Nation Convention to Combat Desertification (UNCCD, 1992) defined desertification as "land degradation in arid, semi-arid and dry sub-humid area resulting from various factors, including climatic variation and human activities".
Land degradation is defined as the reduction or the loss of the biological or economical production of irrigated and non-irrigated cropland, grassland, pastures, forests and woodland in arid, semi-arid and dry sub-humid zones as result of combination of factors including climatic variation and human impacts. Dry ecosystems are very vulnerable to overexploitation, inappropriate land uses and practices. Land degradation leads to unfertile soil, unavailable water, reduction in net primary production, and change of plant cover and biodiversity. The four desertification processes having the most intensive impact on the biological productivity of land are degradation of vegetative cover, soil erosion, salinisation, waterlogging, and soil compaction (UNCCD, 1992). Desertification is caused by overgrazing, excessive woodcutting, land abuse, improper soil and water management, and land disturbance. Its effect appears as reduced productivity of land, environmental degradation, impaired health and lowered standard of living for the local people (Dregne, 1985). Combating desertification requires several activities including technological, political, and social actions such as adoption of rehabilitation programmes and sustainable management practices. Solutions are theoretically possible but lack of finance and managerial ability are still the major constrains for implementation of these solutions. Desertification is measured with reference to status, rate, extent and hazard.
THIS study focused on the assessment of and mapping of landuselandcover change the White Nile state, Sudan. Through mapping and monitoring, the changes that occurred in landuselandcover, due to drought, climate change and mismanagement. The study attempted also to update some information in the study area such vegetation cover and Vegetation density using different methods of data transformation and analysis such as statistical analysis, GIS and remotesensing techniques The Result showed that the White Nile State was rich of forest, agricultural lands and has extensive water resources of 26 million cubic meters from the White Nile water in addition to rain. The state plays a significant role in environmental, social and economic aspects of Sudan. The state has suffered from deforestation and degradation due to natural hazards and human activities. This research conducted by application of remotesensing and investigated the possibility of identification, monitoring and mapping of the landuselandcover changes and dynamics in the White Nile state during the last 30 years. The result show that landuselandcover structure in the White Nile has obvious Changes and there is strong relations between forest cover changes and land area clearance for agriculture.
The difficulties concerning landuse / landcover classification by means of remotesensing in arid and semi-arid regions are well known. Since vegetation-soil-patterns in arid and semi-arid zones are characterized by a sparse distribution of non-photo synthesising vegetation (NPV) its spectral behaviour interferes with spectral signatures of bare soil patterns (Schmidt and Karnieli, 2000, Khiry et. al., 2006). Moreover, the spatial heterogeneity at pixel level strongly affects systematic separation between dominant land uses. Therefore, many studies have recommended subpixel unmixing analysis as a suitable method to overcome such constrains (Elmore, et al., 2000), but still there are many difficulties to be overcome such as unavailability of spectral libraries for dominant plant species and soil types. Mapping of landuse/landcover classes is an important task to conserve natural resources and to recommend suitable management practices. Remotesensing techniques provide promising possibilities to map landuse/landcover classes since remotely sensed data cover a large area with periodic synoptic view. The goal of this study is to determine the major landuse/ landcover classes in this arid zone of Saudi Arabia by usingremotesensing techniques with more emphasis on the effect of sand encroachment on date palm production.
Landcover suitability images were derived to determine the transition suitability of each pixel for each landuse/cover type. The suitability criteria for vegetation, degraded vegetation, bare and degraded soil and water was based on temporal analysis of landcover trends from 1972 to 2006. A similar technique was used by Ye and Bai (2008) to derive suitability images. The state-and-transition model used in rangeland ecology was used to understand the processes underlying landcover change dynamics (Briske et al., 2005). These principles were applied to determine suitable sites for intact vegetation and degraded vegetation, bare and degraded soil because state-and-transition models accommodate greater complexity by considering vegetation dynamics in response to multiple drivers and by characterizing transitions to alternative stable states on individual ecological sites (Briske et al., 2005). Vegetation dynamics are characterized by continuous reversible and discontinuous non-reversible trends (Wu and Loucks, 1995; Watson et al., 1996; Illius and O’Connor, 1999). The occurrence of continuous and reversible vegetation dynamics is dominant in stable vegetation states. Discontinuous and non-reversible dynamics result once one stable state replaces another, when thresholds have been exceeded. Ecological thresholds are difficult to identify since ecosystem modification often imposes a series of feedback mechanisms that maintain or reinforce the altered state and limits reversal to the previous stable state (Archer et al., 2001; Scheffer et al., 2001; van de Koppel et al., 2002). It is noteworthy however, that vegetation dynamics exhibit complex trends difficult to model without simplifications. Given that predictive vegetation mapping is based on the ecological niche theory and gradient analysis. This study therefore assumes that suitable sites for vegetation are ecological niches in which vegetation established itself in the past when anthropogenic effects were minimal and climatic factors favourable. The distribution of settlements in the Keiskamma catchment is characterised by a mixture of land tenure systems that exist in the region (Ruhiiga, 2000; Bank and Minkley, 2005). Such complexities are difficult to model without simplifications.
