Classification Of Landsat Images Research Articles

Permanencia de la cobertura natural en dos propuestas de conectividad entre áreas silvestres protegidas de Costa Rica

Introducción: Las zonas ribereñas constituyen una alternativa para lograr conectividad en paisajes donde la cobertura natural ha sido fragmentada. Objetivo: Analizamos la conectividad estructural entre dos áreas silvestres protegidas de Costa Rica mediante zonas ribereñas y una ruta oficialmente establecida. Métodos: Las áreas silvestres protegidas fueron la Reserva Biológica Lomas Barbudal y la Zona Protectora Mirallaves, ubicadas hacia el noroeste del país, en la provincia Guanacaste. A partir de la clasificación supervisada de dos imágenes Landsat de los años 1997 y 2017, obtuvimos la cobertura de la tierra en las zonas ribereñas y la ruta oficial. Con base en esta información analizamos la permanencia del bosque en las dos propuestas de conectividad y calculamos varios indicadores de fragmentación. Resultados: El bosque fue más extenso y permanente en las zonas ribereñas que en la ruta oficial; no obstante, en ambas propuestas de conectividad se encontraron sectores con concentración de cobertura boscosa distantes entre sí y una interrupción de tal conectividad. En el caso de la ruta oficial, la interrupción se debió principalmente a áreas urbanas mientras que en las zonas ribereñas al desarrollo agrícola. Conclusión: A partir de los hallazgos del presente estudio, resulta razonable proponer las zonas ribereñas como rutas de conectividad estructural entre las áreas silvestres protegidas involucradas; sin embargo, tanto en estas zonas como en la ruta oficial es necesario recuperar dicha conectividad.

In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification

Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification.

ECOLOGICAL DYNAMICS OF THE GREEN DAM BY REMOTE SENSING: THE CASE OF MOUDJBARA (DJELFA, CENTRAL ALGERIA)

Ponte Academic JournalApr 2019, Volume 75, Issue 4 ECOLOGICAL DYNAMICS OF THE GREEN DAM BY REMOTE SENSING: THE CASE OF MOUDJBARA (DJELFA, CENTRAL ALGERIA)Author(s): Boumediene Khaouani ,Azziz Hirche, Mostefa SalamaniJ. Ponte - Apr 2019 - Volume 75 - Issue 4 doi: 10.21506/j.ponte.2019.4.8 Abstract:Abstract: The green dam is one of Algeria's greatest environmental achievements. However, few studies have focused on this area and particularly on its dynamics. This is what we will be examined in this work through a typical region where this reforestation was initiated, the forest of Moudjbara, located in Djelfa. The visual classification of Landsat satellite images 1972, 1987; 2003 and 2016 coupled with field surveys was selected. To limit the exogenous variations, the sun-synchronous satellite images were chosen at the same season, the autumn. Phytoecological surveys based on mixed sampling were performed. In terms of area, afforestation after planting reached about 6 500 ha in 1987 and 7985 ha in 2003 while in 2016 it reached 8500 ha, occupying nearly one third of the total area of the area study. Nevertheless, crops, especially in areas where reforestation has declined, are increasing. Phytosanitary status is a concern but natural forests seem less affected with 1.2 nests per tree in natural forest and 1.85 in reforestation. The comparison between original and reforested forest formations shows that biodiversity is greater in natural forests and the difference between reforestation and natural forest decrease with time, the similarity index between the natural forest and reforestation for the year 2002 is estimated at 63% and it reaches 76% in 2018 for perennial species. The most notable changes are in the nearby steppe area, which is undergoing advanced degradation. The crops which occupied only 1.29% of the study area currently cover 12% of the territory. These work shows that reforestation has relatively maintained their original areas, although growing crops. Contrariwise, the phytosanitary quality and the biodiversity of the stand are less satisfactory compared to natural stands. The rangelands are undergone deep changes which weaken their long-term future and question the survival of livestock farming in these zones. Download full text:Check if you have access through your login credentials or your institution Username Password

