A Review Paper on Monitoring Environmental Consequences of Land Cover Dynamics with The Help of Geo-informatics Technologies

Land cover dynamics is a challenging and vigorous process that associates natural and human systems that have undeviating effects on atmosphere, water and soil which lead to many environmental problems worldwide. Urbanization is one of a major land cover change that is highly correlated with many environmental problems that need emphasis. This paper aimed to review and present level and effect of land use land cover changes, urbanization, factors affecting land cover change and application of geographic information system & remote sensing in monitoring land cover changes. Over the past 300 years, about 1.2 million kilometer square of forests and 5.6 million kilometer square of pasture and rangeland were replaced by other uses worldwide, while cultivated land increased by 12 million km. Inm1950,monlym30 percent of themworldmpopulationmlivedminmurban settings, themfraction raised tom55%mbym2018. This led to about roughly 60% of the ecosystem services are being destroyed or used in unsustainable ways worldwide. Population expansion, change of technology, high land value, corruption, lack of awareness, migration of people and political pressure are among major driving force of land cover changes. Geo-informatics technology specially GIS and Remote Sensing is found to be an excellent tool for study of land cover change that enables observation across large area of earth’s surface with low cost, better efficient and high accuracy. Therefore monitoring, analyzing and evaluation of land cover dynamics with the help of geo-informatics is decisive for improved management & characterizing land cover alteration processes, and determining its environmental consequences.


Introduction
Land cover shows the physical coverage of surface of land by water, vegetation, bare soil or non-natural structures (Verburg et al., 2009;Wossenu, 2016). Natural science experts explain land use based on forms of human activities such as planting, agriculture and buildings that modify surface processes, including geological activities, biodiversity and hydrology (Popović, 2017). Land administrators describe land use more widely to comprise social and economic activities and system in which land is managed, for example the existence of marketable agriculture, rented houses, and private land (Erle, 2007).
Land cover dynamics are the changes in physical and biological features of land that can be attributed to management, including the alteration of pastures and forest area into agricultural lands, urban settlements, contamination and degradation of the soil, vegetation removal and transition to non-agricultural uses (Jokar, 2011;Tesfa et al., 2016). Different experts explain land use land cover change (LULCC) as the modification of surface of the earth by human beings. These changes assimilate the extreme environmental suffering of today's human populations that include climate change, the loss of biodiversity and contamination of water, soil and atmosphere (Erle, 2007).
Changes in land cover can vary from a change in the character of the landscape without affecting existing global classifications to the extreme case in which one type of land cover completely replaces another (Gregorio, 2002;Molders, 2012). Land cover modification refers to human activities like deforestation for agricultural and urban expansion or that naturally caused by floods, forest fires, diseases epidemics and other natural disasters (Molders, 2012;Pielke et al., 2011). The place, time and spatial measures of several Land Cover Changes (LCCs) differ from each other due to their causes (Molders, 2012).
The extent of this alteration can be slow or immediate. For example, a forest fire causes sudden change, while the accumulation of biomass or the migration of ecosystems occurs gradually (Terefe, 2017). The change of LULC is a most important problem with respect to the changing comprehensive environment (Jing, 2007) as it is never static and is constantly evolving in reply to the forceful interaction of drivers and the responses of changes in land use (Mercy & Matheaus, 2016;Terefe, 2017). Thus environmental systems are difficult to adapts as they arises from the connections among the various components of the system, which in turn influence the successive growth and its collaboration (Lepers, 2014).
The changes in land use are the global concern of the 21st century, with an intense participation for human existence and they play a vital role in the study of main ecological change (Jing, 2007). Changes in LULC caused by both human and/or anthropogenic activities have fundamentally led to several environmental complications such as deforestation, biodiversity damage, increase in global temperature, increased flooding, etc. These ecological problems are often correlated with immense urbanization, agricultural and demographic pressure (Stéphenne & Lambin, 2001) leading to a reduction of ecosystem services.
Ecosystems provide many services to the living being (Grafius et al., 2016;Karen et al., 2017;Terefe, 2017). Some of these services include providing basic needs like food, water, fuel, and fibers. These ecological services support the nutrient cycle, soil formation, microclimate and macroclimate regulation, flood regulation, disease prevention, water purification, and other services supporting living things (Walter et al., 2005). Negative consequences of LULCC can result in a huge ecosystem service burden.
Analyzing LULCC detection is very important to understand global environmental transformation processes (Prashant & Dawei 2012). Hence, consideration of the impacts of LULCC is becoming progressively more important. The need to acclimatize to climate change and continuous ecological inconsistency become important issues internationally, in addition to raising consciousness of the significance of environmental change regulation (Pielke et al., 2011). Therefore, analyzing, monitoring and evaluation of LULC, urbanization and their environmental consequence are very crucial for the land planner, manager, governmental and other NGO. Besides scientific community, to realize policies and strategies, improves the use of natural resources and minimizes the impact on the environment. Taking the above truth into consideration, it is critical to review and provide additional information to those in need. Therefore, in this paper, the authors have reviewed LULCC and its environmental impact, and the application of Geo-informatics technologies such as, GIS and remote sensing for LULCC studies.