In the present study, toposheets were demarcated for making landuse and landcover map of 1998 and then through this vector data landuse and landcover map was differentiated into raster data form in ERDAS software. The study area of supervised classification method was done from the satellite data of 2015 with the help of maximum likelihood scheme (MLC). In the study area, dissimilar landuse and landcover patterns were recognised such as urban, rural, ,agricultural, forest, water body, waste landusing the NRSA guidelines landuse and landcover classification pattern of 2002 (Table1). Thereafter, change detection mapping has been done in ERDAS imagine software to evaluate the landuse and landcover changes that might have occurred during year of 1998 to2015 (Table 2).
There are several urban applications where satellite based remotely sensed data are being applied, namely, urban sprawl/ urban growth trends, mapping and monitoring landuse/ landcover, urban change detection and updation, urban utility and infrastructure planning, urban landuse zoning, urban environment and impact assessment, urban hydrology, urban management and modeling (Raghavswamy, 1994). Remotesensing technology and geographic information system (GIS) provide efficient methods for analysis of landuse issues and tools for landuse planning and modeling. By understanding the driving forces of landuse development in the past, managing the current situation with modern GIS tools, and modeling the future, one is able to develop plans for multiple uses of natural resources and nature conservation. The change in any form of landuse is largely related either with the external forces and the pressure builtup within the system (Bisht and Kothyari, 2001; Thomas et al., 2014; Ward et al., 2014).Remotesensing techniques offer benefits in the field of landuse/ landcovermapping and their change analysis. One of the major advantages of remotesensing systems is their capability for repetitive coverage, which is necessary for change detection studies at global and regional scales. Detection of changes in the landuse/ landcover involves use of at least two period data sets (Jenson, 1986). The changes in landuse/ landcover due to natural and human activities can be observed using current and archived remotely sensed data (Luong, 1993).
To analyze and assess the changes in landcover the most dynamic parameters such as cultivated areas; seasonal water courses (Wadis), shrubs, grass areas and forest were examined. The EARDA Imagine Classifier tool 9.2 and ARCHGIS have been used in the data analysis. The standardization procedures for landcover classification created by the Food and Agricultural Organization of United Nations (FAO) and Global Landcover (GLC) which is recognized by International Organization for Standardization (ISO) as standard international approach was adopted. It considered the widely used texture, tone; color and reflectance of the landcover presented in the image. These include image interpretation and classification, field verification and finally data harmonization and finalization. High levels of overall accuracy assessment were obtained before proceedings for the analysis (98.2%, 96.3% and 89.6%) for the images of the years 1973, 1987 and 2006, respectively. The quantitative data obtained from the analysis of the maps in the form of descriptive statistics were presented informs of tables, graphs and percentages. Detecting changes over time series of landuse and landcover of Ghubaysh were exemplified in maps formats.
Land-use suitability mapping and analysis is one of the most useful applications of Geographical Information System (GIS) for spatial planning and management [9; 10]. Such analysis is a multicriteria evaluation, which aims at identifying the most appropriate spatial pattern for future land uses according to specified requirements, preferences, or predictors of some activity [11; 9]. GIS serves the multicriteria evaluation function of suitability assessment well, providing the attribute values for each location and both the arithmetic and logical operators for combining attributes . Remotesensing has long been an effective means for landcovermapping with its ability to quickly collect information on a large regional scale, and many landcover maps on global and regional scales have been produced in recent years usingremotesensing data [13; 14].