Predictions of future hydrological conditions and contribution of snow and ice melt in total discharge of Shigar River Basin in Central Karakoram, Pakistan

The high mountains of Hindu-Kush Karakoram and Himalaya (HKKH) contain a large volume of snow and ice, which are the primary sources of water for the entire mountainous population of HKKH. Thus, knowledge of these available resources is very important in relation to their sustainable use. A Modified Positive Degree Day Model was used to simulate daily discharge with the contribution of snow and ice melt from the Shigar River Basin, Central Karakoram, Pakistan. The basin covers an area of 6,921 km 2 with an elevation range of 2,204 to 8,611 m a.s.l.. Forty percent of the total area is glaciated among which 20% is covered by debris and remaining 80% by clean ice and permanent snow. To simulate daily discharge, the entire basin was divided into 26 altitude belts. Remotely sensed land cover types are derived by classifying Landsat images of 2009. Daily temperature and precipitation from Skardu meteorological station is used to calibrate the glacio-hydrological model as an input variable after correlating data with the Shigar station data ( r =0.88). Local temperature lapse rate of 0.0075 °C/m is used. 2 °C critical temperature is used to separate rain and snow from precipitation. The model is calibrated for 1988~1991 and validated for 1992~1997. The model shows a good Nash-Sutcliffe efficiency and volume difference in calibration (0.86% and 0.90%) and validation (0.78% and 6.85%). Contribution of snow and ice melt in discharge is 32.37% in calibration period and 33.01% is validation period. The model is also used to predict future hydrological regime up to 2099 by using CORDEX South Asia RCM considering RCP4.5 and RCP8.5 climate scenarios. Predicted future snow and ice melt contributions in both RCP4.5 and RCP8.5 are 36% and 37%, respectively. Temperature seems to be more sensitive as compared to other input variables, which is why the contribution of snow and ice in discharge varies significantly throughout the whole century.

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.

Urban development versus wetland loss in a coastal Latin American city: Lessons for sustainable land use planning

Urbanization is a primary cause of wetland loss in coastal metropolitan regions. Therefore, it challenges the preservation of biodiversity and the provision of key ecosystem services for urban settlements. These services include leisure and recreation, climate and water regulation, water purification, and especially alleviation of natural hazards. Tsunami flood mitigation is a particularly valuable regulating service provided by these wetlands, as recently evidenced during the 2010 tsunami that hit the central coast of Chile.The Concepción Metropolitan Area (CMA), located on the central coast of Chile, has experienced noticeable wetland loss in recent decades. Our study focused on the Rocuant-Andalién wetland, which has been particularly affected by urbanization. This wetland strongly contributes to flood control, and has provided effective protection against the CMA’s latest tsunamis (1835 and 2010). Based on Strategic Environmental Assessment (SEA), we have quantified urban growth over the wetland, both executed and projected under the Metropolitan Urban Plan of Concepción (MUPC). Recent loss in wetland area by urban growth has been quantified using land use and cover change (LUCC) maps from 2004 to 2014, obtained from the classification of Landsat images. Prospective changes (considering the complete MUPC deployment) have been inferred by combining the MUPC with the 2014 land cover map. In addition, we quantified the observed effect and planned urban growth on the wetland protected area, geoforms and potential flooding based on the area affected by the last Tsunami. Results show that urban areas have increased by 28% between 2004 and 2014, while future increase is expected to reach 238%. In contrast, wetland area has decreased by 10% from 2004 to 2014 and is expected to decrease by up to 32 %. Thus, the MUPC is not contributing to the mitigation of wetland loss nor the preservation of its biodiversity and ecosystem services. Implications for coastal planning are discussed.

