

Carbon Stocks ◽

Forest Carbon

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An object-oriented approach for agrivultural land classiﬁcation using rapideye imagery

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w4-145-2015 ◽

2015 ◽

Vol XL-7/W4 ◽

pp. 145-148 ◽

Cited By ~ 1

Author(s):

H. Sang ◽

L. Zhai ◽

J. Zhang ◽

F. An

Keyword(s):

High Resolution ◽

Land Cover ◽

Agricultural Land ◽

Object Oriented ◽

Agricultural Lands ◽

Decision Tree Classifier ◽

Red Edge ◽

High Resolution Imagery ◽

Tree Classifier ◽

Edge Band

With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential of red edge band in agricultural land cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing redundant information improves the mapping precision over 9% for AdaTree. WL, and 5% for SVM, the accuracy is over 90% for both approaches. Time phase characteristic is much important in different agricultural lands, and it improves the classification accuracy 7% for AdaTree.WL and 6% for SVM.

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Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data

International Journal of Remote Sensing ◽

10.1080/01431161.2012.666812 ◽

2012 ◽

Vol 33 (17) ◽

pp. 5583-5599 ◽

Cited By ~ 129

Author(s):

Christian Schuster ◽

Michael Förster ◽

Birgit Kleinschmit

Keyword(s):

Land Use ◽

High Resolution ◽

Satellite Data ◽

Edge Channel ◽

Red Edge

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High-resolution mapping of forest carbon stocks in the Colombian Amazon

Biogeosciences ◽

10.5194/bg-9-2683-2012 ◽

2012 ◽

Vol 9 (7) ◽

pp. 2683-2696 ◽

Cited By ~ 68

Author(s):

G. P. Asner ◽

J. K. Clark ◽

J. Mascaro ◽

G. A. Galindo García ◽

K. D. Chadwick ◽

...

Keyword(s):

High Resolution ◽

Tropical Forest ◽

Large Scale ◽

Carbon Stocks ◽

Forest Carbon ◽

Airborne Lidar ◽

High Resolution Mapping ◽

Resolution Mapping ◽

Carbon Mapping ◽

Colombian Amazon

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

Download Full-text