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:: 856343064

Title:: Remote sensing for resources development and environmental management

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

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Contribution of remote sensing to food security and early warning systems in drought affected countries in Africa. Abdishakour A. Gulaid

Document type:: Multivolume work

Structure type:: Chapter

458 2 CROP PRODUCTION FORECASTING CROP PRODUCTION FORECASTING Crop production forecast is an important parameter in the food security programme of a developing country. Therefore, in order to apply remote sensing effectively the following preconditions should be met: - The availability of the data during the actual growing season must be guaranteed. The acquisition of the relevant satellite remote sensing data for crop forecasting purposes in the vegetational season is one of the major constraints facing the users in many parts of Africa. This is mainly due to the lack of ground receiving stations that cover the region between the Sahara and Southern Africa. Moreover, the period between the order and the delivery of the image data lies between 4-8 weeks. There are at present serious proposals regarding the introduction of mobile receiving stations which could be used to support the development projects in many developing countries. - The data should be interpreted as early as possible. In order to be able to deliver information well in time, an immediate processing and analysis of the remote sensing data is necessary. This requirement would be genuinely fulfilled only if enough well-trained and motivated personnel is available. - The interpretation methods should take the regional or the local conditions into consideration. Owing to the fact that the derivation of information from remote sensing data can be conducted in various methods such as the visual image interpretation, digital image class ification, or a combination of both, the choice of the method of interpretation should suit the available resources. - The allocation of enough funds for this purpose should be ensured. In order to establish an efficiently functio ning crop forecasting programme, financial support should be ensured for the technical and administrative aspects. The drought affected countries suffer from inadequate financial resources and the lack of skilled man-power aggravates the situation. These make a technical assistance either international or bilateral character indispensable. However, this technical assis tance should not be unnecessarily prolonged as is with many development projects. The components of crop production forecasting are the identification of crop type and the estimation of crop area, monitoring of crop condition, and the estimation of crop yield. 2.1 Estimation of crop area In most of the drought affected countries in Africa, dry farming or animal rearing is the main occupation of the rural population. The agricultural area is therefore very vast and mostly dispersed over several provinces. The pattern of cultivation is diversified and the fields are irregular. Though the number of newly registered farms increases yearly, the land in the traditional hands is usually not included in the official statistics. However, in order to conduct a sound crop forecasting the estimation of the area under crop is essential. Ground field survey would take a long time and is costly. The conventional aerial survey, though accurate is expensive. Therefore the best method is the derivation of information from satellite images, aerial photographs and a limited ground field survey (fig.l), in combination with the available ancilliary data on soil, topography, and location. 2.2 Monitoring of crop condition and growth The rainfall fluctuations - timing, frequency, quantity and intensity - is very great in most of the arid and semi-arid countries of Africa. The monitoring of crop condition and crop growth is therefore an essential part for the estimati- Figure 1. A simplified diagramme showing the estimation of crop production using remote sensing data. on of the eventual area under adversely affected conditions. Crop condition monitoring is mainly confined to cash-crop plantations. This zone is also a fertile breeding area for the desert locust which is a great hazard to the crops in the region if not carefully and continuously monitored. Several experim ents have been made with satellite remote sensing data to locate the valleys which may serve as the locations for the hatching locust eggs (Hielkma 1980). 2.3 Estimation of the crop yield Crop yield, is among others a function of weather, soil and the vigor of the plants. A crop production forecast can be conducted early enough before the harvest if the yield prospects per hectare are known. There are of course several methods of estimation, namely: - The forecast of the yield based on characteristics of plant or crop, and relation-ships based on weather experiences in the former years. This method has been intensively described in the agrometeorological liter ature. - Estimation of the yield through the spectral values of the plant using a digital spectrometer or densitometer. The spectral reflectances of such a vegetation, water or sand have different characteristics as shown in (fig. 2). This method is based on the concepts of the high correlation between the density of biomass and the ratio of the sunlight reflectance of infra-red and red bands. The Kenya Rangeland Ecological Monitoring Unit made experiments in estimating maize yield in 1984 using a digital photometer. Their results according to (Peden,Mwenda 1984) were comparable with those of the Ministry of Agriculture and Livestock Development. However, there are certain limitations for mixed crops where the seperation of the responses could be difficult; or where the maize plants grow sparsely, as in (fig. 3), particularly by the rainfed agricultural areas, in which the

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