Academic literature on the topic 'Land use mapping – Benue State – Remote sensing'

Increased population, unhealthy agricultural practices, indiscriminate land clearing and illegal structures have led to an increase of erosion in Nigeria and Lagos State in particular. This research focused on identifying land use/land cover changes in Eti-Osa LGA of Lagos State and estimating the actual erosion risk using Remote Sensing and Geography Information System. In addition, this research evaluated the perception of communities within the study area with the view to understanding the risk involved in erosion. Maximum Likelihood Algorithm was the classification method applied on the Landsat imageries (1986-2016) to identify the changes on the land use/land cover types. Analysis of Variance (ANOVA) was used to evaluate the perception of communities within the study area and Revised Universal Soil Loss equation (RUSLE) model was used to estimate the actual erosion risk. The result showed that the sediment yield of the study area was estimated to be between 0 to 48ton/ha/yr. The estimated soil losses were higher in Eti-Osa West compared to other parts of Iru/Victoria Island, and Ikoyi/Obalende areas which recorded low losses. Land uses mostly affected by very high and severe erosion are the bare soils and the crop lands having about 3% to 4% respectively. It can be concluded that rainfall, lack of cover for the surface soil were the major causes of soil loss in the study area.

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

Erosion is one of the major problems in Nigeria and Lagos State in particular. The objectives of this research are to identify land use/land cover changes in Eti-Osa LGA and estimate actual erosion risk using Revised Universal Soil Loss Equation (RUSLE) model. In addition, this research evaluates the perception of communities within the study area with the view of understanding the risk involved in erosion. The result showed that the sediment yield of the study was estimated to be between 0 to 48 ton/ha/yr. The estimated soil losses were higher at Eti-Osa West, parts of Iru/Victoria Island, and Ikoyi/Obalende areas recorded low losses. Land uses mostly affected by very high and severe erosion are the bare soils and the crop lands having about 3% to 4% respectively compared to the others. It was concluded that combination of rainfall, lack of cover for the surface soil, were the major causes of soil loss in the study area.

<p>Agricultural production has contributed over time to food security and rural economic development in developing countries particularly supporting the countryside. Evidence of crop yield decline exist in the Lower River Benue Basin. This was a crop suitability mapping for rice, cassava, and yam to guide policy makers in strategic planning for sustainable agricultural development. Data was collected on various themes including climate, drainage, soil, satellite imagery, and maps. Remote Sensing was used to analyse satellite imagery to produce a digital elevation model, land use and land cover map, and normalised difference vegetation index map. GIS was used to produce thematic maps, weighted percentages of attribute data, and to produce crop suitability maps through weighted overlay. Soils in the study area require fertility enhancement with inorganic fertilisers for better crop yield. Soils in the Lower River Benue Basin are suitable for yam, cassava, and rice cultivation on maps of suitable areas. Some areas were found to be highly suitable for the cultivation of rice (34.22%), cassava (17.08%) and yam (16.08%). Some other areas were found to be moderately suitable for the cultivation of cassava (48.18%), rice (45.46%), and yam (48.85%). Areas with low suitability were 14.99% (rice), 33.68% (cassava), and 29.57% (yam). This study has demonstrated the importance of crop suitability mapping and recommends that farmers’ cooperative societies and policy makers utilise the information presented to improve decision making methods and policies for agricultural development.</p>

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). 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The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1) to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2) to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3) to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

Resource managers of private and public forests are often faced with a host of questions on forest extent, condition, and change in the course of land management. With more than 700 million acres of land covered by forest in the United States, the task of mapping and inventorying forested lands is a challenging one. Detailed and accurate maps of forest condition and structure are a necessity for rigorous ecosystem management. Forest maps are a fundamental information source for fire behavior modeling, animal habitat management, prediction and mapping of forest insect infestations, and plant and animal biodiversity assessment. Digital images acquired by earth imaging satellites are being used to help forest managers provide this information. Satellite images, when analyzed using advanced geostatistical techniques, can produce information on forest condition and structure, information that can be used to help answer questions such as those posed above. Satellite imagery has been used for many years to map land cover in forested regions, but natural resource managers are also starting to use remotely sensed satellite imagery to calculate the age, density, species, and successional state of forests under their care. In May 1999, the Kansas Applied Remote Sensing (KARS) Program at the University of Kansas was selected by NASA Earth Science Enterprise Applications Division to develop methods that use remote-sensing data and advanced geostatistical methods to create maps of forest age and successional state, or "cover types," and of forest biophysical factors, including density, biomass, leaf area, basal area, and height. By calibrating remotely sensed multispectral data with a small number of ground measurements, characteristics of the forest measured at sample points can be extrapolated across a large geographic region. This has significant advantages for forest management, especially when forests are in remote or inaccessible locations. The goal of this research is to develop new methods for the analysis of forest canopy structure, secondary forest regrowth, and forest fire history that take advantage of both the spectral and spatial correlation of ground phenomena and remotely sensed information.

