Comparison of carbon sequestration potential in agricultural and afforestation farming systems

Lin, Chinsu; Lin, Chun-Hsiung

doi:10.1590/s0103-90162013000200006

Cited by 19 publications

(8 citation statements)

References 23 publications

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“…Calculations for stand-level AGC are based on a stand structure model of tree diameter and height distribution. Parameters are fitted into a probability distribution function in order to estimate the AGC for an entire forest stand [1,2]. Although conventional measure the effectiveness of the range of tree-level AGC models in predicting stand-level forest carbon stocks and determine an appropriate algorithm for practical use in stand-level AGC stock accounting.…”

Section: Introductionmentioning

confidence: 99%

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

2016

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Abstract:This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data.

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Section: Introductionmentioning

confidence: 99%

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

2016

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“…Carbon sequestration is generally presented in biomass [4], [5] or net primary production (NPP) [6], [7]. The amounts uptakes from or releases to the atmosphere through plants' photosynthesis (the gross primary production, GPP) or respiration (Ra) respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many studies have concerned prediction of regional NPP [9]- [16], and many studies indicated that the potential of carbon sequestration could be achieved by land management practices, such as sustainable timber production and farm afforestation [5], [17]- [19]. Because of constant change of NPP among terrestrial ecosystems or LULC types [8], an accurate LULC map is very important to support a precise estimation of NPP or carbon sequestration.…”

Section: Introductionmentioning

confidence: 99%

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Lin

Tsogt

et al. 2015

Information Processing in Agriculture

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Changes of Land Use and Land Cover (LULC) affect atmospheric, climatic, and biological spheres of the earth. Accurate LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity reduction. This paper examined effects of pansharpening and atmospheric correction on LULC classification. Object-Based Support Vector Machine (OB-SVM) and Pixel-Based Maximum LikelihoodClassifier (PB-MLC) were applied for LULC classification. Results showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral image. Nevertheless, pansharpening plays much more important roles on the classification accuracy than the atmospheric correction. It can help to increase classification accuracy by 12% on average compared to the ones without pansharpening. PB-MLC and OB-SVM achieved similar classification rate. This study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82% and 89% respectively. A combination of atmospheric correction, pansharpening, and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

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“…In 2008, a few permanent plots were deployed over the broadleaf forest for research of forest growth [ 80 , 81 ], where a series of ground inventory is annually conducted for stand dynamics [ 17 ]. The data have been successfully derived using the forest canopy height model [ 82 ] and have investigated the feasibility of global/local target detection algorithms for the detection of NGL by our prior studies [ 4 , 76 , 78 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Chen

Lin

et al. 2021

Sensors

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The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.

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Comparison of carbon sequestration potential in agricultural and afforestation farming systems

Cited by 19 publications

References 23 publications

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

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