Post-classification Change Detection Research Articles

Urban Expansion Pattern and Land Use Dynamics in Dhaka, 1989–2014

Rapid change in land use and land cover (LULC) and unplanned urban expansion in Dhaka City, Bangladesh, receives continuous attention from local policymakers and the international community. This study employed a supervised classification procedure and postclassification change detection technique to estimate major changes between different LULC classes. The study revealed that built-up area increased significantly from 1989 to 2014. The total urban growth of 81.54 percent resulted in a substantial decrease in natural vegetation cover and agricultural land. In addition, water bodies have declined consistently over the last twenty-five years. The overall accuracy of LULC change maps produced from Landsat data ranged from 89.72 percent to 92.97 percent. The results should contribute to ongoing LULC information updates while forecasting possible future LULC change and sustainable development under greater population density.

Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accuracy higher than 0.98 resulted in change mappings with Overall Accuracy around 0.83).

Land cover change detection using GIS and remote sensing techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh

Tanguar Haor is one of the two listed Ramsar Site of this country which contains a rich amount of biodiversity as well as a highly productive ecosystem. But the valuable landscape undergoes a radical change in its form over the decades. This study uses past and recent satellite data to evaluate the typical landscape change over the decades. Both pre-classification and post classification change detection approach was used to assess the change result from 1980 to 2010. In pre-classification approach CVA, NDVI and NDWI analysis were implemented to assess the change scenario. Maximum likelihood supervised classification technique was performed to create the signature class of significant land cover category (deep water, shallow water, vegetation, and settlement). After ensuring satisfactory accuracy value for each classified image a detail post classification change detection analysis was executed. Image differencing, statistical change detection techniques (transition probability matrix), change dynamics analysis was also operated to evaluate the statistics of past change relative to present. This study illustrated that, about 40% land cover of the total study area has been converted over 30years period. Forested and high land vegetation’s are disappearing rapidly, deep water bodies consist of large lakes are becoming the rare feature of the study area. Widespread development of settlement and dominant shallow water feature are converting the natural wetland a permanent low lying agricultural land.

Satellite based seasonal land use classification and change detection analysis of landsat-8 operational land imager

This study investigated the Landsat-8 Operational Land Imager (OLI) to determine its suitability for change detection analysis and mapping applications due to its enhanced signal-to-noise ratio (SNR), spectral band configuration, technical superiority, improved system design and high radiometric resolution. Earlier Landsat series had a lower SNR, comparatively smaller radiometric resolution and limitations in spectral band configurations. Pre-classification methods e.g., image differencing; image rationing, vegetation indices and principal components used for change detection analysis do not indicate type of the change due to its limitations. Therefore, better technique of post classification change detection was used on OLI data in the study area which provided information of the type of the change i.e., from-to change detection. This paper evaluated the OLI support vector machines (SVM) classification suitability using data covering four different seasons (i.e., spring, autumn, winter, and summer) after pre-processing and atmospheric correction. After classification, the major change detection results of OLI SVM-classified data for the four seasons were compared to change detection results of six cases: (1) winter to spring; (2) winter to summer; (3) winter to autumn; (4) spring to summer; (5) spring to autumn; and (6) summer to autumn. Seasonal change in the shoreline resulted with the corresponding change in categories. The OLI data classifications were made by applying SVM classifier and due to its improved features, OLI data is found suitable for seasonal land cover classification and post classification change detection analysis.

Remote Sensing Based Analysis of the Latest Development and Structure of Abidjan District, Cote d’Ivoire

The purpose of this study is to analyze the land use/land cover dynamics change in relation to urban sprawl during 1990, 2002 and 2014 of Abidjan, the capital of Ivory Coast in West Africa and develop urban structure type (UST) classification using oriented based image analysis (OBIA) method of Abidjan, the capital of Cote d’Ivoire in West Africa. This was done by using the maximum likelihood classification algorithm and postclassification change detection procedure. The spatial-temporal land use/ land cover dynamics change in relation to urbanization sprawl was assessed based on a series of Landsat images for 1990, 2002 and 2014. Afterwards, Spot 5 image from 2013 was used for UST classification through process trees method. The results revealed urban area expansion as major land use change for the periods 1990-2014 and the overall accuracy and kappa of the classification averaged 97.5 % and 0.96 respectively for the three years. However, there was an important increase in urban area between 2002 and 2014 compared to 1990-2002. Also, The result of UST classification revealed a disproportion in all classes’ areas coverage with 2.97% of industrial area, 3.21% of public services, bare soil are of 2.03%, informal, medium and high level residences areas covered 0.28%, 7.83% and 3.2% respectively, and they were surrounded by 70.35% of vegetation area and 10.13% of water body with an overall accuracy estimated to be 62%.

