Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria

Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence. Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanizatio

Оценка возможности использования данных дистанционного зондирования и цепей Маркова для прогноза развития растительного покрова

In conditions of global climate change, it is important to develop reliable models allowing to reliably predict plant development based on combination of the Earth remote sensing data and statistical modeling. Modeling by means of Markov chains is an efficient and at the same time simple way to predict random events, which include prediction of performance of phytomass of agricultural crops. The Earth remote sensing data obtained from the Sentinel-2 satellite with spatial resolution of 10 m were used to calculate the value of vegetation index NDVI and obtain different time rasters (2017-2019) with different degrees of vegetation cover development. To construct the matrix of probability of transition from one state to another for different levels of vegetation cover development, functionality of geoinformation systems (GIS) were used allowing to classify raster images, transform them into vector layers, and establish intersection areas. The probability matrix was later used to predict vegetation cover development using the Markov model as a predictor. The developed prediction model was tested for feasibility of the χ2 test. The results obtained showed that both the modeled values and the actual area of vegetation distribution with different degrees of development, determined from the available raster image of 2019, correlated well with each other. The research results can be useful both in developing forecasting methods and in directly predicting the crop yield of primarily dense-cover agricultural crops, as well as for estimating performance of pastures and creating efficient pasture rotations.В условиях глобальных климатических изменений актуальной является разработка надежных моделей, позволяющих получать достоверные прогнозы развития растений на основе комбинирования данных дистанционного зондирования Земли и статистического моделирования. Моделирование посредством цепей Маркова – эффективный и одновременно простой способ прогнозирования случайных событий, к которым относится и прогнозирование продуктивности фитомассы сельскохозяйственных культур. Данные дистанционного зондирования Земли, полученные со спутника Sentinel-2, с пространственным разрешением 10 м были использованы для вычисления величины вегетационного индекса NDVI и получения разновременных растров (2017–2019 гг.) c различной степенью развития растительного покрова. Для построения матрицы вероятности перехода из одного состояния в другое для различных уровней развития растительности использовались функциональные возможности геоинформационных систем, посредством которых выполнялась классификация растровых изображений, их преобразование в векторные слои и установление областей пересечения. Матрица вероятностей в дальнейшем использовалась для прогнозирования развития растительности с использованием в качестве предиктора марковской модели. Разработанная прогнозная модель была проверена на выполнимость теста χ2. Полученные результаты показали, что как смоделированные значения, так и фактическая площадь распределения растительности с различной степенью развития, определенная по имеющемуся растровому изображению за 2019 г., хорошо соотносятся между собой. Результаты исследования могут быть полезны при разработке методики прогнозирования и при непосредственном прогнозировании урожайности, прежде всего плотнопокровных сельскохозяйственных культур, а также для оценки продуктивности пастбищ и создания эффективных пастбищеоборотов

Land use Change Prediction using Markov Chain Compilation Model and Automated Cells (Case Study: Shirkuh)

Specifics regarding land cover and land use is an essential element of the planning process, as it can undoubtedly lead towards the debate around the present plans and patterns and the necessity to modify land use included in a regional plan. In this research, land use maps were prepared using Landsat TM (2000), (2008) and OLI (2016) satellite imaged. Land cover mapping was conducted after pre-processing and processing satellite images, creation of training samples and assessing maps accurate was done by coefficient kappa and overall accuracy. Supervised classification technique with maximum likelihood method were used to show the land use map. In this research, we use the 2000 and 2008 land cover maps to predict the 2016 land cover map and then use the 2008 and 2016 land cover maps to predict the 2024 land cover map.According to the results, with passing time the area of built-up area and mountainous increased with the passage of time while the dense poor rangeland, rich rangeland and agriculture area decreased during the period 2000-2016. The results of predicting changes in the time interval 2016-2024, showed that 55/0 of agriculture, 82% of rich rangeland, 80% of poor rangeland, 51% of built-up, and 0.97 of mountainous will remain unchange

