XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

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

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; 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:: [VIII/6: Agriculture, Ecosystems and Bio-Diversity]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ANALYSING AND QUANTIFYING VEGETATION RESPONSES TO RAINFALL WITH HIGH RESOLUTION SPATIO-TEMPORAL TIME SERIES DATA FOR DIFFERENT ECOSYSTEMS AND ECOTONES IN QUEENSLAND M. Schmidt, T. Udelhoven

Document type:: Multivolume work

Structure type:: Chapter

B8, 2012 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia ANALYSING AND QUANTIFYING VEGETATION RESPONSES TO RAINFALL WITH HIGH RESOLUTION SPATIO-TEMPORAL TIME SERIES DATA FOR DIFFERENT ECOSYSTEMS AND ECOTONES IN QUEENSLAND M. Schmidt , T. Udelhoven? 2 Department of Science, Information Technology, Innovation and the Arts, Remote Sensing Centre, 4001 Brisbane, Australia — Michael .schmidt@derm.qld.gov.au ^ Remote Sensing and Geoinformatics, University of Trier, 54286 Trier, Germany - udelhoven@uni-trier. de Commission VI, WG VI/4 ABSTRACT: : KEY WORDS: Landsat, MODIS, NDVI, distributed lag models, time series Vegetation responses and ecosystem function are spatially variable and influenced by climate variability. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to combine MODIS (Moderate Resolution Imaging Spectrometer) and Landsat TM/ETM+ (Thematic Mapper/ Enhanced Thematic Mapper plus) imagery for an 8 year dataset (2000-2007) at 30m spatial resolution with 8 day intervals. This dataset allows for a functional analysis of ecosystem responses, suitable for heterogeneous landscapes. Derived vegetation index information in form of the NDVI (Normalised Difference Vegetation Index) was used to investigate the relationship between vegetation responses and gridded rainfall data for regional ecosystems. A hierarchical decomposition of the time series has been carried out in which relationships among the time-series were individually assessed for deterministic time-series components (trend component and seasonality) as well as for the stochastic seasonal anomalies. While no common long-term trends in NDVI and rainfall data in the time period considered exist, there is however, a strong concurrence in the seasonally of NDVI and rainfall data. This component accounts for the majority of variability in the time-series. On the level of seasonal anomalies, these relationships are more subtle. The statistical analysis required, among others, the removal of temporal autocorrelation for an unbiased assessment of significance. Significant lagged correlations between rainfall and NDVI were found in complex Queensland savannah vegetation communities. For grasslands and open woodlands, significant relationships with lag times between 8 and 16 days were found. For denser, evergreen vegetation communities greater lag times of up to 2.5 months were found. The derived distributed lag models may be used for short-term NDVI and biomass predictions on the spatial resolution scale of Landsat (30m). 1. INTRODUCTION The vegetative surface cover has an important function in the earth system (Steffen and Tyson 2001) which is linked via several feedback mechanisms to hydrological and climatological processes. Identifying and quantifying these linkages delivers important insight for environmental modelling, management and informed decision making. Satellite earth observation data with high temporal repeat intervals such as AVHRR (Advanced Very High Resolution Radiometer) or MODIS (Moderate Resolution Imaging Spectroradiometer) deliver spatially dense information about the earth surface and are well-suited for monitoring continental scale surface processes. Vegetation indices derived from earth observation systems have proved to be useful to describe surface vegetation behaviour (Tucker 1979). The most prominent of these is the Normalised Difference Vegetation Index (NDVI) which is proportional to the amount of photosynthetically active radiation absorbed by green vegetation (Asrar et al. 1984). Spectral information from the AVHRR sensor in channel 1 (0.58 to 0.68 micrometers) and channel 2 (0.75 to 1.1 micrometers) are combined to formulate the NDVI as follows: NDVI=(ch2-ch1)/ch1+ch2 The NDVI is widely used to monitor vegetation (Tucker 1979) and ecosystem processes (Pettorelli et al. 2005). For example, Lotsch et al. (2005) used AVHRR time series data (1981-1999) to monitor responses of terrestrial ecosystems to drought in the northern hemisphere. Nemani et al. (2003) have studied the climate-driven increase in global terrestrial net primary production using AVHRR data, building bioclimatic indices and vegetation growth limiting factors. Examples where NDVI and rainfall were related are widespread in the literature (e.g. Ecklundh, 1998, Anayamba & Tucker, 2005; Udelhoven et al., 2009; Schmidt et al., 20102). At regional or local scales are data with higher spatial resolution required. Landsat Thematic Mapper (TM) imagery have proven to be useful in many vegetation monitoring applications at regional scale (Xie et al, 2008, Danaher, 2010). A combination of MODIS and Landsat imagery via data fusion have successfully be used to establish a temporally dense (e.g.

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