Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Lotsch, Alexander; Tian, Yuhong; Friedl, M. A.; Myneni, Ranga B.

doi:10.1080/01431160210154858

Cited by 88 publications

(55 citation statements)

References 26 publications

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“…The input data include spectral and temporal information from MODIS bands 1-7, supplemented by the Enhanced Vegetation Index (EVI) [35]. The five different land cover classifications in MCD12Q1 are: (i) The 17-class International Geosphere-Biosphere Programme (IGBP) classification, MODIS LC-1 [8,36]; (ii) The 14-class University of Maryland classification (UMD) [9]; (iii) A 10-class system used by the MODIS LAI/FPAR algorithm [37,38]; (iv) An 8-biome classification proposed by Running et al [39]; and (v) A 12-class plant functional type classification [40].…”

Section: Modis Land Cover Type Productmentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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Abstract:Forests are one of the most important components of the global biosphere and have critical influences on the Earth's ecological balance. Regularly updated forest cover information is necessary for various forest management applications as well as climate modeling studies. However, map products are not widely updated at continental or national scales because the current land cover products have overly coarse spatial resolution or insufficiently large training data sets. This study presents the results of forests distribution and variation information over China using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data with the first layer of MODIS Land Cover Type product (MODIS LC-1). The NDVI time series histogram characteristic curves for forestland were estimated from MODIS LC-1 and MODIS NDVI time series data.

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Section: Modis Land Cover Type Productmentioning

confidence: 99%

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Zhao

Zhou

et al. 2013

Forests

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“…Most likely, this is due to the overlap in parameterization between the classes. Applying CRASh to image data will have to reveal if the chaotic spatial or temporal variation of the solution observed by other authors [41] is overcome with the parameterization used in this study.…”

Section: Land Cover Classificationmentioning

confidence: 99%

“…Innovation of the approach is not the automated model inversion per se, but the exploitation of novel methods to ensure stable RTM inversions for mono-temporal high resolution data in an operational setting where inclusion of a priori information on the variables is strongly hampered. Whereas operational algorithms for medium to low resolution imagery can rely on more frequent coverage, which facilitates land cover classifications in support of retrievals optimized for specific biomes [13,41], a similar option is not offered for the locally operating high resolution systems. Thus, alternative ways are exploited.…”

Section: Introductionmentioning

confidence: 99%

“…Land cover classification schemes help to split the problem into sub-domains for which prior information is attributed separately [39,40]. Nevertheless, land cover classification may introduce problems due to misclassification and attribution errors and lead to artefacts at the limit between classes translating into more chaotic spatial or temporal variation of the solution [41]. Moreover, many of the biomes found at the Earth surface are better characterized by gradual transitions then by a crisp classification.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

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“…This product is derived from the MODIS output first released by the United States National Aeronautics and Space Administration (NASA) at the end of 2008, with processed yearly observation data from the Terra and Aqua satellites applied to depict land cover types. The chosen dataset consist of five land cover classification systems: IGBP global vegetation classification scheme [32]; UMD vegetation classification scheme based on the modified IGBP classification system [33]; LAI/FPAR scheme adopted by MODIS Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR) products (MOD15) [34,35]; NPP scheme adopted by the MODIS net primary productivity (NPP) product (MOD17) [36]; and Plant Functional Type (PFT) land cover classification scheme [37]. In our study, five MCD12Q1 data layers updated in 2014 are selected.…”

Section: Modis Datasetmentioning

confidence: 99%

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Liang

Zuo

Huang

et al. 2015

IJGI

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Abstract:Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The "woodland" LAI/FPAR is the worst, with a spatial OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2520 similarity (O) of 58.17% due to the misclassification between "woodland" and "others". The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

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Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Cited by 88 publications

References 26 publications

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Distribution and Variation of Forests in China from 2001 to 2011: A Study Based on Remotely Sensed Data

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

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