Ground monitoring of soil massifs takes a lot of time, labor force and material resources, yet is the most accurate and detailed method. When implementing complex methods for monitoring the soil cover, inclusion of space technologies is necessary. Remote sensing data carry objective information over the large areas. The article discusses the possibility of using remote sensing data for mapping and monitoring changes in the soil cover of Northern Kazakhstan. Based on thematic processing of remote sensing data of domestic satellites, spatial analysis of the content of main nutrients was executed in the sowing layer of soils, the relationship was revealed between fertility indicators and the value of vegetation indices for testing ground on the territory of Northern Kazakhstan. Remote sensing methods which are gaining more practical application in determination of qualitative changes in the state of the Earth's surface are considered in this article. The use of remote sensing data enables developing automatic soil recognition and analysis systems for the quantitative assessment of soil variability. The use of remote sensing data of high and medium resolution, along with geoinformation technologies reveals great potential in assessing soil fertility, which contributes to the effective management of land resources, the preservation and maintenance of soil fertility.

Urban expansion along with other changes in land use and land cover is a global phenomenon and most parts of Western Nigeria have experienced tremendous changes in recent past. Osun state, located in Western Nigeria, was originally made up of mostly traditional farming communities. These communities have witnessed rapid urbanisation in the last few decades and most of the communities previously known to be farming communities have transformed to modern well-known cities. This project examines the use of Remote Sensing in mapping of Land Use Land Cover in Osun state over a period of 30 years (1986 to 2016) using Landsat (MSS, TM, and ETM+) images. The aim of this study is to produce a land use/land cover map of Osun state at three epochs in order to detect the changes that have taken place particularly in the built up and Vegetation areas. Landsat Images of Osun state in 1986, 2006 and 2016 were processed into five land use classes namely: Water body, Vegetation, Wetland, Built-up and Bare land. Total area of land use in each class were determined along with percentage change area, Land Consumption Rate and Land Absorption Coefficients. The result of the work shows that built-up area changed from 20.52% in 1986 to 30.71% in 2006 and then 34.45% in 2016. Land Consumption rate was 0.068 in 2016 which is indication of highly compacted living environment. The minimum Land Absorption Coefficient observed was 0.027 in between 2006 and 2016, which indicates that land is acquired for built-up development at very high rate. The resultant effect of these observed changes was a reduction of the vegetation class from 35.82% in 1986 to 31.14% in 2006 and then 23.83% in 2016. The results in this study may influence new land policy that will enhance sustainable use of land in Osun state.

Sudan as a Sahelian country faced numerous drought periods resulting in famine and mass immigration. Spatial data on dynamics of land use and land cover is scarce and/or almost nonexistent. The study area in the North Kordofan State is located in the centre of Sudan and falls in the Sahelian eco-climatic zone. The region generally yields reasonable harvests of rainfed crops and the grasslands supports plenty of livestock. But any attempts to develop medium- to longterm strategies of sustainable land management have been hampered by the impacts of drought and desertification over a long period of time. This study aims to determine and analyse the dynamics of change of land use/land cover classes. The study attempts also to improve classification accuracy by using different data transformation methods like PCA, TCA and CA. In addition it tries to investigate the most reliable methods of pre-classification and/or post-classification change detection. The research also attempts to assess the desertification process using vegetation cover as an indicator. Preliminary mapping of major soil types is also an objective of this study. Landsat data of MSS 187/51 acquired on 01.01.1973 and ETM+ 174/51 acquired on 16.01.2001 were used. Visual interpretation in addition to digital image processing was applied to process the imagery for determining land use/land cover classes for the recent and reference image. Pre- and post-classification change detection methods were used to detect changes in land use/land cover classes in the study area. Pre-classification methods include image differencing, PC and Change Vector Analysis. Georeferenced soil samples were analysed to measure physical and chemical parameters. The measured values of these soil properties were integrated with the results of land use/ land cover classification. The major LULC classes present in the study area are forest, farm on sand, farm on clay, fallow on sand, fallow on clay, woodyland, mixed woodland, grassland, burnt/wetland and natural water bodies. Farming on sandy and clay soils constitute the major land use in the area, while mixed woodland constitutes the major land cover. Classification accuracy is improved by adopting data transformation by PCA, TCA and CA. Pre-classification change detection methods show indistinct and sketchy patterns of change but post-classification method shows obvious and detailed results. Vegetation cover changes were illustrated by use of NDVI. In addition preliminary soil mapping by using mineral indices was done based on ETM+ imagery. Distinct patterns of clay, gardud and sand areas could be classified. Remote sensing methods used in this study prove a high potential to classify land use/land cover as well as soil classes. Moreover the remote sensing methods used confirm efficiency for detecting changes in LULC classes and vegetation cover during the addressed period.

