Multi-temporal Landsat Satellite Images Research Articles

Impact of urban expansion on potential flooding, storage and water harvesting in the city of Sharjah, United Arab Emirates

The work presented in this article is an extension of an earlier study [1] in which runoff in the city of Sharjah, United Arab Emirates (UAE), was assessed for the period 1967–2010. In this study, the impacts of urban expansion on potential flooding, storage and water harvesting were assessed for the period 1976–2016 using rainfall data recorded during the same period. Sharjah is a generally a flat coastal city that is located in a dry hot region average rainfall of approximately 100 mm/y. Remote sensing and GIS techniques were used to assess the expansion of built areas in the city using multi-temporal Landsat satellite images. The built areas in the city increased during the study period by approximately four folds while the population increased by more than 10 folds. The study results demonstrate the historical increase in runoff potential, flooding potential and runoff coefficient and decline in water storage potential. The results are consistent with the observed incidence of significant local flooding episodes in urban areas during rainfall events. Furthermore, an assessment of potential urban water harvesting based on expansion of built areas in the city is provided and appropriate urban storm water management practices for the city are recommended.

Evaluating Land Use/Land Cover Change Dynamics in Bhimtal Lake Catchment Area, Using Remote Sensing & GIS Techniques

The present study was aimed at the assessment of forest cover and land use changes in the catchment area of Bhimtal lake situated in the Kumaun region of Uttarakhand, India using multitemporal Landsat satellite images. The Landsat satellite images of year 1996 to 2015 were acquired and subjected to supervised classification into four different classes viz. urban, agriculture, forest and water body. The classified maps were verified through random field samples and google earth imagery. The images of study area were categorized into four different classes viz., settlement area, agriculture area, forest area and water body. The results revealed that during last 20 years, the settlement area has been increased from 9.70% to 18.38%, agricultural area has increased from 44.32% to 47.63% and forest area has been decreased 43.58% to 31.47%. The findings from the study will be useful for lake and landscape planning and can serve as a potential source of information to the policy makers of Uttarakhand.

GIS based inter-class change analysis of classified multi-temporal landsat satellite images at a drought-prone semi-arid river basin in India

GIS based inter-class change analysis of classified multi-temporal landsat satellite images at a drought-prone semi-arid river basin in India

River mouth dynamics of Swarnamukhi estuary, Nellore coast, southeast coast of India

Swarnamukhi is an east flowing river having a total length of 130 km. This is an independent river which rises at an elevation of 300 m in the eastern Ghats ranges near Pakala village in Chittoor district of Andhra Pradesh, India. This study was carried out using multitemporal satellite images of IRS P6 LISS-III and Landsat 8 OLI/TIRS data from 2011 to 2015. The subsequent short term river mouth dynamics, coastal erosion and accretion rates have been calculated for the years between 2011 and 2015. Low river inflow, wind, tides, movement of the waves and littoral currents play a key role in the dynamic activities of erosion and accretion. The erosion rate from 2011 to 2015 was slightly decreased from 0.081 to 0.027 km2. The total net rate of accretion was estimated at 0.438 km2. The study shows during last five years (2011–2015) accretion is more than the erosion. High fluctuation of erosion and accretion are characteristics for the short term scale at river mouth.

The geomorphological evidences of subsidence in the Nile Delta: Analysis of high resolution topographic DEM and multi-temporal satellite images

This paper investigates the relevance of landforms to the subsidence of the Nile Delta using a high resolution topographic digital elevation model (DEM) and sets of multi-temporal Landsat satellite images. 195 topographic map sheets produced in 1946 at 1:25,000 scale were digitized, and the DEM was interpolated. The undertaken processing techniques have distinguished all the natural low-lying closed depressions from the artificial errors induced by the interpolation of the DEM. The local subsidence of these depressions from their surroundings reaches a maximum depth of 2.5 m. The regional subsidence of the Nile Delta has developed inverted topography, where the tracts occupied by the contemporary distributary channels are standing at higher elevations than the areas in between. This inversion could be related to the differences in the hydrological and sedimentological properties of underlying sediments, as the channels are underlain by water-saturated sands while the successions of clay and silt on flood plains are prone to compaction. Furthermore, the analysis of remote sensing and topographic data clearly show significant changes in the land cover and land use, particularly in the northern lagoons and adjacent sabkhas, which are dominated by numerous low subsiding depressions. The areas covered by water logging and ponds are increasing on the expense of agricultural areas, and aquaculture have been practiced instead. The precise estimation of subsidence rates and distribution should be worked out to evaluate probable changes in land cover and land use.