KAJIAN PERUBAHAN PENGGUNAAN LAHAN DI KECAMATAN KOTO TANGAH KOTA PADANG SUMATERA BARAT

This research was aimed to collect, process, analyze the data and discuss about the land use change from agricultural to non-agricultural in Koto Tangah District Padang City. This study was a descriptive quantitative research and using GIS (Geography Information system) model.The data were primary data which was collected directly from the field and secondary data from references and written analyses by related agencies. Techniques of data collection were using landsat images containing the data of land cover change in 2008,2013and 2018 and field survey of the land use change with land unit of research location in scale 1 : 50000 which was determined by stratified random sampling. The results were (1) the classification of landsat images for 10 years (2008-2018) showed a massive degradation of agricultural area, from 6,373.65 ha in 2008 to 4,994.91 ha in 2018. The degradation was about 1,378.74 ha which was due to the conversion of agricultural land to built-up area in Koto Tangah District. (2) According to hierarchy analysis (Interpretative Structur Modelling), factors affecting the change were the unbalanced fulfilment and unsuitability population distribution. Therefore, the policy direction for the land adjustment could be done through the socialization of suistanable settlement management program and increasing people participation in managing the agricultural area. (3) The effect of land use change could be positive and negative. New built-up area means new livehood, many (non-agricultural) job opportunities, and new business opportunities. However, it also means the densitiy of population, reduced farmer’s working hour, and environmental damage.

Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district.

Impacts of landcover changes on streamflows in the Middle Zambezi Catchment within Zimbabwe

Abstract. We investigate the impacts of land cover changes on the river flows of the Middle Zambezi tributary catchments in Zimbabwe. Trend analysis on rainfall and streamflow was carried out using the Mann-Kendall test at monthly and annual time steps. Rainfall analysis indicated an increasing trend which was not statistically significant (p<0.05) for all stations. Annual streamflow time series indicated negative decreasing trends which were not statistically significant (p<0.05) except for the rainfall months of November and December. The study deduced that the changes in rainfall did not affect hydrological catchment behaviour and changes in streamflow were thus caused by anthropogenic factors such as land cover changes. Statistical tests indicated a weak but significant correlation between rainfall and streamflow which also supports the fact that changes in streamflow are mainly driven by land cover changes. Land cover change assessments were done through supervised classification of Landsat images for the years 1989, 1998, 2008 and 2014. All catchments exhibited increases in cultivation area and decreases in forest and grassland. The semi-distributed HBV-Light model was applied for change detection modelling of the gauged Musengezi catchment. We conclude that the HBV Light model can be successfully used to simulate flows for the catchment.

Estimating the impacts of land use/land cover changes on Ecosystem Service Values: The case of the Andassa watershed in the Upper Blue Nile basin of Ethiopia

Estimating the impacts of land use/land cover (LULC) changes in Ecosystem Service Values (ESV) is indispensable to provide public awareness about the status of ESV, and to help in policy-making processes. This study was intended to estimate the impacts of LULC changes on ESV in the Andassa watershed of the Upper Blue Nile basin over the last three decades (1985–2015), and to predict the ESV changes in 2045. The hybrid land use classification technique for classifying Landsat images, the Cellular-Automata Markov (CA-Markov) model for LULC prediction, and the modified ecosystem service value coefficients for estimating ESV were employed. Our findings revealed that there was a continues expansions of cultivated land and built-up area, and withdrawing of forest, shrubland and grassland during the 1985–2015 periods, which are expected to continue for the next three decades. Consequently, the total ESV of the watershed has declined from US$26.83 × 106 in 1985 to US$22.58 × 106 in 2000 and to US$21.00 × 106 in 2015 and is expected to further reduce to US$17.94 × 106 in 2030 and to US$15.25 × 106 in 2045. The impacts of LULC changes on the specific ecosystem services are also tremendous.