Sudan as a Sahelian country faced numerous drought periods resulting in famine and mass immigration. Spatial data on dynamics of land use and land cover is scarce and/or almost nonexistent. The study area in the North Kordofan State is located in the centre of Sudan and falls in the Sahelian eco-climatic zone. The region generally yields reasonable harvests of rainfed crops and the grasslands supports plenty of livestock. But any attempts to develop medium- to longterm strategies of sustainable land management have been hampered by the impacts of drought and desertification over a long period of time. This study aims to determine and analyse the dynamics of change of land use/land cover classes. The study attempts also to improve classification accuracy by using different data transformation methods like PCA, TCA and CA. In addition it tries to investigate the most reliable methods of pre-classification and/or post-classification change detection. The research also attempts to assess the desertification process using vegetation cover as an indicator. Preliminary mapping of major soil types is also an objective of this study. Landsat data of MSS 187/51 acquired on 01.01.1973 and ETM+ 174/51 acquired on 16.01.2001 were used. Visual interpretation in addition to digital image processing was applied to process the imagery for determining land use/land cover classes for the recent and reference image. Pre- and post-classification change detection methods were used to detect changes in land use/land cover classes in the study area. Pre-classification methods include image differencing, PC and Change Vector Analysis. Georeferenced soil samples were analysed to measure physical and chemical parameters. The measured values of these soil properties were integrated with the results of land use/ land cover classification. The major LULC classes present in the study area are forest, farm on sand, farm on clay, fallow on sand, fallow on clay, woodyland, mixed woodland, grassland, burnt/wetland and natural water bodies. Farming on sandy and clay soils constitute the major land use in the area, while mixed woodland constitutes the major land cover. Classification accuracy is improved by adopting data transformation by PCA, TCA and CA. Pre-classification change detection methods show indistinct and sketchy patterns of change but post-classification method shows obvious and detailed results. Vegetation cover changes were illustrated by use of NDVI. In addition preliminary soil mapping by using mineral indices was done based on ETM+ imagery. Distinct patterns of clay, gardud and sand areas could be classified. Remote sensing methods used in this study prove a high potential to classify land use/land cover as well as soil classes. Moreover the remote sensing methods used confirm efficiency for detecting changes in LULC classes and vegetation cover during the addressed period.

Agricultural production has contributed over time to food security and rural economic development in developing countries particularly supporting the countryside. Evidence show that crop yields are declining in the Lower River Benue Basin of Nigeria. This study conducted a land use evaluation and suitability mapping for production of yam, cassava and also assessed the possible socioeconomic impediments that may hinder or enhance sustainable agricultural development in the Lower River Benue Basin. The study adopted physical assessments and socioeconomic approach coupled with mapping which incorporated processing of satellite imagery. Statistical methods were used to measure the status, trends, level of dispersion, and relationships between the variables of physical and socioeconomic parameters. Modelling techniques for determining potential impacts assessment, agricultural suitability index, adaptive capacity index, finally producing suitability maps. Geo-informatics processes were used to produce a digital elevation model, land use and land cover map, and normalised difference vegetation index map. The results were thematic maps, weighted percentages of attribute data, and suitability maps produced through weighted overlay. An intensive analysis of climatological data depicted a progressive intensity of rainfall, and a decreasing trend in the number of rain days; a gradual temperature rise; and high relative humidity during the planting season which is about 168 days. Laboratory analysis show that soils in the study area require fertility enhancement with inorganic fertilisers to encourage better crop yield. Results show that the Lower River Benue Basin is suitable for yam, cassava, and rice cultivation as classified on maps of suitable areas. Rice had the highest suitability percentages (38.30%). The study area was found to be moderately suitable for each of the crops examined by more than 40% for each crop. Cassava had the least suitability percentages (34.47%). Evidence suggests that agricultural development in the Lower River Benue Basin is under threat from potential impacts of climate variability and change, population growth, and infectious diseases. The agricultural suitability index of the study area regards the study area as suitable (70.5%) and the adaptive capacity index of the study area was moderate (50.83%), but it was found that serious attention need to be given to farm technology and infrastructure. Mitigation strategies and recommendations which are beneficial to the sustainable development of agriculture have been provided in line with the established characteristics of the Lower River Benue Basin.
Environmental Sciences
D. Phil. (Environmental Management)