Spatio-temporal analysis of landuse dynamics in Upper Opa Catchment, Southwest Nigeria

This study explored the use of geospatial techniques to assess land use change within upper Opa catchment area in Ile-Ife, Osun State, Nigeria for a period of 28 years between 1986 and 2014. To accomplish this, Landsat TM 1986, ETM 2002 and OLI 2014 were acquired from the USGS Earth Explorer in Global Land Cover Facility (GLCF) web site and subjected to supervised classification using the Anderson classification Scheme. Six land use/landcover classes were identified: Built-up, Bareland, Riparian, Forest Vegetation, Rock Outcrop and Water body using ENVI 5.1 Software. A change detection analysis of LULC was carried out to provide the necessary understanding of changes over the period and prediction for expected change in future was carried out. Result showed remarkable changes in all the land uses. For instance, Built-up increased from 7.01 km2 (6.4%) in 1986 to 11.92 km2 (10.8%) and 20.86km2 (18.8%) in 2002 and 2014, respectively while vegetation reduced from 61.72km2 (61.30%) in 1986 to 55.41km2 (50.2%) in 2014. The study further confirmed that if the current rate of reduction in the vegetation cover is allowed to continue unabated, there may be no vegetation again in the area in the next 30 years, thus, jeopardizing the need of the future generation and causing greater harm to the environment. In view of the above, efforts should be made to control land use activities within upper Opa catchment by enforcing the “Green Policy” of the Environment Act of the Federal Government of Nigeria which will check the indiscriminate land uses particular the encroachment of other uses into vegetation land.Keywords: Opa Catchment, LULC, Anthropogenic activities, Southwestern Nigeri

Multispectral remote sensing of wetlands in semi-arid and arid areas: A review on applications, challenges and possible future research directions

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions

Detecting coastal urbanization and land use change in Southern Turkey

One of the most important needs in contemporary landscape planning is quantitative land use/land cover (LU/LC) change information. The reason a strong emphasis is placed on landscape change information is that it serves as an ecological and geographical basis for preparing and implementing development plans in a more sustainable manner. Multi-temporal analysis of LU/LC changes on the Eastern Mediterranean coast of Turkey revealed that there is a marked preference for these areas primarily for building development. This paper demonstrates a methodology that relies on quantitative analysis techniques for assessing spatiotemporal changes in LU/LC in the case of the eastern Mediterranean coast of Turkey. In this respect, satellite image datasets (SPOT panchromatic, Landsat TM) acquired in 1989, 1995, 2001 and 2007 were enhanced. Resulting images were classified and compared to detect coastal urbanization and development trends.  Post-classification change analyses were employed to quantify land cover conversions in three periods from 1985 to 1995, from 1995 to 2001 and from 2001 to 2007. This paper demonstrated that urban, agriculture and shrublands changed rapidly in this part of the Mediterranean coast

An assessment of land cover change patterns using remote sensing : a case study of Dube and Esikhawini, KwaZulu-Natal, South Africa.

Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.During the past two centuries, land cover has been changing at an alarming rate in space and time and it is humans who have emerged as the dominant driver of change in the environment, resulting in changes of extraordinary magnitudes. Most of these changes occur due to demands placed on the land by the ever-increasing human population and their need for more land for both settlement and food production. Many researchers underscore the importance of recognizing and studying past land-use and land cover changes as the legacies of these changes continue to play a major role in ecosystem structure and function. The objectives of this study were to determine the extent of land cover changes between 1992 and 2008 in the study areas, Esikhawini and Dube located in the uMhlathuze municipality, KwaZulu-Natal, and to both predict and address the implications of the extent of future changes likely to occur in the area by 2016. Three Landsat satellite images of the study area were acquired for the years, 1992, 2000 and 2008. These images were classified into nine classes representing the dominant land covers in the area. An image differencing change detection method was used to determine the extent of the changes which took place during the specified period. Thereafter, a Markov chain model was used to determine the likely distribution of the land cover classes by 2016. The results revealed that aside from Waterbodies and Settlements, the rest of the classes exhibited a great degree of change between 1992 and 2008, having class change values greater than 50%. With regards to the predicted change in the land cover classes, the future land cover change pattern appears to be similar to that observed between 1992 and 2008. The Settlements class will most likely emerge as the dominant land cover in the study area as many of the other classes are increasingly being replaced by this particular class. The overall accuracy of the classification method employed for this study was 79.58% and the results have provided a good overview of the location and extent of land cover changes in the area. It is therefore plausible to conclude that these techniques could be used at both local and regional scales to better inform land management practices and policies

