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

damen, m. c. j.

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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:: 856662364

Title:: Remote sensing for resources development and environmental management

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

Scope:: VI, Seiten 959-1074

Year of publication:: 2016

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856662364

Signature of the source:: ZS 312(26,7,3)

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:: Invited papers

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Approaches to monitoring renewable resources using remote sensing and geographical information system. Lennart Olsson

Document type:: Multivolume work

Structure type:: Chapter

1048 Figure 2. Schematic diagram showing the kinds of data included in the modelling of supply and demand of fuel-wood in semi-arid Sudan. Source: Olsson et.al. (1985) 7.4 Combined supply/demand modelling When quantifying e.g. vegetation amount by use of remote sensing, the result is normally presented as amount per area unit. When dealing with supply and demand of resources, the aim is to translate the area based measure into a per capita based measure, and then compare, in one way or another, the supply with the demand. I will briefly outline two different attempts to model supply and demand, both carried out in semi-arid parts of the Sudan, dealing with fuel- wood (Olsson et.al. 1985 and Olsson 1985) and millet production (Olsson 1985). In the case of wood resources, the modelling was based on Landsat MSS derived data on supply of woody biomass (tons/ha) and estimated, by interviews, annual consumption of wood, in Kordofan province of the Sudan. The modelling compared the demand for each village with the resources within a certain walking distance, a schematic description of the different kinds of data and analyses is presented in Figure 2. As a result the walking distance needed to satisfy the demand or the actual wood deficit was calculated for each village. The methodology is readily appli cable to studies on future development of the wood situation, through simulation of different variables, population increase, migration, afforestation, land management etc. In the case of millet production, the modelling was based on population distribution and statistical prediction of millet productivity. Spatial modelling yielded detailed information on land availability (Ha/capita). This was achieved by, following the rule that a piece of land belongs to the nearest settle ment, applying an algorithm delineating polygons around every settlement (Thiessen polygons). The number of hectars available per capita could then be calculated and represented as a raster image. The yield of millet was predicted from climtic data, represented as a trend surface, and combined with the land availability. The combination of productivity estimates and land availability made it possible to project the per capita production. When combined with more accurate measurements of actual crop return, based on remote sensing methodology, this approach will be a powerful instrument for studies of spatial distribution of food production. 7.5 Soil erosion modelling and prediction Much research has been undertaken to establish mathematical relationships between various landscape features and rate of soil erosion. The most wellknown example is perhaps the Universal Soil Loss Equation (USLE) by Wishmeier & Smith (1960) , primarily developed for North American conditions, but subsequently adjusted for applications in other areas. The traditional methodology for mapping of erosion hazards is based on measurements in limited catchments, from which approximations have then been done. It is, however, today possible to apply a mathematical erosion model in a GIS, in order to carry out spatially full-covering mapping of poten tial soil erosion. The most important factors controlling erosion are topography, vegetation cover and land use. All these factors can with high accuracy and speed be treated using the combination of remote sensing and GIS. REFERENCES Bartlett D.S., R.W.Johnson, M.A.Hardisky & V.Klemas 1986: Assessing impacts of off-nadir observation on remote sensing of vegetation: use of the Suits model. Int. J. Remote Sensing, vol. 7, pp 247-264. Bauer M.E., J.E.Cipra, P.E.Anuta & J.B.Etheridge 1979: Identification and area estimation of agricultural crops by computer classification of Landsat MSS data. Remote Sensing of Environment Vol:8 pp.72-92 Bush & Ulaby 1978: An evaluation of radar as a crop classifier. Remote Sensing of Environment Vol: 7, pp. 15-36 Cihlar J., R.J.Brown & B.Guindon, 1986: Microwave remote sensing of agricultural crops in Canada. International Journal of Remote Sensing, Vol 7, pp 195-212. Cohen J. 1960: A coefficient of agreementof nominal scales. Educational and Psychological Measurement Vol:20, pp. 37-46. (in Rosenfield & Fitzpatrick- Lins 1986) Cohen J. 1968: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin Vol: 70, pp.213-220 (in Rosenfield & Fitzpatrick-Lins 1986) Coiner J.C. 1980: Using Landsat to monitor changes in vegetation cover induced by desertification processes. Proc. 14th International Symposium on Remote Sensing of Environment, pp.1341-1351. Colwell R.N. 1956: Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia Vol: 26 pp. 223-286. (in Reeves 1975) Crist E.P. & R.J.Kauth 1986:The tasseled cap de mystified. Photogrammetric Engineering & Remote Sensing Vol:52, pp 81-86. Curran P.J, 1980: Multispectral remote sensing of vegetation amount. Progress in Physical Geography, Vol: 4, No: 3, pp. 315-341. Curran P.J, Longman, I Duggin M.J. Evaluation Applied Op Ewalt D. 19 study Vol. Report 15 Townshend Graetz R.D., 1982: Th resource applicatic Australian Symposium Lands, Cai Guignard J. and operat Journal Vc Haralick R.M and spatia Sensing of Hay A.M. 19 accuracy. Sensing Vc Hellden U. digital da an enviro University & Notiser- Hellden U., remotely the Sudan. Rome 2-6 D Hoffer R.M consider at techniques Swain & Da Holben B.N. assessment Photogramm 46 pp. 651 Hord R.M. criteria. Sensing Vo Hugli H. & reflectanc Engineerin Hutchinson Landsat a cation im Remote Sen Justice C.O. 1985: Ana tion Using Journal of Kauth R.J. graphic developmen Landsat. Remote Sen Ch. 1103-1 Kettig R.L. multispect

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