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
The state of Khartoum being the most populated state in Sudan, faces the consequences of floods recurrence almost annually during rainy season. Policy makers and on ground NGOs need to tackle the hazard of floods in an effective and efficient manner. Recent research demonstrated the capabilities and potentials of remote sensing in flood hazard and risk mapping. This study aims to map flood hazard and assess the risk of floods in state of Khartoum, Sudan. In order to identify the flood hazard in state counties, an inundation indicator is used, namely the relative frequency of inundation (RFI). Flood events that occurred from 1988 to 2018 were mapped using Landsat satellite images, and maximum flood extent was then delineated. RFI was obtained using maximum flood extent maps and served as the flood hazard map. We developed a Land Cover Land Use (LCLU) map using Landsat 8 to identify affected urban and croplands areas in the state of Khartoum. RFI values was used along with LCLU map to assess state counties, and to assess the vulnerability of public facilities (health and educational facilities) using zonal statistics. It was demonstrated that, in terms of average RFI values for LCLU classes per county, croplands had the highest flood hazard, and Urban areas carried a relatively moderate flood hazard. The results of this study indicate that croplands on the riverbanks are the most inundated areas in the state of Khartoum, and the most urbanized counties have the highest flood hazard.

Remote sensing and thematic data were used to provide comprehensive views of surface conditions related to land degradation and desertification, considered environmental extremes in arid and semi-arid regions. The current work applies techniques, starting with simple visual analyses up to a parametric methodology, adopted from the FAO/UNEP and UNESCO provisional methodology for assessment and mapping of soil degradation. Egyptian case studies are highlighted to insinuate on studied aspects. Variable satellite imageries (MSS, TM, and ETM) and aerial photographs were utilized to provide data on soil conditions, land cover, and land use. IDRISI and ArcGIS software were used to manage thematic data, while ERDAS IMAGIN was used to process satellite data and to derive the normalized difference vegetation index (NDVI) values. A GIS model was established to modify the universal soil loss equation (USLE) calculating the present state and risk of soil degradation. The study area is found exposed to slight hazard of water erosion, however, and to high risk of wind erosion. It is also threatened by a slight to high salinization and slight to moderate physical degradation. It is recommended to use a GIS in detailed and very detailed studies for evaluating soil potentiality in agricultural expansion areas.

The upper few centimeters of the soil are extremely important because they are the interface between soil science and land-atmosphere research and are also the region of the greatest amount of organic material and biological activity (Wei, 1995). Passive microwave remote sensing can provide a measurement of the surface soil moisture for a range of cover conditions within reasonable error bounds (Jackson and Schmugge, 1989). Since spatially distributed and multitemporal observations of surface soil moisture are rare, the use of these data in hydrology and other disciplines has not been fully explored or developed. The ability to observe soil moisture frequently over large regions could significantly improve our ability to predict runoff and to partition incoming radiant energy into latent and sensible heat fluxes at a variety of scales up to those used in global circulation models. Temporal observation of surface soil moisture may also provide the information needed to determine key soil parameters, such as saturated conductivity (Ahuja et al., 1993). These sensors provide a spatially integrated measurement that may aid in understanding the upscaling of essential soil parameters from point observations. Some specific issues in soil hydrology that could be addressed with remotely sensed observations as described above include (Wei, 1995): (1) criteria for soil mapping based on spatial and temporal variance structures of state variables, (2) identifying scales of observation, (3) determining soil physical properties within profiles based on surface observations, (4) quantifying correlation lengths of soil moisture in time and space relative to precipitation and evaporation, (5) examining the covariance structure between soil water properties and those associated with water and heat fluxes at the land-atmosphere boundary at various scales, and (6) determining if vertical and horizontal fluxes of energy and matter below the surface can be ascertained from surface soil moisture distributions. In this chapter, the basis of microwave remote sensing of soil moisture will be presented along with the advantages and disadvantages of different techniques. Currently available sensor systems will be described.