Deriving the Spatiotemporal NPP Pattern in Terrestrial Ecosystems of Mongolia Using MODIS Imagery

Lin, Chinsu; Dugarsuren, Narangarav

doi:10.14358/pers.81.7.587

Cited by 30 publications

(17 citation statements)

References 39 publications

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“…Lin and Dugasuren [40] introduced a percentage change index to account for spatiotemporal vegetation productivity. The same method can be applied to the AGC models to show how well alternative models contributed to stand-level AGC estimation relative to a base model.…”

Section: Evaluating the Predictive Performance Of The Modelsmentioning

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: Evaluating the Predictive Performance Of The Modelsmentioning

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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“…Changes of NPP can greatly affect global carbon balance and climate change [6], which has been a key issue of ecological studies since last decades [8].…”

Section: Introductionmentioning

confidence: 99%

“…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. The high resolution LULC maps play critical roles on the issues of: 1) reducing emission from deforestation and forest degradation (REDD) [20]- [22], 2) presenting accurate large-scale LULC map and predicting LULC changes [23]- [25], 3) detecting the response of vegetation to environmental factors and estimating the spatiotemporal variations of NPP at multiple spatial scales [8], 4) predicting land surface temperature [26], and 5) calculating the large-scale/subcanopy-based heterogeneous evapotranspiration [27].…”

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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“…Abbreviation: *: The green, blue, and red circles in "relative quantity model" column stand for forest, grassland, and desert steppe respectively; and the level of the circle indicates the relative quantity among the vegetation types. , Lin & Dugarsuren 2015. Conversely, in the forest, temperature plays a main role in the timing of OG , Chu & Guo 2012.…”

Section: Discussionmentioning

confidence: 99%

Temporal variations in phenological events of forests, grasslands and desert steppe ecosystems in Mongolia: a remote sensing approach

Dugarsuren

Lin

2016

Ann. For. Res.

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. Temporal variations in phenological events of forests, grasslands and desert steppe ecosystems in Mongolia: a remote sensing approach. Ann. For. Res. 59(2): 175-190.Abstract. The occurrences of phenological events are important variables in the evaluation of the influence of climate change on terrestrial ecosystems. Changes in climate can cause significant changes in the timing and duration of phenological events. Information related to large-scale phenology is therefore useful for exploring the seasonal and inter-annual variability in vegetation-climate interactions. This study aimed to obtain the timing and temporal pattern of the onset of green-up and dormancy (OG and OD) and length of growing season (LGS) using the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) for Forest, Grassland, and Desert steppe in Mongolia over the 10-year period from 2000 to 2009. Results demonstrated that phenological events can be differentiated by multi-temporal NDVI and EVI data and that the timing ascribed to OG and OD is slightly different between the two indices. In general, NDVI and EVI agreed that the OG of forest varied from late May to middle July. The OG of grassland and desert steppe suggested by NDVI were from late May to middle July and from middle May to middle July respectively, however EVI suggested an earlier timing of OG. NDVI and EVI also showed similar variation for the timing of OD from early September to early October. The derived LGS showed the least variation for forest, highest variation for desert steppe, and only moderate variation for grassland. Grassland and desert steppe experienced high positive and negative variations in the OG and LGS during the years from 2000 to 2009. These regions might be vulnerable to global change and are likely to be strongly affected by meteorological changes. Keywords phenological events; onset of green-up; onset of dormancy; length of growing season; peak time of vegetation maturity; vegetation indices; MODIS NDVI and EVI

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Deriving the Spatiotemporal NPP Pattern in Terrestrial Ecosystems of Mongolia Using MODIS Imagery

Cited by 30 publications

References 39 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

Temporal variations in phenological events of forests, grasslands and desert steppe ecosystems in Mongolia: a remote sensing approach

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