Drivers and dynamics of land use land cover in Ambung VDC of Tehrathum district, Nepal

Land use and land cover change has immense impact on the global environment and ecosystem. Geospatial technologies are very important for monitoring these changes. This research aims to find out the land use land cover dynamics and drivers of Ambung VDC, Tehrathum district. The Landsat images of the year 1990 and 2013 were used for quantifying the changes. Household survey, key informant interview, focus group discussion, training samples collection and direct field observations were carried out to gather socio-economic and bio-physical data. Supervised classification was performed to prepare land cover maps. Change on land use was calculated by using post classification change detection. During 1990–2013, forest cover was found to have increased by 6.6%, agriculture decreased by 5.9% and others (barren, settlement, grass, rock and water bodies) decreased by 0.7%. The VDC was found to have severe problem of rapid drying of water resources in spite of the increase in forest cover, and so research should be carried out to find out the reason and solve the problem before it is too late.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page:90-96, 2016

Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data

Woodlands and savannahs provide essential ecosystem functions and services to communities. On the African continent, they are widely utilized and converted to subsistence and intensive agriculture or urbanized. This study investigates changes in land cover over four administrative regions of North Eastern Namibia within the Kalahari woodland savannah biome, covering a total of 107,994 km2. Land cover is mapped using multi-sensor Landsat imagery at decadal intervals from 1975 to 2014, with a post-classification change detection method. The dominant change observed was a reduction in the area of woodland savannah due to the expansion of agriculture, primarily in the form of small-scale cereal and pastoral production. More specifically, woodland savannah area decreased from 90% of the study area in 1975 to 83% in 2004, and then increased to 86% in 2014, while agricultural land increased from 6% to 12% between 1975 and 2014. We assess land cover changes in relation to towns, villages, rivers and roads and find most changes occurred in proximity to these. In addition, we find that most land cover changes occur within land designated as communally held, followed by state protected land. With widespread changes occurring across the African continent, this study provides important data for understanding drivers of change in the region and their impacts on the distribution of woodland savannahs.

Mapping deforestation and urban expansion in Freetown, Sierra Leone, from pre- to post-war economic recovery.

Freetown, the capital of Sierra Leone has experienced vast land-cover changes over the past three decades. In Sierra Leone, however, availability of updated land-cover data is still a problem even for environmental managers. This study was therefore, conducted to provide up-to-date land-cover data for Freetown. Multi-temporal Landsat data at 1986, 2001, and 2015 were obtained, and a maximum likelihood supervised classification was employed. Eight land-cover classes or categories were recognized as follows: water, wetland, built-up, dense forest, sparse forest, grassland, barren, and mangrove. Land-cover changes were mapped via post-classification change detection. The persistence, gain, and loss of each land-cover class, and selected land conversions were also quantified. An overall classification accuracy of 87.3% and a Kappa statistic of 0.85 were obtained for the 2015 map. From 1986 to 2015, water, built-up, grassland, and barren had net gains, whereas forests, wetlands, and mangrove had net loses. Conversion analyses among forests, grassland, and built-up show that built-up had targeted grassland and avoided forests. This study also revealed that, the overall land-cover change at 2001-2015 was higher (28.5%) than that recorded at 1986-2001 (20.9%). This is attributable to the population increase in Freetown and the high economic growth and infrastructural development recorded countrywide after the civil war. In view of the rapid land-cover change and its associated environmental impacts, this study recommends the enactment of policies that would strike a balance between urbanization and environmental sustainability in Freetown.