AUTOMATED MAPPING WITH LiDAR AND SPECTRAL CHARACTERIZATION IN MEDITERRANEAN FOREST AGROECOSYSTEMS

Mapping with LiDAR data is not a standardized practice, though LiDAR databasesare increasing in all countries in Europe. We develop and test a simple method forautomated land-cover mapping. The study area was a farm located at a natural parkof southern Spain. It comprises 502 ha covered by Mediterranean forestagroecosystems, like dehesa (a very open woodland of scattered evergreen treesused by grazing animals), woodland and scrubland, and transitions among them,composing a heterogeneous landscape. This heterogeneity is caused by variationsin holm and cork oak tree density and a sclerophyllous shrub cover, i.e., 3Dstructure of woody vegetation. Using aerial photographs digitization, Landsatimage classification, and image segmentation of tree crowns, land-cover maps weregenerated. Besides, other maps were produced from LiDAR-derived canopy coverand height of tree vegetation and shrub stratum. These 3D variables allowed to awall-to-wall characterization of woody vegetation land-cover classes in the studyarea, that was completed with a NDVI assessment. The results show that automatedmapping with LiDAR is reliable and accurate enough in comparison with othermapping techniques. It outperforms them because its higher spatial resolution, andcan be combined with other remote sensing methods to provides an improvedunderstanding of forest landscapes.

Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis

Remote sensing data and techniques are reliable tools for monitoring and studying urban land cover and land use (LCLU) change. Fine spatial resolution (FRes) commercial satellite image in conjunction with geographic object-based image change analysis (GEOBICA) methods have been used to generate detailed and accurate urban LCLU maps. The integration of a backdating approach improves LCLU change classification results for the first date of a bi-temporal image sequences. Conversely, moderate spatial resolution satellite images such as those from Landsat sensors may not allow for detailed urban land use and land cover mapping. The objective of this study is to test a new bi-temporal change identification approach that integrates image classification of fine spatial resolution satellite imagery at time-2 and moderate spatial resolution satellite imagery (Landsat) at time-1, in a backdating and GEOBICA framework for mapping urban land use change. We compare the results from this approach to those of a GEOBICA approach based on fine spatial resolution imagery in both periods. The overall accuracy of the time-1 Landsat image classification is 0.82 and that of the fine spatial resolution image is 0.87. Moreover, the overall accuracy of the areal change data estimated from the pixel-wise spatial overlay of bi-temporal FRes LCLU maps is 0.80 while that from overlaying a time-2 FRes-generated map to that from a Landsat time-1 image is 0.81. The proposed method can be used in areas that lack FRes data due to limited coverage in the early 2000s.

Broken bridges: The isolation of Kilimanjaro's ecosystem.

Biodiversity studies of global change mainly focus on direct impacts such as losses in species numbers or ecosystem functions. In this study, we focus on the long-term effects of recent land-cover conversion and subsequent ecological isolation of Kilimanjaro on biodiversity in a paleobiogeographical context, linking our findings with the long-standing question whether colonization of African mountains mainly depended on long-distance dispersal, or whether gradual migration has been possible through habitat bridges under colder climates. For this, we used Orthoptera as bioindicators, whose patterns of endemism and habitat demands we studied on about 500 vegetation plots on Kilimanjaro and Mt. Meru (Tanzania) since 1996. Land-cover changes in the same area were revealed using a supervised classification of Landsat images from 1976 to 2000. In 1976, there was a corridor of submontane forest vegetation linking Kilimanjaro with Mt. Meru, replaced by human settlements and agriculture after 2000. Until recently, this submontane forest bridge facilitated the dispersal of forest animals, illustrated by the large number of endemic submontane forest Orthoptera shared by both mountains. Furthermore, the occurrence of common montane endemics suggests the existence of a former forest corridor with montane vegetation during much earlier times under climatic conditions 2-7°C cooler and 400-1,700mm wetter than today. Based on the endemicity patterns of forest Orthoptera, negative consequences are predicted due to the effects of isolation, in particular for larger forest animals. Kilimanjaro is becoming an increasingly isolated ecosystem with far reaching consequences for diversity and endemism. Forest bridges between East African mountains acted as important migratory corridors and are not only a prehistoric phenomenon during periods with other climatic conditions but also disappeared in some places recently due to increasing and direct anthropogenic impact.