Abstract: LandUse and LandCovermapping is of great significance in scientific, scholarly research, planning and management. Regional landuse pattern reflects the character of interaction between man and environment and the influence of distance and resources based on mankind’s basic economic activities. Remotely sensed satellite images provide a synoptic overview of the whole area in a very short time span. This leads to quick and truthful representation of the real world in the best possible manner. It provides an insight to coordinate relationship among transportation, residential, industrial and recreational land uses, besides providing broad-scale inventories of natural resources and monitoring environmental issues, including land reclamation, mangrove restoration, disaster relief, water quality and planning economic development. LandUseLandCover features have been precisely captured through on-screen visual interpretation and digitally on fused very high resolution (0.60 m, Quick Bird) and medium to coarse resolution (LISS IV, LISS III) satellite imagery. Provision of such maps helps town planners in effective and best possible utilization of its resources besides providing a comprehensive view of the total area. The present study focuses on the role of remotesensing and geographic information system (GIS) in assessment of changes in landuselandcover area of Nagpur, Maharashtra, India. Extracting landuse/landcover information is an indispensable exercise for agricultural land. This information is beneficial for decision support system, planning and development in landuse. This information can be collected through visual interpretation and classification process of satellite data. The proposed system use the features of existing widely used classification system that are amenable to data derived from remotesensing sources.
average elevation of 751 metres (2463 feet). The city is situated in the north-western parts of Karnataka and lies at the border of two states, Maharashtra and Goa on the Western Ghats (50 km from the Goa state border). The study area belongs to the Northern Dry zone and Northern Transition Zone. The annual rainfall ranges from 464.5- 785.7 mm and about 52 % of the annual rainfall is received during Rabi season. The elevation is between 450-900 m. The soils are shallow to deep black clay in major areas. The important crops grown here are Rabi jowar, Maize, Bajra, Groundnut, Cotton, Wheat, Sugarcane and Tobacco.12 watersheds covering Belgaum District was taken up for the study. The study area was extracted from LISS IV images as of February 2011. Sub watersheds were extracted by overlaying watershed boundary shapefiles. The details of the watersheds are as tabulated below
Abstract - The structure, functions and dynamics of most landscapes across the world are mainly determined by their landuse and landcover. Mappinglanduse/ landcover changes at regional scale is essential for wide range of applications, including landslides, erosion, land planning, global warming etc. Rapid and extensive modifications of landuse/ landcover due to accelerated human activities have largely resulted into changes which have wider ramifications due to the sensitiveness of these fragile ecosystems of mountainous areas. The study area is environmentally fragile region facing tremendous and undue pressure on its natural resources. Watershed as a resource region clearly reveals the impact of developmental activities. The watershed is therefore an ideal spatial management unit for analyzing land dynamics as well as integrated development of natural resources potentialities of these sensitive systems. RemoteSensing satellite IRS P6 LISS III (2012) data have been used in this study along with information collected from the field for preparing the landuse / landcover classes. Suitable ground control points were selected for ground truthing and validation of data. Varied landuse/ landcover classes were identified ranging from moderate to dense forests to orchards to snow cover. An important element of this research paper has been advanced and improved data products and data collection systems. The thrust of the present research in mountainous areas revolve around better understanding of landuse/landcover analysis and suitable resource management in the Hamal Watershed of Kashmir.
Agriculture has been the most dominant force of land transformation on earth’s surface. Approximately, a third of the Earth’s land surface is currently being used for cultivating crops or grazing cattle (FAO 2004). The expansion in agricultural land area has been at the cost of natural forests, grasslands and wetlands that provide valuable habitats for species and important services to humankind (Millennium Ecosystem Assessment 2003).Extensive research on land-use changes in tropical Asia is available for the period 1880-1980 (Flint and Richards 1991). This involves an area of 8 million km 2 and 13 countries (India, Sri Lanka, Bangladesh, Myanmar, Thailand, Laos, Cambodia, Vietnam, Malaysia, Brunei, Singapore, Indonesia and the Philippines). With the recognition that landuse is an important driver of global environment change, numerous studies in the last two decades have estimated the rates of tropical deforestation and other kinds of land-cover change around the world. Remotesensing has
Based on the accuracy assessment, it can be shown that the accuracy of the three produced maps is very similar (higher than 84%). The overall accuracies and kappa indices are, respectively, 88% and 84% for 1985, whereas years 2002 and 2017 show an overall accuracy of 93% and 88.4%, while the kappa indices are around 90.6% and 85%, respectively. Table 2 shows the producer and user ac- curacy for the related years. The choice of the SVM classifier made this task eas- ier, especially for 1985 and 2002 images, where there is a lack of the data needed to establish the different samples classes ROIs (Regions of Interest). As explained above, this classification is based on the quality, not on the number of samples. Therefore, we have been able to use topographic maps, field data and data col- lected from the High Commission for Water and Forests and the Kenitra Urban Agency. This approach allows for a reliable classification and assessment of the obtained results using a limited number of quality samples. The classified images of LUCL for the years 1985 and 2002 and 2017 are presented in Figure 3.