An Operational Framework for Land Cover Classification in the Context of REDD+ Mechanisms. A Case Study from Costa Rica

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

EXAMINING URBAN EXPANSION USING MULTI-TEMPORAL LANDSAT IMAGERY: A CASE STUDY OF THE MONTREAL CENSUS METROPOLITAN AREA FROM 1975 TO 2015, CANADA

Abstract. Urban expansion, particularly the movement of residential and commercial land use to sub-urban areas in metropolitan areas, has been considered as a significant signal of regional economic development. In 1970s, the economic centre of Canada moved from Montreal to Toronto. Since some previous research have been focused on the urbanization process in Greater Toronto Area (GTA), it is significant to conduct research in its counterpart. This study evaluates urban expansion process in Montréal census metropolitan area (CMA), Canada, between 1975 and 2015 using satellite images and socio-economic data. Spatial and temporal dynamic information of urbanization process was quantified using Landsat imagery, supervised classification algorithms and the post-classification change detection technique. Accuracy of the Landsat-derived land use classification map ranged from 80% to 97%. The results indicated that continuous growth of built-up areas in the CMA over the study period resulted in a decrease in the area of cultivated land and vegetation. The results showed that urban areas expanded 442 km2 both along major river systems and lakeshores, as well as expanded from urban centres to surrounded areas. The analysis revealed that urban expansion has been largely driven by population growth and economic development. Consequently, the urban expansion maps produced in this research can assist decision-makers to promote sustainable urban development, and forecast potential changes in urbanization growth patterns.

EXAMINING URBAN EXPANSION USING MULTI-TEMPORAL LANDSAT IMAGERY: A CASE STUDY OF THE MONTREAL CENSUS METROPOLITAN AREA FROM 1975 TO 2015, CANADA

Urban expansion, particularly the movement of residential and commercial land use to sub-urban areas in metropolitan areas, has been considered as a significant signal of regional economic development. In 1970s, the economic centre of Canada moved from Montreal to Toronto. Since some previous research have been focused on the urbanization process in Greater Toronto Area (GTA), it is significant to conduct research in its counterpart. This study evaluates urban expansion process in Montréal census metropolitan area (CMA), Canada, between 1975 and 2015 using satellite images and socio-economic data. Spatial and temporal dynamic information of urbanization process was quantified using Landsat imagery, supervised classification algorithms and the post-classification change detection technique. Accuracy of the Landsat-derived land use classification map ranged from 80% to 97%. The results indicated that continuous growth of built-up areas in the CMA over the study period resulted in a decrease in the area of cultivated land and vegetation. The results showed that urban areas expanded 442 km<sup>2</sup> both along major river systems and lakeshores, as well as expanded from urban centres to surrounded areas. The analysis revealed that urban expansion has been largely driven by population growth and economic development. Consequently, the urban expansion maps produced in this research can assist decision-makers to promote sustainable urban development, and forecast potential changes in urbanization growth patterns.

Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan.

One of the detailed and useful ways to develop land use classification maps is use of geospatial techniques such as remote sensing and Geographic Information System (GIS). It vastly improves the selection of areas designated as agricultural, industrial and/or urban sector of a region. In Islamabad city and its surroundings, change in land use has been observed and new developments (agriculture, commercial, industrial and urban) are emerging every day. Thus, the rationale of this study was to evaluate land use/cover changes in Islamabad from 1992 to 2012. Quantification of spatial and temporal dynamics of land use/cover changes was accomplished by using two satellite images, and classifying them via supervised classification algorithm and finally applying post-classification change detection technique in GIS. The increase was observed in agricultural area, built-up area and water body from 1992 to 2012. On the other hand forest and barren area followed a declining trend. The driving force behind this change was economic development, climate change and population growth. Rapid urbanization and deforestation resulted in a wide range of environmental impacts, including degraded habitat quality.

Monitoring deforestation in Malaysia between 1985 and 2013: Insight from South-Western Sabah and its protected peat swamp area

