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

510 is an added advantage. 2 OBJECTIVES The main objective now is to develop further and use the method for accurate and precise inventory ing, mapping and monitoring of land use and land cover for regional as well as national resource management. Specifically, therefore, the method is being used, among other things, to estimate: 1. crops (and other class types) hectarages; 2. crops (and other class types) distribution and intensity zones; 3. crop mix zones; and 4. spread of cultivation into the rangeland. Outlined below is a summary of the procedures and preliminary results for a land use and land cover survey for Meru District in 1985. Similar surveys, whose results are briefly compared, have been accomplished for 9 other districts and more are in the process. 3 METHODS The method entailed multistage sampling techniques using low altitude aerial vertical photographs. The district covering approximately 9850 km^ was divided into 5x5km square sampling units using the UTM grid system and east-west sampling flight lines were placed 5km apart. Two sample photographs were taken systematically at every 2.5km UTM grid inter sections in the high potential region and at 5km in the marginal rangelands. Flight height was approximately 488m above ground level. Navigation used the Global Navigation System (GNS) fitted in the twin-engine Partenavia aircraft. The photographic system used was a 35mm Nikon F3 camera with a 20mm lens and 35mm ektachrome 200 slide films. This gave positive transparencies covering approximately 560m x 850m on the ground, with a scale of 1:24,380. The positive trans parencies were each overlayed and fixed with 91 (7x13) systematically placed dots on a transparency and observed under a hand held slide viewer or sometimes projected onto a screen. Crop and other cover classes coincident with each dot were identi fied by photointerpretation. The area covered by each class type was then estimated by the pro portion of dots for that class type to the total number interpreted in the 5x5km sample unit and expressed as a percentage. This was the basis for the construction of overlays for each class type in terms of distribution, density, and crop mix zones. A framework of procedures for regional and field familiarization before and during interpretation were established and followed to increase accuracy and consistency in interpretation. Before actual district-wide photointerpretation began preparatory procedures involved literature review; increasing relevant local knowledge through discussions; choosing one or two training photosamples and making trial runs on them in interpretation and then ground checking them for every major ecologi cal region in the district; and this last exercise enhanced the interpreters' familiarization of the land use and land cover classification system which was being used. Procedures were also laid down on how to classify dots falling on non-pure class types like mixed fields, field boundaries and single trees to avoid personal bias. The identification of land use and land cover types for every dot on the photograph was done for at least the first three levels of classification coarseness as described by Anderson et al (1976) and modified for local adoption. Accuracy and consistency of identification of class types were assessed using 'confusion tables' as outlined by Kalensky (1975,1978) and linear regression analysis between photointerpretation and field sample checks at every level of identification using aggregate data from every photo-sample field checked. The correlation coefficient was considered a measure of consistency in interpretation. Near perfect inter pretation would have a regression line of Y = X and a correlation coefficient (r) > 0.90. Mapping was partly done using Map Analysis Package (MAP) a Geographical Information System (GIS) software developed at Harvard and Yale Universities. 4 RESULTS AND DISCUSSION A total of 443 photographs were analyzed for 263 sample units. Three areas of the district, Mount Kenya National Park and Forest; Meru National Park area; and the Northern Conservation Area, were not surveyed mainly because their uses and cover types are well documented and partly because of terrain and air turbulence. For accuracy and consistency tests twenty-seven (27) photo samples were interpreted and ground checked by each of the three photointerpreters. The dots overlay method was used in assessing their interpretation accuracy and consistency. Each dot was fixed and examined by each interpreter and therefore making it possible to compare their performance. A total of 2457 dots were checked in the 27 photos. Correlations were carried out between photointerpretation and ground truth for those class types with sufficient data and occur rence for the three interpreters separately and for the three combined to give an average. Interpreta tion for the major cover types was fairly similar between the three interpreters. The interpretation accuracy test showed that land under cultivation as a class was interpreted with an average accuracy of 91% with r=0.93; improved and unimproved pasture 77% with r=0.89; bushlands 71% with r=0.90;; forests 84%; and the rest of the third level classes had few (<50) dots and photo sample occurrence for analysis. For specific crops and cover classes, maize was interpreted with an average accuracy of 83% with r=0.94; coffee 86%; tea 85%; bananas 62%; cotton 49%; ploughed land 72%. The rest had insufficient data for analysis. Table 1 below gives the percent cover for the major class types condensed from over 35 specific class types identified in the district. For example, of the cultivated land, 38% was under maize, 14% was under coffee, 12% was ploughed, 9% was fallow and the remainder 27% was distributed among 16 different crops. Distribution and intensities for each of the extensive class type was mapped and contouring approximated using MAP. The systematic dot method was therefore found to be quick and reliable for estimating areas of each class type for the whole district; a difficult dot could be discussed among the interpreters to reach a consensus. Thus it is contended that district-wide interpretation accuracy and con sistency should be higher than that given above by the accuracy tests for most class types. Previous analyses for the other districts invariably involved the projection of the slide photos onto a screen which had fifty random dots on which class types were identified. For each district an attempt was made to sample survey the whole district surface area but large tracts of water bodies and extensive gazetted forest reserves were often omitted to reduce costs and expedite the process to cover much more critical areas. Table 1 is also a summary of the percent area under the major land use and land cover types found in the sampled area for the nine districts. (The indicated areas in brackets are the sampled areas only, which may not necessarily be the area of the district).

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