Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: QUALITY ASSESSMENT OF GLOBAL MODIS LAI PRODUCT FOR THE REGIONAL SCALE APPLICATIONS Sun-Hwa Kim and Kyu-Sung Lee

Document type:: Multivolume work

Structure type:: Chapter

ul 2004 TM+ study. ' than 2 any inopy ta to LAI. plane ontrol maps. 1 raw 'essed of the nsor’s image el for model tained sing se the aspect. nethod ng the model d have 1duced | Yoon, innaert 1 LAT ETM+ Xt were wiation 1 of the ind the jalysis. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 The empirical models to link the spectral reflectance to the field measured LAI were developed separately for each forest type as well as for all forest types. In this study, we tried to use several vegetation indices (SVI) as independent variables to the multiple regression model. Three other spectral indices of brightness (BR), greenness (GN), and wetness (WT) were also created by the tasseled cap (TC) transformation. To build the optimal statistical regression model to estimate LAI for entire study area, we compared several sets of independent variables that are subset of a few spectral vegetation indices. Two multiple regression models were built for each of two forest types. In addition to the forest cover map, we also need a land cover map to validate the empirical LAI estimation algorithm. A land cover map, in which the class categorization is comparable to the MODIS Land cover type 3- scheme (9 classes), was obtained by ordinary maximum likelihood classification method. Although the major portion of the study area is forest, it also includes small and segmented agricultural areas. The LAI values for the grass and croplands (mostly rice paddy) were adapted from the previous study by Hong et al. (1998). Quality assessments of MODIS LAI product As shown in Figure 3, the operational algorithm for producing MODIS LAI uses two MODIS land products of the surface reflectance (MODO09) and land cover (MODI4). The 1km resolution MODIS LAI products are produced every 8 days, which corresponds to the maximum value composition interval to remove cloud cover. LAI values are calculated by mathematical inversion of a rather sophisticated canopy reflectance (CR) model that uses MODO09 and MOD 14. If the CR model-based main algorithm fails, a backup algorithm based on the empirical relationship with vegetation index is triggered to estimate LAL The MODIS LAI image that corresponded to the date of the reference LAI map was obtained. Since the MODIS LAI value is separately calculated by cover type, the MODIS land cover products of were also acquired. MODIS land products can be directly obtained from the Earth Observing System Data Gateway (EOS, 2003). The MODIS LAI and land cover products supplied by the EOSDG is originally referenced by the sinusoidal map projection. To compare with the reference LAI map of the study site, the MODIS products were geo- referenced to the Transverse Mercator map projection by using the MODIS reprojection tool (MRT) software provided by NASA. To compare the reference LAI surface with MODIS LAI product, the reference LAI map having 28.5m pixel size was rescaled to Ikm pixel size. For the quality assessment of MODIS LAI data, we applied three phases of 1) quality of input datasets, 2) the MODIS LAI estimation algorithms, and 3) LAI value by land cover type (Figure 3). The accuracy of the MODIS land cover product was assessed by the reference land cover map. We also analyzed the effects of cloud cover at each pixel location for the MODIS reflectance data. After the validation of input datasets, we compared the reference LAI map with the MODIS LAI data by the estimation algorithms. Within the scene, some pixels had LAI value from the main CR-based algorithm and the other pixels had LAI value from the NDVI-based backup algorithm. As LAI value is also very sensitive to vegetation types, we tried to analyze the MODIS LAI value by different vegetation type (forest vegetation and grass /cropland). assessment of Estimation of LAI MODIS LAI For Land Cover types Y MODIS LAI product | Quality | Assessment | : of Input data | MSS MODIS - Estimation | Reflectance Land cover classified accuracy | (MOD09) (MOD14) of MODIS Land | cover ] - Assessmentof | Cloud effect in | MODIS | reflectance | | Y 7 | MODIS LAI Main Quality | Algorithm assessment of | - 3D Canopy Radiative MODIS LAI | Transfer Modeling for Algorithm | type i: Back-up | Fal, Algorithm | - - Empirical | Success modeling used | NDVI | Quality | " | | | | | | Figure 3. Flowchart of producing MODIS LAI and schemes of quality assessment of MODIS LAI RESULTS AND DISCUSSIONS Two separate multiple regression models to predict LAI for the reference map were developed for each of coniferous and deciduous forest. To avoid over-fitting problem of too many independent variables (five SVIs), we imposed the rule that only three variables can be selected for each model. Table 1 shows the selected independent variables, R^ value, and root mean squared error of each of two models developed. Although the use of two separate estimation models requires additional effort of classifying the forest into two species groups, it should be a better approach to obtain more reliable LAI map. We generated the high-resolution reference LAI map by applying the best regression models to the radiometrically corrected reflectance data. Using the reference land cover type map, the coniferous and deciduous forests were extracted prior to applying the models and the grass and croplands were given the same LAI value that was measured in September. Table 1. Regression models to estimate LAI over the study area Selected independent 2 ; Type variables (SVI) R RMSE Coniferous RSR, BR, GN 0.8018 | 0.6289 Deciduous NDVI, RSR, WT 0.3413 | 0.4504

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