Monitoring land cover changes provides an effective and accurate evaluation of deforestation rates that shed light on reducing emissions from deforestation and forest degradation (REDD) implementation. Located in Klias Peninsula, southwestern Sabah, Malaysia there lies a pristine peat swamp forest area. This type of ecosystem plays a significant role in global climate regulation. Despite its importance, the peat swamp forest is threatened and highly degraded, due to the increasing demand for agricultural expansion. This is where we monitored deforestation and land cover change between 1985 and 2013. Temporal changes were determined by means of supervised classification, using the maximum likelihood classification rule to observe changes in the area. Post-classification change detection techniques were applied in order to understand the change in forest coverage in the Klias Peninsula and inside the protected area boundaries. The overall accuracy for the Klias Peninsula and the protected area were more than 88% (±4% margin of error) and 95% (±2% margin of error), respectively. Based on these findings, it appears that more than half of the forest area in Klias Peninsula disappeared from 142,713ha (±6818 margin of error) to 73,403ha (±6796 margin of error) between 1985 and 2013. The annual rate of change in the protected area was 10.94% (±0.85% margin of error) per year for deforestation and 0.86% (±5.19% margin of error) per year for forest area. The result revealed that most of the peat swamp forest was converted to other stable non-forest areas, including agriculture which can be a threat to the already disturbed protected area. Therefore, we also conducted an accurate monitoring of the forest cover change and deforestation data in the protected area and its surrounding environs in order to promote sound political decision-making regarding the future protection and sustainability of the remaining peat swamp area.

서해연안 토지이용 및 토지피복 변화탐지를 위한 KOMPSAT-2 영상의 활용

토지이용 및 토지피복변화에 대한 신뢰성 높은 평가는 수로학 및 지리학적 연구에서 침식 및 퇴적, 해안 모니터링, 생태영향평가와 같은 다양한 실질적인 사안들을 발전시켰다. 원격탐사 이미지는 시간 변화에 따른 자연 및 토지변화를 살펴보는데 있어 뛰어난 잠재력을 지니고 있다. 따라서 최근에서는 환경 모니터링을 위해 고해상도의 원격탐사 영상 이미지를 활용한 보다 정확한 연구가 요구되고 있다. 본 논문에서는 갯벌보호지역이 위치한 한반도의 전라남도, 전라북도 일부지역의 토지이용 및 토지피복 변화에 대한 맵핑 및 변화탐지 방법을 실시하였다. 이를 위하여 2008년부터 2015년에 촬영된 KOMPSAT-2 위성의 다중분광 이미지를 사용하였다. 토지이용 및 토지피복변화 맵핑은 무감독 토지분류방법으로 분석하였으며, postclassification 변화탐지 방법으로 평가하였다. 전라북도와 전라남도의 연안지역에 대한 토지이이용 및 토지 피복변화에 대한 평가결과는 시간변화에 따라 큰 차이가 나타나지는 않았으나 각각 약 1.97%, 4.34% 정도의 변화를 보였다. 본 연구결과는 연구지역의 토지피복 변화 양상을 정량화 하였으며, 특히, 화소기반 분석을 통해 연안지역에 대한 KOMPSAT-2 다중분광 이미지의 효율적이고 경제적인 활용 가능성을 확인하였다. 이러한 토지이용 및 토지피복변화 정보는 연안환경 관리 및 정책결정을 위해서 환경 및 정책관리자들에게 유용할 것으로 기대된다. Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.

Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay)

Land cover changes considered as one of the important global phenomena exerting perhaps one of the most significant effects on the environment than any other factor. It is, therefore, vital that accurate data on land cover changes are made available to facilitate the understanding of the link between land cover changes and environmental changes to allow planners to make effective decisions. In this paper, the post classification approach was used to detect and assess land cover changes of one of the important coastal zones in Egypt, Abu Qir Bay zone, based on the comparative analysis of independently produced classification images of the same area at different dates. In addition to satellite images, socioeconomic data were used with the aid of land use model EGSLR to indicate relation between land cover and land use changes. Results indicated that changes in different land covers reflected the changes in occupation status in specific zones. For example, in the south of Idku Lake zone, it was observed that the occupation of settlers changed from being unskilled workers to fishermen based on the expansion of the area of fish farms. Change rates increased dramatically in the period from 2004 to 2013 as remarkable negative changes were found especially in fruits and palm trees (i.e. loss of about 66km2 of land having fruits and palm trees) due to industrialization in the coastal area. Also, a rapid urbanization was monitored along the coastline of Abu Qir Bay zone due to the political conditions in Egypt (25th of January Revolution) within this period and which resulted to the temporary absence of monitoring systems to regulate urbanization.

