REMOTESENSING AND GIS

This paper aims at establishing changes in land use and land cover in Igbokoda municipality using Geographic Information System and remote sensing techniques. Three satellite images for three different epochs 1986, 1999 and 2013 were used to produce a land use/land cover map classification for Igbokoda. In determining the extent of land use/land cover changes in the township from 1986 through 1999 to 2013, Landsat images of the town were downloaded from the United State Geological Survey online archive. The images were analyzed using change detection technique (NDVI differencing) along with SRTM 90m DEM of the study area to generate the extent of the changes that have occurred. Ground trotting was carried out to ascertain the accuracy of data and the major changes in the land use/land cover. Results show that vegetation has decreased from 75.04% in 1986 to 46.81% in 2013 which was due to increase in population and rapid urbanization. In 1996 the Built-up area covers 19.6321 km2 of the study area but has increased rapidly to 39.1505 km2 in the year 1999 with an average annual increment of 2.025Km2/year. By the year 2013, the built-up area has increased to 64.1520Km2. Also in the same vein, the bare surface area which was 13.28029km2 in 1986 was increased to 39.6053 and 50.240Km2 in 1999 and 2013 respectively. On the contrary, the vegetated area of Igbokoda reduced from 196.3046Km2 in 1999 to 122.4680Km2 in 2013. This study has demonstrated that remotely sensed data and GIS based approach is timely and cost effective than the conventional method of analysis, classification of land use pattern effective for planning and management. It further shows that If the rapid change in land use is not properly manage, the situation poses a serious threat to Igbokoda town by increasing surface runoff and susceptibility to flooding.

Soil erosion assessment for watershed management is a world-wide concern for ecologists and land users. In this study soil erosion was predicted using Universal Soil Loss Equation (USLE) for Katteri watershed in Nilgiris, Tamilnadu. Topographically the study area falls under steep slope and undulating terrain comprising 49% of tea plantation, 23% of horticultural crops, and 24% of forest area. The IRS-IC LISS III satellite imagery was classified and used for preparing the landuse/landcover which estimates cover management factor (C) and the landuse practice factor (P). Using digital elevation model (DEM) as input, the slope length factor (LS) was determined by AML programme. Soil erodibility (K) values were measured for all mapped soil types in the study area. The rainfall erosivity factor (R) was directly computed from rainfall intensities. All the above mentioned USLE factors, with associated attribute data, were digitally encoded in a GIS database and converted into 30 m grid cells. Simultaneous overlaid operation on these five layers produced a resultant layer of soil erosion rate in t ha -1 yr -1 . This research confirms that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Katteri watershed.

Abstract
This study highlights the application of geophysical and remote sensing/GIS techniques in the study of the proposed Otobi dam ax-is/foundation in southeastern Nigeria in order to establish: optimal depth to bed rock, the rock type which make up the foundation, the geo-logical structures of the foundation, overburden thickness, concealed basement morphology, fractures /seepage channels in the subsurface thus, enabling the evaluation of the suitability of the proposed dam foundation. In this study, lineaments have been extracted directly from digital TM Land sat image after a series of enhancements and bands ratioing.The Arc GIS version 10.0 and ILWIS 3.1 was used to generate thematic drainage maps, lineament maps and rose diagram of structures. The interpretation of the satellite image of the area showed that the proposed dam foundation is dominated by NE-SW trending fractures and this is in tandem with most of the fractures observed on outcrops of Otobi sandstone in the area. This was followed by an electrical resistivity profiling along the proposed dam axis using the Wenner array. The 2D resistivity imaging reveals that the upper layer of the study area to be 7-10metres thick and consists of alluvial and residual soils with resistivity values ranging from 8-100 ohm-m. The second layer is over 10metres thick and has a resistivity value of 5-50ohm-m and made up of moderately weathered shales and mudstones with parallel permeable groundwater rich zones which are indicative of fractures. The results of the study indicate that the proposed Otobi dam foundation is underlain by numerous fractures which are detrimental to the dam and could serve as seepage channels through the dam.

The increasing availability of multi-dimensional remote-sensing data for large geographical areas is generating a new wave of landscape-scale research that promises to be every bit as revolutionary as the application of aerial photographic survey during the 20th century. Data are becoming available to historic environment professionals at higher resolution, greater frequency of acquisition and lower cost than at any previous time. However, to take advantage of this explosion of data a paradigm change is needed in the methods used to routinely evaluate aerial imagery and interpret archaeological evidence. Central to this shift is a fuller engagement with the concept of computer aided methods of feature detection as a viable way to analyse airborne and satellite data. This requires a reassessment of workflows and an understanding of the different types of information that may be generated, developing a multi-stage assessment based on explicit predicates vital to providing confidence in outputs, while improving our ability to work with vast datasets.
Automated and semi-automated image analysis is routine in fields such as environmental remote sensing, where they underpin the analysis of extensive datasets (see Lasaponara and Mansini (2012) for a review of these techniques which is not replicated here). While aspects of these developments have made their way into archaeological applications, they remain far from routinely used and are often viewed with suspicion. This paper outlines the status quo and key issues for readers unfamiliar with debates that have mainly played out at conferences and steering groups but are seldom committed to print. We summarise the difficulties surrounding the creation of geographically extensive, systematic datasets for heritage management and research from high-volume data derived from airborne laser scanning (ALS), satellite imagery and airborne digital spectral data. At the heart of this issue is the potential to incorporate methods that exponentially increase the rapidity with which initial historic environment datasets can be created, moving beyond the ‘human-timescales’ within which most archaeological survey is undertaken (i.e. ‘manual’ analysis), to fuller exploitation of computer assisted techniques. In order to do this we must recognise the fundamentally different but complimentary types of information that the two approaches produce, and assess their value and contribution as part of broader research objectives. Throughout we use the term computer vision to define all methods by which imagery can be processed, analysed and understood using computer generated algorithms (including classifications) and in particular with reference to the electronic replication of the abilities of human ocular perception (Sonka et al. 2008).

The increasingly tensions rising between society's need for resources and space on the one hand, and the capacity of the land to support these needs on the other hand lead to unprecedented changes in landscape configuration, and hence, to the environment. This paper analyzes changes in the agricultural landscape of Podoleni, Neamt County, Romania, in spatio -temporal terms, as a result of the modernization of the ground processing techniques, types of crops, and the degree of fragmentation of land parcels. The study was conducted using Geographical Information Systems techniques, the main materials used in the analysis are topographic maps from different representative periods, and the 2006 orthophotomaps. Application of GIS techniques at a detailed scale (1:5000) allows an efficient management of land use for local authorities. The results highlight the optimal management of agricultural land so as to maintain a balance between their basic function (food needs) and preservation of nature (landscape, environment).

The advantages and disadvantages of the traditional (non-morphological) filters are commonly known. Focusing only on disadvantages, it is necessary to mention that most of
these filters mainly blur noise over the image, making it less visible. That operation causes depreciation of the amount of information on the image rather than increasing its quality.
In recent years there has been a trend to develop more efficient procedures for improving image quality. Theoretically, morphological filters have potential in such usage without
possessing the drawbacks of traditional filters. The purpose of this paper is to present the results of an evaluation of the chosen
morphological filters in comparison to the non-morphological ones. To achieve this nonmorphological filtering was applied as a reference and objective indicators were used.