XXII ISPRS Congress 2012: Technical Commission VIII

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

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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:: REGIONALIZATION OF AGRICULTURAL MANAGEMENT BY USING THE MULTI-DATA APPROACH (MDA) G. Bareth and G. Waldhoff

Document type:: Multivolume work

Structure type:: Chapter

REGIONALIZATION OF AGRICULTURAL MANAGEMENT BY USING THE MULTI-DATA APPROACH (MDA) G. Bareth*"* and G. Waldhoff* ? Institute of Geography (GIS & Remote Sensing Group), University of Cologne, 50923 Cologne, Germany - (g.bareth, guido.waldhoff)@uni-koeln.de ^ ICASD - International Center for Agro-Informatics and Sustainable Development (www.icasd.org) KEY WORDS: crop, agriculture, management, GIS, remote sensing, multi-data approach, land cover, land use, regional modelling ABSTRACT: Regional process-based (agro-)ecosystem modelling depends mainly on data availability of land use, weather, soil, and agricultural management. While land use, weather, and soil data are available from official sources or can be captured with monitoring systems, management data are usually derived from official statistics for administrative units. For numerous spatial modeling approaches, these data are not satisfying. Especially for process-based agro-ecosystem modeling on regional scales, spatially disaggregated and land use dependent information on agricultural management is a must. Information about date of sowing, dates of fertilization, dates of weeding etc. are required as input parameters by such models. These models consider nitrogen (N)- and carbon (C)-matter fluxes but essential amounts of N-/C-input and N-/C-output are determined by crop management. Therefore, in this contribution a RS- and GIS-based approach for regional generation of management data is introduced. The approach is based on the Multi-data Approach (MDA) for enhanced land use/land cover mapping. The MDA is a combined RS and GIS approach. The retrieved information from multitemporal and multisensoral remote sensing analysis is integrated into official land use data to enhance both the information level of existing land use data and the quality of the land use classification. The workflow of the MDA to generate enhanced land use and land cover data consists basically of two components: (a) the methods and data of the remote sensing analysis and (b) the methods and data of the GIS analysis. The MDA results in disaggregated land use data which can be used to link crop management information about the major crops and especially crop rotations like date of sowing, fertilization, irrigation, harvest etc. to the derived land use classes. Consequently, depending on the land use, a distinct management is given in a spatial context on regional scale. In this contribution, three case studies of different regions in Germany will be presented: (i) the dairy farm region “Württembergisches Allgäu”, (ii) the arable land region “Kraichgau”, and (iii) the diverse Rur-Watershed in Western Germany. For each of the study regions, a different MDA-based approach for regionalizing agricultural management is applied and will be discussed. 1. INTRODUCTION Since decades, regional modelling of ecosystems is in the focus of numerous research activities and publications (Li, 1996; Li, 2007; Wolf et al, 2012). For agro-ecosystems models which focus on plant growth, yield forecast, and matter fluxes are developed and are available for regional applications (Klar et al., 2008; Leip et al., 2008; Lenz-Wiedemann et al., 2010). The most important limitations of regional application of agro- ecosystem models are described by Kersebaum et al. (2007). The authors state that the "applications of agro-ecosystem models on field or regional scale are mostly characterized by a high uncertainty of input data, especially regarding soil and management information". This fairly strong statement is a result of comparing 18 different models on the same input dataset. Bareth and Angenendt (2003) and Bareth and Yu (2005) present similar findings for regional economic- ecological agro-ecosystem modelling. In this context, Bareth (2009) proposes the establishment of spatial environmental information systems (SEIS) which provides such data. In this contribution, the focus is on generating spatial management data for regional agro-ecosystem modelling. Process-based models like the DNDC model (www.dndc.sr. unh.edu) need detailed input data such as date of sowing, of fertilizing, of weeding, of tillering etc. Additionally, the amounts of the inputs are required (e.g. irrigation, fertilizer etc.). Due to the above mentioned problem and limitation of the * Corresponding author. availability of management data, different approaches for providing spatial management are described. Bareth et al. (2001) use administrative units to distribute and calculate average fertilizer inputs for townships while Neufeldt et al. (2006) use crop specific inputs for spatial modelling units. Similar disaggregated approaches are described by Leip et al. (2008) and Smith et al. (2010), especially the latter focus on a modelling tool for management data. In contrast to the described and available approaches to regionalize management data on administrative or modelling units, we propose in this contribution a new method to produce spatially disaggregated management data. The proposed method is based on the Multi-data Approach (MDA) (Bareth, 2008; Bareth and Waldhoff, 2009) and builds on crop type maps or even crop rotation (CR) maps (Waldhoff et. al, 2012). Therefore, we (i) introduce the general approach of the MDA, we (ii) present results of the land use classification for three agricultural regions in Germany, and we (iii) evaluate the potential of using the MDA to produce spatially disaggregated agricultural management data. 2. MULTI-DATA APPROACH (MDA) The basic idea of the MDA is integrating remote sensing classification data into official land use data and overlaying additional available and appropriate data sources (Bareth,

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