Agriculture plays an important role in economy’s growth of countries. The food is important for everyone, due to the climates changes, water & soli pollution. The production of food is less than previous years. Farmer and an Agricultural Agency face a problem to producing food in a cost- effective manner. GIS, RemoteSensing Technique for agricultural/LC are use for decision support in a production of a food.GIS &Remotesensing image are benefit in agricultural production because It gives the accurate information of agricultural land like crop identification, crop classification, crop condition, crop & soil monitoring. This paper attempts a concise review for GIS and Remotesensing Techniques for Agricultural LandUse/ Cover.
Spatio-temporal Land-Use and Land-Cover (LULC) changes have been affecting geo- environmental and climate change globally. This study aims to analyze LULC changes in Bahir Dar city and its surrounds. Landsat 5 TM (1987), Landsat 7 ETM+ (2002) and Landsat 8 OLI (2017) and SPOT images, and aerial photographs, master plan map and Google Earth Landsat images were used to analyze changes. In Bahir Dar city and its surrounds, LULC has been changing in space and time. During 1987-2017, more than 50% of the study area was covered with cropland. Settlement areas have increased from 3.3% in 1987 to 9.13% in 2017. However, wetland vegetation, shrubland, grassland, forest, and waterbodies have degraded. These changes are mainly attributed to population growth and its effect on the environment. Land-use and land-cover is a serious problem and it causes land and environmental degradation, climate change and loss of the biological environment.
cover (LULC) changes play a major role in the study of global change. Landuse/landcover and human/natural modifications have largely resulted in deforestation, biodiversity loss, global warming and increase 2004). This environmental problems are frequently occurs related to LULC changes. So, available data is used in LULC changes. It can provide critical input to decision to make of environmental management and When growing population and ing socio economics are creates a LULC changes (Seto, 2002). The LULC alterations are generally caused by mismanagement of agricultural, urban, aquaculture pond and forest lands which lead to severe environmental problems such c. Remotesensing and Geographical Information Systems (GIS) are powerful tools to derive accurate and timely information on the spatial distribution of anges over large areas (Carlson et al., 2003) Past and present studies conducted by organizations and institutions around the world, mostly, has concentrated on the application of LULC changes. Remotesensing imagery is the most important data resources
Vegetated Land includes all vegetation cover which growth naturally or by human activates like agricultural land. The agriculture land in the study area is based mainly on grain yield production such as barley, wheat and rice. In addition comprised cropland and pasture orchards, date palms, reeds, sedges and shrubs, mostly comprised of herbaceous plant species that were mainly less than 1 m in height, which are distributed as scattered plants or small communities on the whole parts of study area .
Following 1998, agricultural activities recessed as ur- banized areas expanded. Irrigated lands, on the other hand, started to decrease as prices of water and cost of pump- ing groundwater increased. Subsequently, during 2003- 2013, the total irrigated area was 5400 ha, which repre- sented 1.8% of the study area. Generally, the trends of urbanization were consistent with the increase of popula- tion in the study area. According to the official records of the Department of the Statistics , population of the study area increased from 1.4 million in 1983 to 3.6 mil- lion in 2012. This increase resulted in increasing urban- ized areas from 6% in 1983 to 22% in 2013 (Figure 2). The increase in population and urbanized areas resulted in increasing the amounts of treated wastewater from 15 MCM in 1983 to 85 MCM in 2013. Therefore, popula- tion growth could be considered as the main driving force for landuse/cover change in the study area and would call for future plans to cope with its adverse impacts on land resources.
Application of RemoteSensing and GIS technique in morphometric and landuse/landcover analysis have paved way for efficient planning and management of sub-watersheds considering their priority for natural resources Viz., soil and water resources. The present study reveals that sub-watersheds SW2 and SW14 receives a very high priority based on morphometric analysis and sub-watershed SW8 ranked under very high category based on landuse/landcover analysis. It is observed that upon integrating both the morphometric and landuse/ landcover thematic layers three sub-watersheds SW6, SW9 and SW11 are found to receive common priority by both the approaches and remaining sub-watersheds show little correlation difference. Thus integrated approach of morphometric and landuse/landcover based analysis with the application of remotesensing and GIS helped not only in prioritizing the sub-watersheds but also for planning and decision making for sustainable sub-watershed development and management.