Land Use Land Cover Change (LULCC)
The LULCC is an endless process that has taken place on the surface of the earth since ancient time (Gebrekidan, 2014;Shiferaw, 2011). For many years, human societies have been living together modifying and changing the natural environment (Andreas et al., 2013).Over the past 300 years, about 1.2 million kilometer square of forests and 5.6 million kilometer square of pasture and rangeland were replaced by other uses worldwide, while cultivated land increased by 12 million km 2 (Chetan et al., 2002). After 1990, humans converted the environment faster and more widely than throughout any equivalent time period in human history, principally to fulfill the increasing demand for food stuff, water, wood, fibers and fuels. This has caused in a considerable and mostly irreparable damage of the variety of life on Earth (Pielke et al., 2011;Walter et al., 2005). For example, since 1950 about 30% of forest area changed to crop land. Roughly 60% of the ecosystem services are being destroyed or used in un sustainable ways worldwide (Walter et al., 2005).
A study (Pielke et al., 2011) shows that, globally total cultivated area has increased considerably, from 12 million hectares in 1871 to almost 70 million hectares at the end of the twentieth century. Most of studies reviewed in this paper and related with LULCC show that, evergreen forests, grasslands and bare lands decreased extensively, while agricultural land and built-up has increased (Abebe & Megento, 2016;Halefom & Teshome, 2018;Mercy &, Matheaus, 2016). For instance, it indicates that evergreen forests decline from 39% in 2000 to 17% in 2016 (Mercy & Matheaus, 2016). Similarly, another study demonstrated that the reduction of green spaces plantations, forest, grass and cultivated land at yearly rates of 5.9%, 3.3%, 5.4% and 3.7 % respectively during 1986 to 2015, while built-uncover increased at annual rate of 5.7% (Abebe & Megento, 2016).
LULCC has impact on water basins. Hydrological practices of watersheds are affected by different causes such as, LULCC, climate, earth physicochemical properties, geology of the place, landscape, and spatial & temporal connections between these factors (Charles, 2016;Roland, 2000). Soil degradation, loss of biodiversity, and environmental damage (Mikias, 2015;Tadesse, 2014;Tesfa et al., 2016) are some of the major consequences of LULCC worldwide.
On the positive side, some changes in LULC have paid a substantial profit in terms of human welfare and economic growth. However, these advances were experienced at a high loss in the form of distraction of uncountable ecosystem services, amplified jeopardies of nonlinear changes and the escalation of poverty for some groups of people (Summers & Linthurst, 2012).To determine the values of ecosystem services analyzing the LULC and a global database is important. For example, a study by Terefe (2017) showed that, due to forest cover reduction by 54.2% in four decades led to nearly US $ 3.69 million intimated ecosystem services were lost.
LULCC is intricate and interconnected such that the enlargement of one kind take place at the expense of other classes (Molders, 2012;Pielke et al., 2011). Detection, analysis and modeling of changes in LULC and driving forces are important to establish the cause and effects detected in the past, as well as to predict future changes in LULC (Hassan et al., 2016;Tesfa et al., 2016), are also tremendously important systems for land managers and decision makers (Crews-Meyer, 2002).