MONITORING THE WHEAT, CORN AND COTTON AREAS IN AN EASTERN MEDITERRANEAN AGRICULTURAL BASIN BETWEEN 2007 AND 2013

Abstract. Detecting the seasonal agricultural crop pattern accurately is a vital part of the agricultural planning. In this extent, Cukurova Region that is located in Eastern Mediterranean Region of Turkey was evaluated on agricultural landscape pattern. This region is the most productive agricultural region of Turkey also crop variability and yield are higher than many parts of the world. The main agricultural part of the area is called Lower Seyhan Plane (LSP) and it has been formed by the Seyhan, Ceyhan and Berdan rivers. The purpose of the study was to define the wheat, corn and cotton crop pattern using multi-temporal Landsat satellite images and object based classification approach for 2007 and 2013 cropping years. Three main crop’s areal difference were evaluated and changes were monitored between 2007 and 2013. The accuracy of the classifications were obtained by the spatial kappa statistics. Overall kappa accuracy was derived to be 0.9. Classification results were shown that wheat areas were decreased 35% and corn and cotton areas were increased 49% and 69% respectively. Particularly, government subventions and market demands were impacted cropping pattern in the region significantly. In addition, multi-temporal Landsat images and object based classification were a great combination to define regional agricultural crop pattern with very good accuracy (>90%).

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling.

Temporal Satellite Images in The Process of Automatic Efficient Detection of Changes of the Baltic Sea Coastal Zone

The goal of the research was to perform tests aimed at assessing possibilities of utilising multi-temporal Landsat satellite images for automatic efficient detection of changes (e.g. accumulation and erosion) of the sea coastal zone. The research database was composed of Landsat satellite images, and standardized NDVI vegetation indexes from the years 1998 and 2015, as well as multi-temporal vector maps and aerial orthophotomaps. The result map of Change detection allowed to locate areas, in which diametrical changes in land coverage took place. Satellite images reflecting the condition of the examined area for twenty years enabled outlining changes of Baltic coastal zone, and then determining the rate and extent of transformations of examined part of coast (erosion and accumulation), and also made it possible to trace the migration of dunes. The analysis showed the range of shifting dune displacement in the years 1998-2015 amounts to ca. 180 m. On the examined section of seashore (32 km), the process of erosion and accumulation was detected respectively on the length of 21 km and 10 km along the coast. The changes of accumulation and erosion of the sea coast are easily identifiable and clearly visible. Properly conducted workflow of processing the satellite images have allowed the rapid and efficient detection of changes coastline, dunes and vegetation.

Identification of land reclamation area and potential plantation area on Bagmati river-basin in the Terai region of Nepal

Utilization of land reclamation area offers the potentiality of increasing greenery as well as providing forest products. This study refers to the identification of the land reclamation areas and potential plantation areas on the Bagmati river-basin in the Terai region of Nepal, and recommends appropriate species for plantation in order to rehabilitate such areas. Multi-temporal Landsat Satellite Images (Landsat 7 and Landsat 8) were acquired for 2002 and 2014. Object-based Image Classification method was used to classify the land cover classes into four broad categories: i) Water, ii) Sand and gravel, iii) Plantation potential (open areas suitable for plantation) and iii) Others (forest, agriculture, built-up areas etc.). The Mean Normalized Difference Water Index (NDWI) values and Mean Brightness values were found to be helpful in identifying the water and sand & gravel areas from the other land cover classes. The overall classification accuracy was 0.97 with a kappa coefficient of 0.89 in the case of the 2014 Image classification. In this study, the land reclamation area referred to the areas occupied by water, sand & gravel on the river-beds that were converted into plantation potential and other classes between 2002 and 2014. Similarly, the potential plantation area referred to the summation of the area of reclaimed land, the area of ‘Others’ class converted into ‘Plantation potential’ class and the area that remained to be plantation potential on the bed of the Bagmati River and its tributaries between 2002 and 2014. Altogether, 4,819.10 ha land was reclaimed in the study area, and a total of 5,395.10 ha land was found to be potential for plantation within the study area.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page:53-59, 2016

MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

Abstract. The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.

MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.

Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Our case study is located in an area highly affected by coal mining activity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI and EVI2), chlorophyll vegetation index (CVI), and tasseled cap transformations were used as vegetation spectral indices to estimate canopy height. In turn, canopy height was used to predict a coniferous forest’s biomass using Jenkins allometric and Lambert and Ung allometric equations. The biophysical properties of 700 individual trees at eight different scan stations in the study area were obtained using high-resolution ground LiDAR. Nine models (Hi) were established to discover the best relationship between the canopy height model (CHM) from the airborne LiDAR and the vegetation spectral indices (VSIs) from Landsat images for the year 2005, and HB9 (Jenkins allometric equation) and HY9 (Lambert and Ung allometric equation) proved to be the best models (r2 = 0.78; root mean square error (RMSE) = 44 Mg/H, r2 = 0.67; RMSE = 58.01 Mg/H, respectively; p < 0.001) for estimating the canopy height and the biomass. This model accurately captured the most affected areas (deforested) and the reclaimed areas (forested) in the study area. Five years were chosen for studying the biomass change: 1988, 1990, 2001, 2005, and 2011. Additionally, four pixel-based image comparisons were analyzed (i.e., 1988–1990, 1990–2005, 2005–2009, and 2009–2011), and Mann-Kendall statistics for the subsets of years were obtained. The detected change showed that, in general, the environment in the study area was recovering and regaining its initial biomass after the dramatic decrease that occurred in 2005 as a result of intensive mining activities and disturbance.