Assessment of the urban sprawl on agriculture lands of two major municipalities in Jordan using supervised classification techniques

The growing populations in urban areas are continuously changing the surrounding natural ecosystems. Jordan is a country with limited resources and witnessing rapid population growth rates and urban advancement towards their agricultural lands. The aim of this research is to investigate the spatial and temporal urban expansion on the agricultural lands of Greater Amman (GAM) and Greater Irbid (GIM) municipalities for the period 2003–2015. Geographic Information System and ENVI-supervised classification technique were used to classify Landsat images of municipalities’ lands into urban, agricultural, and undeveloped lands. In 2015, the urban area of GIM land increased by 70% of that in 2003. The newly urbanized area represented 12% (39km2) of the total GIM land. The GIM land’s conversion rate to urban area was 1% (3.24km2). In 2015, the urban area of GAM land represented 41% of the municipality’s land where 16% (128km2) is the newly urbanized land with an annual land conversion rate of 1.3% (10.7km2). This sprawl was mainly on agricultural lands where 18 and 50% of the 2003 agricultural lands of GIM and GAM, respectively, were eliminated by 2015. The existed agricultural lands in GIM by 2015 represented 38% of the total municipality lands while only 14% of the total GAM land is agricultural land. The annual agricultural land recession rates in GIM and GAM were 1 and 1.1%; therefore, their agricultural lands are expected to be eliminated by 2053 and 2028, respectively. These rates of losing agricultural lands are alarming and need to be regulated through sustainable and recent urban planning approaches.

Plantaciones forestales y su extensión hacia áreas urbanas en el área metropolitana de Valparaíso y su relación con el aumento de incendios forestales

A partir de la década de los 70 en Chile central, la expansión de las plantaciones forestales ha sido una de las principales causas de la modificación del paisaje, siendo influenciada por los cambios de cobertura de la superficie terrestre. Considerando el contexto nacional de los incendios forestales que ocurrieron durante el verano de 2017, existe una opinión generalizada que la ocurrencia de estos eventos está relacionada con la proximidad y el acercamiento de las plantaciones a las áreas urbanas, especialmente en el Área Metropolitana de Valparaíso (AMV). Desde esta perspectiva se analizó la disminución dela distancia entre la expansión de las plantaciones forestales y la expansión del suelo
 urbano en el AMV utilizando una clasificación supervisada de imágenes Landsat para los años 1989 y 2015. Adicionalmente se realiza un análisis sobre el grado de severidad de los incendios y se calculan métricas del paisaje por medio del programa FRAGSTATS. Los principales resultados muestran un aumento de superficie para las plantaciones forestales para el año 2015, duplicando su tamaño con respecto al 1989. A partir de las métricas estudiadas se observó una mayor heterogeneidad en el paisaje. Con respecto al índice de severidad se observa un mayor grado en plantaciones forestales con respecto a otras coberturas como agricultura o matorral espinoso.

Potencial de las imágenes UAV como datos de verdad terreno para la clasificación de la severidad de quema de imágenes Landsat: aproximaciones a un producto útil para la gestión post incendio

<p>Mapping fire severity is determinant to understand landscape evolution after a wildfire and provides useful information for decision making during post fire management. Quantitative fire severity mapping from relative changes in Normalized Burn Ratio index (RdNBR) is not actually being incorporated into decision making processes, being more useful the categorization in severity levels (high, moderate and low). However, the most common mapping severity methodologies based on the definition of RdNBR thresholds from field information are not always possible due to lack of field data or because the published thresholds are unsatisfactory in new regions. The boom in the use of UAVs (Unmanned Aerial Vehicle) has raised these platforms as potential tools for validation of remote sensing data. This paper presents the potential of UAVs as ground truth information in forest fires. From the photointerpretation of high resolution RGB images, the Aerial Severity Proportion Index (ASPI) has been created. Non-linear regression models between RdNBR and ASPI allows to delimitate of thresholds for the classification of Landsat images and to obtain qualitative severity maps. Validation with random points presents a kappa index of 0,5 and a relative accuracy of 70,8%. Therefore, UAV images become a very useful tool for wildfire severity mapping and for fill the gap between remote sensing information and expensive field ground campaigns</p>

Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia

Understanding the hydrological response of a watershed to land use/land cover (LULC) changes is imperative for water resources management planning. The objective of this study was to analyze the hydrological impacts of LULC changes in the Andassa watershed for a period of 1985–2015 and to predict the LULC change impact on the hydrological status in year 2045. The hybrid land use classification technique for classifying Landsat images (1985, 2000 and 2015); Cellular-Automata Markov (CA-Markov) for prediction of the 2030 and 2045 LULC states; the Soil and Water Assessment Tool (SWAT) for hydrological modeling were employed in the analyses. In order to isolate the impacts of LULC changes, the LULC maps were used independently while keeping the other SWAT inputs constant. The contribution of each of the LULC classes was examined with the Partial Least Squares Regression (PLSR) model. The results showed that there was a continuous expansion of cultivated land and built-up area, and withdrawing of forest, shrubland and grassland during the 1985–2015 periods, which are expected to continue in the 2030 and 2045 periods. The LULC changes, which had occurred during the period of 1985 to 2015, had increased the annual flow (2.2%), wet seasonal flow (4.6%), surface runoff (9.3%) and water yield (2.4%). Conversely, the observed changes had reduced dry season flow (2.8%), lateral flow (5.7%), groundwater flow (7.8%) and ET (0.3%). The 2030 and 2045 LULC states are expected to further increase the annual and wet season flow, surface runoff and water yield, and reduce dry season flow, groundwater flow, lateral flow and ET. The change in hydrological components is a direct result of the significant transition from the vegetation to non-vegetation cover in the watershed. This suggests an urgent need to regulate the LULC in order to maintain the hydrological balance.

Comparison of RS/GIS analysis with classic mapping approaches for siting low-yield boreholes for hand pumps in crystalline terrains. An application to rural communities of the Caimbambo province, Angola

In poverty-stricken regions of Sub-Saharan Africa, groundwater for supply is often obtained by means of hand pumps, which means that low-yield boreholes are acceptable. However, boreholes are often sited without sufficient hydrogeological information due to budget constraints, which leads to high failure rates. Cost-effective techniques for borehole siting need to be developed in order to maximize the success rate. In regions underlain by granite, weathered formations are usually targeted for drilling, as these are generally presented as a better cost-benefit ratio than the fractured basement. Within this context, this research focuses on a granite region of Angola. A comparison of two mapping techniques for borehole siting-groundwater prospect is presented. A classic hydrogeomorphological map was developed first based on aerial photographs, field mapping and a geophysical survey. This map represents a considerable time investment and was developed by qualified technicians. The second map (RS/GIS) is considerably simpler and more cost-effective. It was developed by the integration in a GIS platform of six maps of equal importance-slope, drainage density, vegetation vigor, presence of clay in the soil, lineaments and rock outcrops-prepared from Landsat 8 imagery and a Digital Elevation Model (DEM). Similar results were obtained in both cases. By means of a supervised classification of Landsat images, RS/GIS analysis allows for the identification of granitic outcrops, house clusters and sandy alluvial valleys. This in turn allows for the delineation of low-interest or contamination-prone areas, thus contributing additional qualitative information. The position of a well that is going to be powered by a handpump is chosen also upon social and local matters as the distance to the stakeholders, information that are not difficult to integrate in the GIS. Although the second map needs some field inputs (i.e. surveys to determine the thickness of the weathered pack), results show that RS/GIS analyses such as this one provide a valuable and cost-effective alternative for siting low-yield boreholes in remote regions.

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh.

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

Developments in Landsat Land Cover Classification Methods: A Review

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.