Urban Land Cover Dynamics and Urbanization
Urbanization is one of the further most dominant & observable social activities on Earth (Dawson, 2009). Urban development and LCC are the two most correlated issues studied in different scientific papers. It is one of anthropogenic results that shows a global trend and reflects the growth of the world's population. Hence, the growth of population can result in expansion and modification of urban land. This led humans to undergoing a radical change in urban life (Grimm et al., 2008;Montgomery, 2008). It is obvious that cities intrude towards peripheries at the expense of farm lands, forest and other natural resources (Tawhida et al., 2015). The aesthetic benefits from open spaces, the livelihood of farming communities at the pre urban area are being increasingly changed by urban settlements (Mengistu, 2016;Yared & Heyaw, 2019). Understanding urban LCC is crucial for improved understanding of their fundamental characteristics and processes, and the impact of LCC on environment (Bagan & Yamagata, 2014).
At the beginning of 20 th century, almost merely 10% of the world's population was living in urban areas (Grimm et al., 2008). Today, the world has been urbanizing more rapidly. InM1950, onlym30 percent ofmthemworld'smpopulationmlivedminmurban setting, this number increase to 55 percent in 2018 (United Nation, 2018b). The population changes and the expansion of megacities are leading to the transformation of forests and agricultural areas into urban agglomerations (Kumar et al., 2018;Liu et al., 2014). The estimates of the global urban area varies from 1 to 3% of the land surface United Nation, 2018a). This difference of coverage is because different studies define urban land in different way (example; "cities area", "Urban administrative area", area conquered by built-up areas", "built up area", "impervious surface" etc.) . Worldwide, the number of large metropolitan is anticipated to increase from 33 in 2018 to 43 in 2030 (Nation, 2018).

Consequences of Urbanization and Other Land Cover Change
Even though the urbanization process has important implications for economic development (Chen et al., 2014;Henderson, 2003;Mcgranahan, 2013), unplanned, nonsystematic and rapid urban growth have intense impacts on several ecological constituents, particularly land, water and air (Bisrat et al., 2018;Cui & Shi, 2012;Patra et al., 2018;Seto, et al., 2012). This is especially true since many of the urban areas of the world have experienced significant LCC for many years. Hence, those urban areas use the greatest amount of world's energy (Cui & Shi, 2012;Liu, 2009) and result in severe ecological complications (Cui & Shi, 2012;Popović, 2017) like deprivation of air and water quality, and exacerbation of land degradation (Battista & de Lieto Vollaro, 2017;Patra et al., 2018;Yan, et al., 2016). Urbanization has also resulted in an enormously serious public health issues (Gebrekidan, 2014;Popović, 2017;Walter et al., 2005).
The interchange of energy, water, and exchanges between land cover and the atmosphere is highly affected by the physical features of land cover (Cui & Shi, 2012). In the growth of urban land, forests, vegetation cover, other natural and man-made plants are mostly changed to paved surfaces. This can significantly affect natural processes and ecosystem services. For example the urbanization process affects precipitation patterns, this in turn affect ground water levels (Patra et al., 2018).
Increased urban land cover has substantial effects on local environment and weather, and also interferes with many natural processes (Cui & Shi, 2012), like inconsistency of rainfall, relative humidity and surface water quality (Cui & Shi, 2012;Tolera, 2018). The urban heat island (UHI) is increased by the expansion of the urban landscape, in contrast to rural areas (Wossenu, 2016). Wind speed and direction are also affected by urban land expansion (Mohan & Kandya, 2015;Yan et al., 2016;Bisrat et al., 2018;Srivastava & Satyaprakash, 2020). This all can affect air quality and result in public health problems (Tawhida et al., 2015). For instance, studies in some portions of the northern hemisphere show that, on average urban areas get 12% less solar radiation, 14% additional precipitation, 8% more clouds, 10% more snowfall, and 15% more storms, in addition the concentration of pollutants is10 times greater and the areas are 2 o C more warmer when compared to their rural counterparts (Taha, 2008).
LULCC also takes a main role in climate change from global to local scales (Erle, 2007;Zhang et al., 2015). As land cover changes, degradation of the forest and different plants reduce the potential for carbon storage and sequestration (Reda, 2017). Dynamics of land cover change the terrestrial carbon balance (Zhang et al., 2015). In history large amount of carbon dioxide were released into the environment because of LULCC. This had extraordinary impact on local to global atmospheric amount of carbon dioxide and the earth energy balance (Pielke et al., 2011). Therefore, a detailed understanding and monitoring of the change of LULC and urban growth, and their possible consequences is necessary to manage environmental problems and facilitate sustainable development.

