Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

damen, m. c. j.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856641294

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: IX Seiten, Seiten 551-956

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A,. A. Balkema

Identifier (digital):: 856641294

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,2)

Language:: English

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

Editor:: Damen, M. C. J.

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: 8 Geo-information systems. Chairman: J. J. Nossin

Write comment:: Wegen zu enger Bindung kommt es teilweise im Original zu Textverlust.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The potential of numerical agronomic simulation models in remote sensing. J. A. A. Berkhout

Document type:: Multivolume work

Structure type:: Chapter

907 Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede / August 1986 The potential of numerical agronomic simulation models in remote sensing J.A.A.Berkhout Centre for World Food Studies, Wageningen-Amsterdam, Netherlands ABSTRACT: Long-term experience with numerical agronomic simulation techniques has resulted in a methodology that is specifically application-oriented. Incorporated in a geographic information system, which consists of static data on land, the simulation model has proven to be a valuable tool in a quantitative land evaluation (i.e. the identification and quantification of possible land use developments). Introduction of Remote Sensing Images will introduce the possibility to monitor actual crop growth situations (land use, crop development). The ability to monitor actual crop growth using RS images enhances the detection of 'problem areas' and thus the system can be used for early warning purposes. The paper will describe a GIS system including crop growth models. The possible uses of RS images in such a system and the possibilities of such a system as a partial substitute of ground truth in RS analysis will be developed and demonstrated in the paper. 1 Introduction Satellite Remote Sensing (RS) provides a scientific tool to analyze the spatial variability in the momen tary state of the surface of a defined area on the earth, and applying the multi-temporal possibilities of Remote Sensing, the differential change of the state in space and time. The usefulness of the infor mation obtained by RS depends on the one hand on the type of RS techniques applied, including their spa tial resolution and on the other hand on the avail ability of real information about the observed re gion, i.e. the ground truth, regarding the actual land use and vegetation, the landscape and soils, the hydrological and meteorological conditions, etc. This paper discusses the possibilities for reduc ing the required ground truth data for RS image in terpretation purposes by the introduction of numer ical simulation models on plant- and crop growth within the framework of a Geographic Information Sys tem (GIS). The disciplines involved are treated and it is argued that all applications will benefit from a close cooperation. 2 A systems approach to plant- and crop growth To study the complex, continuous reality of the world, a meaningful (as related to the goal of the study) section of the reality has to be identified and separated from its environment. To obtain rele vant and useful results, such a section, i.e. a so called system - in the systems approach terminology - must be sufficiently complex to exhibit a high de gree of internal coherence. But on the other hand, it must be simple enough for comprehension and in vestigation (Chorley, Kennedy, 1971). For a region al, agricultural land use analysis the conceptual model of the system must at least cover the two main objects involved, i.e.; i. land, which comprises according to the F.A.O. description (Brinkman, Smyth, 1973) all the earth- related features such as landscape, soil, hydrology, weather, vegetation and man made structures; and ii. the farming system, or all the human activi ties that are directly and indirectly, related to agriculture in the region. The selected features or object attributes, inclu ding their observed or assumed relations, determine the type of model applied. A schematic distinction can be made between: i. stochastic models, that contain statistical re lations between some relevant and perceptible at tributes of the system and lead indirectly to the required results. The functioning of the system in terms of the flow of energy, mass and information within the system is considered a black box. Specimens of this approach are the commonly used (multiple) regression models, such as that by Wiegand and Richardson (1984) and Ambroziak (1985). The former describing the relation between incident photosynthetic active radiation, leaf area index and yield of defined crops. Ambroziak estimates yields using actual monthly total precipitation and the condition of the crop as deduced from its reflec tance, in combination with historical records on the performance of a crop in the selected regions. A disadvantage of this type of models is their in herent specific character with respect to the site and crop type; they do not separate causes and can not be applied in evaluation modules, assuming pos sible changes within the current agricultural sys tem. ii. deterministic models, where at a predefined lev el of generalization the variables and their relat ions are formulated and quantified, based on insight in and knowledge of the underlying basic processes. Such models are applicable under a wide range of conditions after a sound validation and calibration procedure (van Keulen, 1976). The Centre for World Food Studies has developed such a 'cause-and-effect' model for agricultural production, following a hierarchical approach (van Keulen, de Wit, 1982). A top-down approach is ap plied to generate production estimates (expressed in kg/ha of dry matter of various components of the crops as roots, stems, leaves and storage organ) for specific crops, cultivated at specific locations, with specific growth periods (van Keulen, Wolf, 1986; Rappoldt, 1986). The latter are characterized by their specific soil and weather conditions. For any crop, characterized by its genetic and physio logical properties, the model (fig. 1) starts with the calculation of the potential production as a function of the incident photo-synthetic active ra diation (PAR, roughly 50 percent of the global ir radiance) and the temperature only. This potential can subsequently be reduced by the negative influence on crop production of the lack or excess of water (using a water balance model with time steps of one day), the lack of plant nutrients and the occurrence of weeds, pests and diseases. Fig. 2 shows some model results: for example produc tion of Pearl Millet calculated for Dori, Burkina Faso, using long-term mean monthly meteorological

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