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:: 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:: National land use and land cover mapping: The use of low level sample photography. R. Sinange Kimanga & J. Lumasia Agatsiva

Document type:: Multivolume work

Structure type:: Chapter

511 Table l. Land use and land cover statistics for 10 districts in Kenya, as a percent of the total land surveyed. Land use and land cover District Class type Meru (6575) Kisii *(2375) South Kisumu Nyanza (2200) (5500) Bung- oma (2475) Mur- anga (1875) Nyand- arua (2400) Busia (1450) Ker- icho (4800) Trans- nzoia (2075) Cultivated (crops, ploughed) 25.1 43.9 27.9 15.1 41.3 52.6 17.1 30.3 20.0 39.9 Pasture (improved, unimproved) 20.1 32.5 36.2 71.2 36.0 20.1 58.0 51.2 45.5 45.2 Bush and Shrublands 38.3 0.0 20.6 5.6 9.8 6.5 11.8 6.3 12.4 3.1 Forest/Woodlands 8.1 8.2 0.0 0.4 9.0 0.0 0.0 0.0 15.4 4.5 Transportation (roads, paths, etc.) 1.3 3.1 3.1 1.5 2.9 4.7 2.1 1.9 2.2 1.4 Structures (buildings) 0.6 2.4 1.2 0.5 0.0 1.8 0.0 0.7 0.1 0.0 Woodlots 1.0 0.0 0.0 0.2 0.0 9.6 0.9 0.0 0.0 4.4 Hedges 0.6 8.4 6.6 2.2 0.7 4.7 1.8 4.2 3.5 0.6 Swamps/Rivers 0.8 0.6 4.2 0.7 0.2 0.0 5.1 2.8 0.7 0.1 Barren (rocks, gulleys) 2.3 0.0 0.4 0.3 0.0 0.0 0.9 0.0 0.0 0.8 Others 1.9 0.9 0.0 2.3 0.0 0.0 2.3 2.6 0.1 0.0 Total 100.1 100.0 100.2 100.0 99.9 100.0 100.0 100.0 99.9 100.0 Sources: Agatsiva 1984, 1985; Epp et al 1983; Muchoki 1985a,b; Mwendwa 1984, 1985; Ottichilo 1985; Peden et al 1984. * area surveyed in km^ - See text. Significant changes in future of any of these statistics in any district in area or amounts will be a reflection of a process of changes going on as a reaction to certain pressures or certain manage ment policies. A more detailed comparison between districts and detection of changes will be more useful when the data is disaggregated into differ ent ecological strata. The use of aerial sample photography for the mapping of resources has been advocated and dis cussed before by many authors for example Berry and Baker (1968) and Robertson and Stoner (1970). However, its application to a large scale mapping project has been lacking. The Kenyan project was adopted after a trial run in Kisii District (Epp, Killmayer and Peden 1983). In the Kisii study and those that followed the photo samples were strati fied and analyzed on the basis of sub-district administration units. Thus when the average per cent land covered by a class type was mapped, the distribution was depicted as evenly spread through out the administration unit through differential shading or pie charts. However for Meru District, class types' intensity and extents were first mapped independent of any factor. This method of mapping class type and intensities gives a picture close to real distribution within any type of stratification consequently overlayed. Stratifica tion can be on basis of administration units, soils, vegetation, climate or human population for various types of studies (e.g. factors affecting a class type distribution) in an effort to make rational development plans. Present generations of GIS systems with analytical capabilities can now perform these functions much faster. A major use of this type of mapping is the detec tion and measure of the movement and cultivation activities into the marginal areas. Epp and Killmayer (1982) suggested that areas with land 10% or more under cultivation be designated as 'agri cultural'. Jaetzold and Schmidt (1982) have how ever defined in details agroecological zones in Kenya in which agricultural areas are ecologically delineated. Using these two criteria, newly set tled areas as well as cultivation extending beyond ecologically acceptable boundaries can be detected using the photosample methodology described here and by map overlays. A recent experimentation using similar principles of methodology has been reported from Nigeria with success in providing fairly accurate statistics for planning (Bauchi 1984; Nigeria 1984). Mapping was done using pie charts and proportionate circles on each grid cell, to depict coverage or intensity of a class type in that cell. 5 CONCLUSION The method described above has proved useful in crop hectarage estimation and distribution studies in the intensive agricultural areas of Kenya where a great variety of crops are grown in small land segments. Accuracy and consistency were found high in the second and third classification levels and in those finer class types which have extensive areal coverage like maize. However, some vegetation cover type classifications need more refined description and definition to reduce their class confusions, for example between unimproved pasture and bushlands, which was responsible for their relatively lower interpretation accuracy. It is anticipated that the infornmtion from all the districts will eventually be combined to give a national overview for every class or combination of class types. This is expected to be possible because the data is being collected using same method, measurement units (percent cover) as well as the UTM system base maps for every district, and thus produce a detailed land use and land cover map of Kenya. It is also anticipated that it will be possible to detect areas of land use conflicts and potential desertification frontiers. 6 ACKNOWLEDGEMENT This study was funded by the Kenya Government through KREMU, while our studies, including the project time, were made possible jointly by the Kenya Government and the Canadian International Development Agency (CIDA). We are grateful to both. Professor Tom Henley of the Natural Resources Institute, University of Manitoba, and

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