Factors Affecting LandIUse and LandICoverIChange
Knowledge of driversmofmLULCC is usedmto mark the beginning of the cause-effect relationship seen in the previous story. It is also very useful for land managers and policy makers to help forecast the future LULCC and its effect (Erle, 2007). While much effort has been devoted to identifies the factors, many test results show that LULCC drivers continue to change from time to time depending on specific problems. The drivers of change in the ecosystem have global, regional and local contacts (Molders, 2012;Walter et al., 2005). The instantaneous evolution and enlargement of urban centers, the pressurized population growth, immigration to cities, the shortage of land, the need for increased production, and technological changes are among the many driving forces for LULCC in today's world (Song, 2011;Cui & Shi, 2012;Kindu et. al., 2015;Mikias, 2015;Tolera, 2018). Additionally a study in Shanghai, China show that beside population growth, policy reform and economic development are among the drivers of LULCC (Cui & Shi, 2012). A study done in Khartoum, Sudan shows that natural conditions, the countries government policy, interaction among people and industrial growth were the main drivers of the LULCC experienced (Galal, 2015).  (Azeb et al., 2018;Tesfa et al., 2016;Walter et al., 2005). As migration to cities is one of the LULCC drivers it is caused by lack of employment, insecurity, reduction of agricultural productivity, lack of human life facilities, poverty, drought and natural disasters (Tawhida et al., 2015). Therefore understanding thedrivers of changes in LULC is critical for modeling future land dynamics and development of management strategies to prevent further decline of natural resources and prevention environmental problems (Song, 2011).

The Role of Remote Sensing and GIS in Land Use/Land Cover Analysis
Strong, accurate, consistent and updated information systems on LULCC and urbanization trends are needed for possibilities of choice, planning, forecasting and execution of land use arrangements to encounter the growing burdens for rudimentary human requirements, welfare and sustainable LULC development (Stéphenne & Lambin, 2001;Zhang et al., 2018).With the passage of time, different technologies gets developed making asimpler and more efficient. Geo-informatics technologies, which mainly include Satellite Remote Sensing (RS) and Geographic information system (GIS), are widely implemented for assessing and characterizing LULCC (Hassan et al., 2016).
Remote sensing has recognized to be a wonderful tool for monitoring the Earth's surface to improve perception of our environment, leading to unprecedented advancements in Similarly, GIS is used to store and handle large data sets about the images and all related information to enable analysis, interpretation and finally application according to choice of interest (Sadoun & Al Rawashdeh, 2009).
Historical qualitative and manual measuring systems are currently unable to satisfy the expanded need for scientific studies of land change management, the development of satellite RS starting from the early 1970s delivering data for the active follow up and measurable analysis of LULCC (Yue et al., 2018).
As RS has a high value for self-determining and as an objective source of information for LULCC (Ali Khawaldah, 2016;Halefom & Teshome, 2018), many researchers have shown evidence of the effectiveness of RS technology for LULC study (Awoke, 2010;Barani & Seelam, 2018). Specially integrating RS and GIS can provide a tremendous method for ideal LULC planning and management (Rozenstein & Karnieli, 2011;Sadoun & Al Rawashdeh, 2009;Weng, 2016). Most of the important processes in LULC assessment using RS and GIS may be listed as follow.