Satellite Image Processing for Land Use and Land Cover Mapping

In this paper, urban growth of Bangalore region is analyzed and discussed by using multi-temporal and multi-spectral Landsat satellite images. Urban growth analysis helps in understanding the change detection of Bangalore region. The change detection is studied over a period of 39 years and the region of interest covers an area of 2182 km. The main cause for urban growth is the increase in population. In India, rapid urbanization is witnessed due to an increase in the population, continuous development has affected the existence of natural resources. Therefore observing and monitoring the natural resources (land use) plays an important role. To analyze changed detection, researcher’s use remote sensing data. Continuous use of remote sensing data helps researchers to analyze the change detection. The main objective of this study is to monitor land cover changes of Bangalore district which covers rural and urban regions using multi-temporal and multi-sensor Landsat multi-spectral scanner (MSS), thematic mapper (TM), Enhanced Thematic mapper plus (ETM+) MSS, TM and ETM+ images captured in the years 1973, 1992, 1999, 2002, 2005, 2008 and 2011. Temporal changes were determined by using maximum likelihood classification method. The classification results contain four land cover classes namely, built-up, vegetation, water and barren land. The results indicate that the region is densely developed which has resulted in decrease of water and vegetation regions. The continuous transformation of barren land to built-up region has affected water and vegetation regions. Generally, from 1973 to 2011 the percentage of urban region has increased from 4.6% to 25.43%, mainly due to urbanization.

Assessing shoreline response to three submerged breakwaters at Kerteh Bay, Terengganu, Malaysia using Landsat imagery

As part of a project to determine the exact structural and environmental parameters governing the mode and magnitude of salient formation behind a submerged breakwater, a remote sensing technique is being adopted to assess the extent of erosion/accretion at Kerteh Bay, T errengganu, Malaysia. Multi-temporal Landsat satellite images of coarse resolution for the years of 1994, 2000, 2006, 2009 and 2012 were acquired for this purpose. The images were subsets divided into smaller areas of interest and classified using supervised classification of support vector machine. The classified image is then vectorized to extract shoreline based on waterline in each of the subset rasters images. Tidal correction were adopted to correct the waterline/shoreline to the mean sea level (MSL) datum. Comparison of corrected shorelines was carried to obtain the extent of erosion/accretion at the Kerteh Bay, Terrenganu, Malaysia. It was observed that substantial accretion was observed between the years 1994-2006 at the upper part of the study area, the part between northern part and the southern part also experienced accretion but not as much as compared to northern part for the same year. Erosion was noted between the years 2006-2012 for all of the areas of the study area but the rate slowed down between the years 2009-2012 for all the areas. Slope estimated from the imageries were compared with in situ slope of the same area, this served as a validation for the method used.

Predicting the Trend of Land Use Changes Using Artificial Neural Network and Markov Chain Model (Case Study: Kermanshah City)

Nowadays, cities are expanding and developing with a rapid growth, so that the urban development process is currently one of the most important issues facing researchers in urban issues. In addition to the growth of the cities, how land use changes in macro level is also considered. Studying the changes and degradation of the resources in the past few years, as well as feasibility study and predicting these changes in the future years may play a significant role in planning and optimal use of resources and harnessing the non-normative changes in the future. There are diverse approaches for modeling the land use and cover changes among which may point to the Markov chain model. In this study, the changes in land use and land cover in Kermanshah City, Iran during 19 years has been studied using multi-temporal Landsat satellite images in 1987, 2000 and 2006, side information and Markov Chain Model. Results shows the decreasing trend in range land, forest, garden and green space area and in the other hand, an increased in residential land, agriculture and water suggesting the general trend of degradation in the study area through the growth in the residential land and agriculture rather than other land uses. Finally, the state of land use classes of next 19 years (2025) was anticipated using Markov Model. Results obtained from changes prediction matrix based on the maps of years 1987 and 2006 it is likely that 82% of residential land, 58.51% of agriculture, 34.47% of water, 8.94% of green space, 30.78% of gardens, 23.93% of waste land and 16.76% of range lands will remain unchanged from 2006 to 2025, among which residential lands and green space have the most and the least sustainability, respectively.

