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:: Field experience with different types of remote-sensing data in a small-scale soil and land resource survey in southern Tanzania. T. Christiansen

Document type:: Multivolume work

Structure type:: Chapter

398 step this map was refined (and partly modified) by the subsequent soil survey. During the soil survey the agro-ecological land units were subdivided into soil units. Due to the small scale each soil unit usually consists of a complex or an association of different soils. The mapping was done on toposheets at a scale of 1:50,000, to which the interpretation results and the agro-ecological boundaries had been transferred. 4. REMOTE SENSING DATARIAL Aerial photographs and two types of satellite imagery were used for the survey work. For the nor thern and central parts of the area recent black & white panchromatic aerial photographs at a scale of 1:50,000 were available. These photos were taken in 1982 and are of excellent quality. For the southern part of the survey area old (1969-1970) aerial photographs of the same type and scale, but of poor quality, had to be used. The satellite imagery consists of six Landsat Multi-Sp>ectral Scanner (MSS) scenes. Four of the six scenes were recorded by Landsat V (September / October 1984). The other two scenes were taken by Landsat III (November 1982). Two typies of imagery were produced of each scene: - standard false-colour composite (FCC), - colour-enhanced imagery (ratio/IHS encoded data). The result was displayed on paper prints at a scale of 1:250,000. The ratio/IHS encoding procedure has been des cribed in detail by Haydn et al. (1982). The abbre viation 'IHS' refers to the three colour components intensity (I), hue (H) and saturation (S). Single band values or band ratio values are assigned to each of these three components. Summarized and sim plified, the data transformation works as follows: - band 7 value is coded into intensity, - the ratio value band 5 / band 4 is assigned to the hue (as the ratio value declines from high to low, the hue changes from red to yellow, green and purple), - the ratio value band 5 / band 6 is assigned to the component saturation (high values result in saturated colours and vice versa). As compared with the FCC, the IHS imagery has the following two main advantages: - better colour discrimination, - easier colour interpretability. An additional advantage for soil mapping is that in dry areas with little vegetation for several soils the actual soil colour corresponds to the colour on the IHS image (e.g. red soils appear red, yellow soils show up yellow). A disadvantage of IHS is its inferior discrimina tion of texture differences (e.g. drainage lines are generally better visible on FCC). 5. REQUIREMENTS OF REMOTE SENSING DATA FOR SMALL- SCALE SOIL AND LAND RESOURCE SURVEYS Three main aspects have to be taken into account to define the requirements and to assess the use fulness of different types of remote sensing data for a specific survey: - costs (for production and interpretation), - 'handling' qualities (time and instruments re quired for interpretation and transfer of the interpretation results to the base map), - information content (related to the specific survey task). Cost and handling requirements are almost the same for most types of surveys, i.e. the material should be cheap and easy to handle. Comparing aeri al photographs and satellite imagery concerning these two points, the prevailing advantages of the latter are generally accepted and need not be dis cussed here. The more interesting question is which typo of remote sensing data supplies the surveyor with the highest amount of information for his specific sur vey tasks. In a land resource survey at a scale of 1:250,000 the main mapping task is the delineation and iden tification of broad homogeneous units, mainly based on climate and physiography. In a small-scale soil survey at a scale 1:100,000 the surveyor has different mapping tasks: - delineation of soil boundaries, - identification of the soils in each mapping unit, - assessment of the relative portion (percentage) of each soil within the mapping unit. The following chapters demonstrate that the uti lity of different remote sensing data for these survey tasks depends on many factors and varies from area to area. 6. GENERAL RESULTS IN DIFFERENT AGRO-ECOLOGICAL ZONES The. survey area has been subdivided into thirty- six agro-ecological land units, grouped into five major agro-ecological zones. The coding of the five agro-ecological zones was adopted from an existing small-scale map for the whole country (Samki 1982), but the zone boundaries were modified considerably. Table 2 gives a summary of the most important characteristics of these five zones. 6.1 Zone 16 Zone 16 covers the warmest and driest part of the Iringa Region. Part of the zone consists of rocky hills with Miombo woodland and stony shallow soils. A great portion of the zone is almost flat and co vered by an open, degraded, rather uniform Acacia- thornbush vegetation. Due to the dry climate and overgrazing there is very little grass cover in the dry season resulting in much bare soil being expos ed. In these flat areas heavy dark to yellow brown sandy clay loams and dark cracking clays are the prevailing soils. In this zone the satellite imagery was of much greater use than the aerial photographs. While the major boundaries (i.e. the boundaries between the agro-ecological land units) could easily be mapped from either typo of remote sensing data, the aerial photographs were almost useless for any further subdivision of the units. This was mainly due to the almost flat relief and the rather homogeneous vegetation. Main mapping supports in this zone were the spec tral characteristics of the exposed bare soil sur face. These spectral differences are excellently displayed by the IHS image (see Figure 1 IHS). Due to high albedo values which reduce the colour con trasts the spectral differences were much less pro nounced on the FCC (Figure 1 FCC). Textural differ ences, however, were more clearly visible on the FCC. Hence, drainage lines could be better identi fied on the FCC (Figure 1 FCC), but little use could be made of this advantage due to the very uniform drainage system. Major interpretation problems in this zone were caused by an area covered with dense bush and some areas where a veneer of light sand covers a dark loam subsoil (see Chapters 7 and 8). 6.2 Zone 8 Zone 8 lies about 500 metres higher than zone 16. Therefore the area is less arid and considerably cooler, but it still belongs to the drier part of the Iringa Region. Characteristic for zone 8 are piediplains with Table 2. Climate ( fication) Dominant Mean annu Physiogra teristics Dominant land use Dominant many floode consis tion ( maize. The and lc some i footsl affect verely in sateil graphs reflec were c most t gery. better Figure The the s£ lineat ciatio gradua on the tern. The map er the tv, p»enden also c small the sn ly or imager identi scale Gen burnt very £ ferent diffic (e.g. niness simila give t p>ectiv graphs

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