Selection of Remotely Sensed Data
RS data are a practical source of data that land use maps can be created from and managed efficiently (Rozenstein & Karnieli, 2011). The selection of suitable sensor data is the most significant step for an effective image classification. RS data vary in spatial, spectral, radiometric, and temporal resolutions (Phinn, 1998 Currently, in addition to the above platforms, LANDSAT Operational Land Imager (OLI 8) and Sentinel 2 MSI (multispectral instrument) are widely used. In general LANDSAT satellites are a main data source for global to regional LULC analysis (Prashant & Dawei, 2012), because of the easy and free availability of the data. Apart from the above, there are many other satellite platforms such as IKONOS, GeoEye, LIDAR, etc., which are also used for LULCC studies. However, before using the satellite image, prioritization of different types of sensor and other GIS data is critical for the selection of appropriate data for intended purpose (Padmanaban et al., 2017;Phinn, 1998).

Image Pre Processing
The raw satellite image may not be directly used for intended applications and needs some modifications and correction. There are some pre-processes that must be done on the raw image before it can be used for further refinement, interpretation, or analysis. Some of these processes are designed to correct errors that could lead to data being secretly collected, while others make the data more sensitive for further processing (Thomas & Ralph, 2015).
Pre-treatment activities include noise and haze removal, radiation correction, geometry correction, sub image preparation, mosaic, and stacking are among pre-processing activities (Obodai et al., 2019;Padmanaban et al., 2017;Weng & Lu, 2007). A first step in preprocessing is for individual bands of each scene need to be stacked to create a multispectral image for each scene. The second step of image preprocessing is image extraction/sub setting of an Area of Interest (AoI). Subtracting the AoI from the entire part of the image is important to reduce the bulkiness of the image file. This is not only removes unnecessary data in the image, but also it increase processing speed because of the reduced extent of data to work on (Prashant & Dawei, 2012).
Ziyad Ahmad Abdo and Satya Prakash / GEOSI Vol 5 No 3 (2020) 364-389 Subsequently geometric, radiometric corrections and image enhancement are conducted. Geometric correction involves removing orientation distortion and it also involves changing of data to ground coordinates (Herold et al., 2002;Otukei & Blaschke, 2010). The sources of these distortions may be the speed of the sensor platform, difference in altitude, earth curvature, atmospheric deflection and refraction (Thomas & Ralph, 2015). On the other hand, radiometric correction includes correction for atmospheric noise or unwanted sensor and data correction for sensor irregularities is needed (Abebe & Megento, 2016;Padmanaban et al., 2017). Image enhancement is to enhance the appearance of images to aid visual analysis, classification and interpretation of images (Abebe & Megento, 2016). However, for digital image processing of the satellite images, image enhancement might not be needed.

Classification System
Image classification involves categorizing the corrected stacked images or the individual panchromatic images into a fewer number of individual land use land cover classes or themes, based on the reflectance values (Thomas & Ralph, 2015). This can increase and simplify the applicability of RS data. There are two types of image classifiers system, which are supervised and unsupervised. It is important to know that there is no single perfect one in which image can be classified (Thomas & Ralph, 2015). The major difference among this two method of classification is, in supervised classification first training site development comes which is followed by classification stage (Rozenstein & Karnieli, 2011;Thomas & Ralph, 2015). In other way in the unsupervised system, first image is classified based on natural spectral grouping, then the image expert defines land cover identity and group them according to their similarity by comparing with ground truth data (Thomas & Ralph, 2015).
There are many classification algorithms in supervised classification. Maximum likelihood classifier, parallel epiped classifier, minimum likelihood classifier, spectral angle mapper, object-based classification, neural network machine hearing, deep learning classification and a spectral correction mapper are the major ones that are often used (Thomas & Ralph, 2015). The maximum likelihood classification method applies the theory of probability to classification activities and is generally work based on statistics (mean, variance/covariance). It is the most powerful/common classifier and mostly employed (Ayele, et al., 2018;Kebede, 2018). This method has been used by many researchers, for example many articles reviewed by the authors (Alqurashi & Kumar, 2013;Hernández-Guzmán et al., 2019) used this method. Identifying appropriate classification system and algorithm is among the main activities need for LCC change study.

