Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Morel, Alexandra; Saatchi, Sassan; Malhi, Yadvinder; Berry, Nicholas J.; Banin, Lindsay F.; Burslem, David F. R. P.; Nilus, Reuben; Ong, Robert C.

doi:10.1016/j.foreco.2011.07.008

Cited by 169 publications

(128 citation statements)

References 47 publications

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“…RMSE decreases to 43.25 Mg/ha in this model (Table 8). Saturation of L-band that occurs at high AGB can explain the moderate correlation [15,19,22,88,89]. Results from ANOVA test (at 5% confidence interval) among P1-P5 models confirm that all of the models are significantly different from one another.…”

Section: Forest Agb Estimation Model Based On Corrected Datamentioning

confidence: 65%

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Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

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Abstract:The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R 2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R 2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. OPEN ACCESSRemote Sens. 2014, 6 3694

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Section: Forest Agb Estimation Model Based On Corrected Datamentioning

confidence: 65%

“…Soil and vegetation moisture related to the precipitation events impact the SAR backscattering and could be a confounding factor in AGB estimation [89]. L-band SAR can penetrate more through vegetation; thus, the soil backscattering is involved in the total backscattering [21,97].…”

Section: Limitation and Sources Of Errorsmentioning

confidence: 99%

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

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“…Nonetheless, since variations in single woody vegetation category exist due to different planting year, detailed land cover mapping to further distinguish diversity of plantation's stand age needs to be addressed. Knowledge on the stand age is also useful to estimate the biomass since the level of biomass is known proportional to stand age [Morel et al, 2011]. Nonetheless, very limited reports have been presented in the discrimination of stand age in rubber plantation using polarimetric SAR data.…”

Section: Introductionmentioning

confidence: 99%

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

Trisasongko

2017

European Journal of Remote Sensing

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This paper presents an evaluation on strategies for rubber plantation mapping employing SAR data coupled with Random Forest (RF) and Support Vector Machine (SVM). Linear backscatter coefficients achieved saturation point at about 10 years, making this form of polarimetric data being robust only for young to mature stands. This research found that the performance of both algorithms was comparable. The addition of texture features gave substantial impact to the overall accuracy. As indicated by the analysis of variable importance, only some texture features have contributed to higher overall accuracy. Classification using a subset of texture features pointed out that accuracy could be improved using dual polarimetric data, while trivial enhancement was seen in combined HH, HV and VV backscatter intensities. The research showed that classification accuracy could be further augmented by setting proper classification parameters. Nonetheless, it is argued that the level of improvement would greatly depend on selecting a proper dataset fed into classifier, rather than tuning classifier parameters. ARTICLE HISTORY

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“…Such analyses would certainly extend CLASlite's capabilities of provisioning map products with even greater site relevance. More thorough interpretations of natural versus anthropogenic influences on forest cover and change might also be achieved by further classifying the forest cover and change CLASlite products based on factors that would likely influence human accessibility to forested areas, such as distance to roads, travel time from nearest city, and topographic features [25], or interpreting CLASlite products alongside remotely sensed radar data, which have been shown to discriminate oil palm plantations from forest stands with high accuracy [33]. Although the GFCD product as applied within the framework of our study was not the most optimal of the land-cover classification tools employed, the product itself still harbours opportunities for extended analyses as well.…”

Section: Discussionmentioning

confidence: 99%

“…Technologies with the ability to make this distinction would be vital resources for zero net deforestation targets, which value both the protection of native forests and the planting of new ones, and zero gross deforestation targets, which are particularly concerned with gross loss of forest area over time and broadly aim for no deforestation anywhere [30]. Remote sensing technologies that can map tree plantations separately from native forests will also contribute to more effective monitoring and a greater understanding of the impacts of land conversion due to growth in commercial agriculture [31,32], and critically inform carbon credit schemes such as the United Nation's Reducing Emissions from Deforestation and Forest Degradation (REDD) Programme [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Estimating aboveground biomass in forest and oil palm plantation in Sabah, Malaysian Borneo using ALOS PALSAR data

Cited by 169 publications

References 47 publications

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Mapping stand age of rubber plantation using ALOS-2 polarimetric SAR data

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

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