Evaluation of coastal erosion and accretion processes along the southwest coast of Kanyakumari, Tamil Nadu using geospatial techniques

The impact of coastal erosion has adversely affected the socioeconomic conditions of the coastal community worldwide. The coastal environment is experiencing a wide range of natural and anthropogenic pressure in India. This study was carried out along the southwest coast of Kanyakumari, South India using multitemporal Landsat satellite images from 1999 to 2011. The long-term coastal erosion and accretion rates have been calculated for the periods between 1999 and 2011, and the subsequent short-term changes were performed during 1999–2000, 2005–2006, and 2010–2011. Thus, the long-term coastal changes indicate that the net erosion rate is higher on the coasts of Kanyakumari, Kovalam, Manavalakurichi, Mandaikadu, and Thengapattinam; the values are 0.118, 0.105, 0.127, 0.133, and 0.017 m2, respectively. Meanwhile, the annual erosion rate of these areas is 10,000, 9,000, 11,000, and 1000 m2/year. The coastal zones Ganapathipuram and Enayamputhandurai have experienced accretion; the net accretion rate is 0.271 and 0.081 m2, respectively. Coastal beaches, beach ridges, and marine terraces are predominantly disturbed by the hydrodynamic processes including wave action, littoral current, and intervention of littoral drift by the artificial coastal structures like groins, revetments, and seawalls. Moreover, the study area has been demarcated with site-specific erosion and accretion zones based on the frequent seaward or landward coastline fluctuation through geospatial technique.

Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish, Egypt

Environment in arid conditions is dynamic and needs more investigation to understand the complexity of change. This spatiotemporal study will help to assess and monitor the land use and land cover change in the arid region of El-Arish area, where the climate and human activities are the major threats to rural development. In the past 11 years, dramatic changes of environment have been recorded in case studies. The post-classification comparison method was used to observe the changes using multi-temporal satellite images which were captured in the years 1999, 2001, 2005, and 2010. The overall accuracy of the produced thematic images was assessed regarding to the quantity and allocation disagreements. Five classes were defined in this investigation: bare soil, vegetation, urban, sand dunes, and fertile soil. From the year 1999 to 2010, fertile soil was increased by 13 %. Bare soil class occupied more than 50 % of land in the case study during for over a decade. From year 1999 to 2010, vegetation cover witnessed a dramatic increase. Soil and water management are the keys of land development and positive land use and land cover dynamics. Changing agricultural policies of using the available water resources are needed in the case study to prevent severe food shortage in the future.

Monitoring of Deforestation and Fragmentation in Sarawak, Malaysia between 1990 and 2009 Using Landsat and SPOT Images

Sarawak is the largest state in Malaysia that covers 37.5% of the total land area. Multitemporal satellite images of Landsat and SPOT were used to examine deforestation and forest fragmentation in Sarawak between 1990 and 2009. Supervised classification with maximum likelihood classifier was used to classify the land cover types in Sarawak. The overall accuracies of all classifications were more than 80%. Our results showed that forests were reduced at 0.62% annually during the two decades. The peat swamp forest suffered a tremendous loss of almost 50% between 1990 and 2009 especially at coastal divisions due to intensified oil palm plantation development. Fragmentation analysis revealed the loss of about 65% of the core area of intact forest during the change period. The core area of peat swamp forest had almost completely disappeared during the two decades.

Identificação de Cerrado Rupestre por meio de imagens multitemporais do Landsat: proposta metodológica

O Cerrado Rupestre corresponde a uma formação savânica do bioma Cerrado, ocorre em relevos acidentados e em meio a afloramentos rochosos, apresenta elevada biodiversidade e várias espécies endêmicas e funciona como barreira para a expansão agrícola. No estado de Goiás, ocorrências expressivas dessa fitofisionomia são encontradas no Parque Nacional da Chapada dos Veadeiros (PNCV). O objetivo deste estudo foi desenvolver uma nova abordagem metodológica para discriminar Cerrado Rupestre do PNCV com base em imagens multitemporais do satélite Landsat. Sete cenas do referido satélite foram convertidas para reflectância de superfície terrestre com suporte do algoritmo de correção atmosférica denominado FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes). Em seguida, os valores de reflectância de cada cena foram somados e as imagens resultantes foram processadas por meio da técnica de segmentação de imagens por crescimento de regiões. Os segmentos foram exportados para o formato shape e os polígonos correspondentes ao Cerrado Rupestre foram identificados por meio de análise visual na tela do computador da composição colorida falsa-cor das bandas 3, 4 e 5. Foram mapeados 24.451 hectares de Cerrado Rupestre, o que corresponde a 37% da área do parque. A exatidão global do mapeamento foi de 85%. Como continuação desta linha de pesquisa, recomenda-se a inclusão de um número maior de cenas, principalmente da estação chuvosa, a integração dos dados de sensoriamento remoto com modelos digitais de elevação e a análise sinergística entre os sensores ETM+ e TM do Landsat com calibração cruzada.