Accuracy Assessment
It is necessary to validate to what degree the produced classification is similar with what exists on the ground. It is significant for accepting the obtained results and making good decisions (Gashu & Egziabher, 2018;Obodai et al., 2019). Accuracy assessment is also needed to reduce the error in RS data (Alqurashi & Kumar, 2013). Source of reference data, sampling system, and sample size must be taken into account to properly develop an error matrix for accuracy assessment (Mausel et al., 2004;Padmanaban et al., 2017).
Collection of the reference data is a key element of an accuracy assessment.
Reference data is the data that is to be compared against classified image. Different researchers use different system to collect reference data, also called ground truth. For instance, ground observation using GPS (Faye et al., 2016;Hailemariam et al., 2016;Park, 2016), aerial photography (Hailemariam et al., 2016), available thematic maps interpretation (Ghosh & Das, 2019;Sarkar, 2018) and Google earth images (Alqurashi &Kumar, 2013;Bisrat & Gizaw, 2018;Herold et al., 2002) can be used. The most common sampling system that is used for collecting reference data is simple random sampling, systematic sampling, stratified random sampling, cluster sampling, and stratified systematic (Padmanaban et al., 2017). Different researchers use different sample sizes calculation for reference data. On the other hand, classifications covering large areas or with more than 12 land cover categories require more thorough sampling, typically 75 to 100 samples per category.

Land Cover Change Detection
Detecting changes in LCC is one of the most fundamental and common uses of remote sensing image analysis. It is the system of recognizing variation in the status of things or event by seeing it at different period of time (Mausel et al, 2004;Alqurashi & Kumar, 2013). There are many LCC detection systems. Post-classification comparison, Change vector analysis (CVA), Image differencing, image rationing, , chi-square, decision trees, image fusion, hybrid change detection, principal component analysis (PCA), artificial neural networks (ANN) are among the major methods (Alqurashi & Kumar, 2013;Mausel et al., 2004;Thomas & Ralph, 2015).
All techniques have their own pros and cons. For example, post-classification comparison gives more detail about the image, object that are selected and change detected.
On other hand, image differencing, image rationing, and principal components analysis, do not provide sufficient change trend information (Alqurashi & Kumar, 2013), therefore it is A comparison of many of the LULCC detection techniques have been compiled in the

Conclusion
Land cover change is intricate and a continuing process, and is also interconnected such that the extension of one type occurs at the cost of other classes. A millions hectares of land cover changes occur from one type to another within a short period of time worldwide.
The expansion of urban settlement at the expense of forest and agricultural land is one of the major land cover change registered. The population changes and the expansion of megacities are leading to the transformation of forests and agricultural areas into urban agglomerations.
These LCC have both positive and negative consequences. On the positive side, some changes in LULC have paid a substantial profit in terms of human welfare and economic growth. However, these advances were experienced at ahigh loss in the form of distraction of uncountable ecosystem services, amplified jeopardies of nonlinear changes and the escalation of poverty for some groups of peoples. Rapid growth and enlargement of urban centers, rapid population growth, migration to cities, the shortage of land, the need for more production, policy reforms, and high land value, lack of coordination between institutions, corruption, unplanned resettlement programs and change of technologies are among the many drivers of LULCC in today's world. Spatially integrating RS and GIS can produce powerful technique for optimal LULC planning and monitoring. Therefore understanding, analyzing, monitoring and evaluation of dynamics of LCC by using Geo-informatics technology like GIS and remote sensing is crucial for better management and knowledge of their essential features, processes, and its environmental consequences to support sustainable development.