Strategic environmental assessment design for wetland assessment and conservation policy development in an urban planning context

This research advances Strategic Environmental Assessment (SEA) design and methodology for wetland assessment and policy development within an urban planning context. The thesis is a ‘manuscript-style’ and consists of three manuscripts, which collectively contribute to the overarching research purpose. The first manuscript presents and demonstrates a spatial framework for the application of SEA in the context of land use change analysis for urban wetland environment. The study aims to meet the needs for a proactive framework to assess and protect wetland areas more efficiently, and advance urban planning and development design. The proposed framework, adopting Geographic Information System and Remote Sensing approaches, presents a temporal evaluation of wetland change and sustainability assessment based on landscape indicator analysis. The results show that despite the recent extremely wet period in the Canadian prairie region, land use change contributed to increasing threats to wetland sustainability in the developing urban environment of the city of Saskatoon from 1985 to 2011. The second manuscript presents a scenario-based approach to SEA for wetland trends analysis and land use and land cover (LUC) modeling. Alternative future LUC was simulated using remote sensing data and city planning documentation using a Markov chain technique. Two alternatives were developed for LUC change and threats to urban wetland sustainability: a zero alternative that simulated trends in urban development and wetland conservation under a business as usual scenario, in the absence of prescribed planning and zoning actions; and an alternative focused on implementation of current urban development plans, which simulated future LUC to account for prescribed wetland conservation strategies. Results show no improvement in future wetland conditions under Saskatoon’s planned growth and wetland conservation scenario versus the business as usual scenario. Results also indicate that a blanket wetland conservation strategy for the city may not be sufficient to overcome the historic trend of urban wetland loss; and that spatially distributed conservation rates, based on individual wetland water catchment LUC differences, may be more effective in terms of wetland conservation. The results also demonstrate the challenges to applied SEA in a rapidly changing urban context, where data are often sparse and inconsistent across the urban region, and provides potential solutions through LUC classification and prediction tools to help overcome data limitations to support land use planning decisions for wetland conservation. The third manuscript presents an analytical approach to SEA, bridging strategic level assessment with operational planning and implementation. An expert-based strategic assessment framework was developed and applied to assess the potential implications of alternative wetland conservation policy targets on urban planning goals, and to identify a preferred conservation policy target. Site-specific algorithms, based on wetland area and wetland sustainability, were used to prioritize wetlands for conservation to meet policy targets within urban planning units. Results indicate a preferred wetland conservation policy target beyond which higher targets provided no additional benefit to urban development goals. The use of different implementation strategies, based on wetland area versus wetland sustainability, provides operational guidance and choice for planners to meet policy objectives within neighborhood planning units, but those choices have implications for local land use and wetland sustainability. Overall, the research contributes to the following aspects of SEA design and methodology: i) scoping processes to define the spatial and temporal context for SEA; ii) baseline assessment for analysis of environmental conditions and changes across space and/or over time; iii) methods to support the identification and evaluation of potential impacts of strategic alternatives; and iv) structured and systematic, quantitative assessment and decision-support tools for SEA that bridge strategic-level assessment with operational planning and implementation