A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

Monitoring Land Use/Land Cover Changes Around Damietta Promontory, Egypt, Using RS/GIS

Land use/land cover (LULC) change is considered one of the most important signals of regional environmental changes. In this study, supervised classification and post-classification change detection techniques were applied to Landsat images acquired in 1987 and 2015 to map LULC changes along the north part of the Nile delta coastal zone specifically, at Damietta promontory. Landsat images were radio-metrically and geometrically corrected, and then, multi-temporal post-classification analysis was performed to detect LULC changes rates of the Nile delta coast around Damietta promontory where the Damietta branch (of the Nile River) meets the Mediterranean Sea.Four categories including seawater, developed (agriculture and urban), and undeveloped areas were selected to evaluate their temporal changes by comparing the processed images. The objective of this study is to map and assess the rate of changes LULC changes at the study area which can help the decision makers to replan the use of natural resources efficiently.By applying remote sensing/GIS technique, areas of rapid change are identified and targeted for more detailed monitoring in the field. Changes among different LULC classes were assessed. The results show that the sew water and urban area were increased.

Urban land use/land cover changes in the Tema metropolitan area (1990–2010)

Land use and land cover changes are local and place specific, occurring incrementally in ways that often escape our attention. This study sought to detect changes in land cover in the Tema Metropolis of Ghana from 1990 to 2010. Multispectral Landsat Thematic Mapper data sets of 1990, 2000 and 2007 were acquired, pre-processed and enhanced. Unsupervised classification of the images was performed and six land cover classes (water, wetlands, closed vegetation, open vegetation, cropped lands, and built-up) were derived. The post-classification change detection technique was performed to derive the changes in land cover and their corresponding change matrices. Between 1990 and 2010, built-up areas expanded steadily to become the most prevalent land cover type in the metropolis, reducing vegetation cover dramatically. High population growth with its attendant rise in the demand for housing, and increasing commercial activities, were found to have influenced land cover changes over the period.

Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data.

Changes in land cover and land use reveal the effects of natural and human processes on the Earth's surface. These changes are predicted to exert the greatest environmental impacts in the upcoming decades. The purpose of the present study was to monitor land cover changes using Multispectral Scanner Sensor (MSS) and multitemporal Landsat Thematic Mapper (TM) data from the counties of Isfahan Province, Iran, during 1975, 1990, and 2010. The maximum likelihood supervised classification method was applied to map land cover. Postclassification change detection technique was also used to produce change images through cross-tabulation. Classification results were improved using ancillary data, visual interpretation, and local knowledge about the area. The overall accuracy of land cover change maps ranged from 88 to 90.6%. Kappa coefficients associated with the classification were 0.81 for 1975, 0.84 for 1990, and 0.85 for 2010 images. This study monitored changes related to conversion of agricultural land to impervious surfaces, undeveloped land to agricultural land, agricultural land to impervious surfaces, and undeveloped land to impervious surfaces. The analyses of land cover changes during the study period revealed the significant development of impervious surfaces in counties of Isfahan Province as a result of population growth, traffic conditions, and industrialization. The image classification indicated that agricultural lands increased from 2520.96 km(2) in 1975 to 4103.85 km(2) in 2010. These land cover changes were evaluated in different counties of Isfahan Province.

The Use of Landsat and Aerial Photography for the Assessment of Coastal Erosion and Erosion Susceptibility in False Bay, South Africa

Coastal erosion is a worldwide hazard, the consequences of which can only be mitigated via thorough and efficient monitoring of erosion. This study aimed to employ remote sensing techniques on aerial photographs and Landsat TM/ETM+ imagery for the detection and monitoring of coastal erosion in False Bay, South Africa. Vegetation change detection as well as post-classification change detection were performed on the Landsat imagery. Furthermore, aerial photographs were analysed using the Digital Shoreline Analysis System (DSAS), which determines statistical differences in shoreline position over time. The results showed that while the resolution of the Landsat imagery was not sufficient to quantify and analyse erosion along the beach itself, the larger area covered by the satellite images enabled the identification of changes in landcover conditions leading to an increased susceptibility to erosion. Notably, the post-classification change detection indicated consistent increases in built-up areas, while sand dune, beach, and sand (not beach) decreased. NDVI differencing led to the conclusion that vegetation health was decreasing while reflective surfaces such as bare sand and roads were increasing. Both of these are indicative of an increased susceptibility to coastal erosion. Aerial photographs were used for detailed analysis of four focus areas and results indicated that coastal erosion was taking place at all four areas. The higher resolution available on the aerial photographs was vital for the quantification of erosion and sedimentation rates.