Landsat-7 ETM + Research Papers (original) (raw)

Motivated by the need to generate a pan-European coastline database from Landsat 7 ETM+ images, we present a new methodology for extracting automatically the coastline and its application to the entire European continent. Our approach consists of the combination of spectral and spatial information for the images using morphological image segmentation techniques. For these purposes several morphological segmentation algorithms were implemeted inside a GIS platform to evaluate their performance in coastline extraction. The results demonstrate the accuracy of the developed methodology and its applicability to a large area such as the European continent.

- by
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- Image segmentation, Spatial Information, Landsat-7 ETM +

Due to the importance of database systems, integration between two Geomatics sciences, GIS and Remote Sensing has been made in order to support and serve various sectors in Jordan. GIS has been used to create layers that can show decision makers in a simple, easy and flexible manner. Remote Sensing will be used in creating images of how Jordan is seen from space, which will give the users more information and an overview about the study area. Satellite images could be converted into digital image maps, using digitizing procedures. The features viewed in the original scene could be studied by using different remotely sensed techniques. Landsat-7 (ETM+) and SPOT images were implemented, in order to extract the information needed for the topography of the region, land cover areas, boundaries, drainage patterns, road networks, man-used areas, vegetated areas and many other features in Al-Salt areas. The validity and the appropriateness of GIS and Remote Sensing techniques, particularly data fusion of images were evaluated in relation to visualization.

- by Eyad Fadda and +1
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- Relational Database, Remote Sensing, Land Cover, Data Fusion

An automatic cloud cover assessment algorithm was developed for the Landsat 7 ground system. A scene dependent approach that employs two passes through ETM+ data was developed. In pass one, the reflective and thermal properties of scene features are used to establish the presence or absence of clouds in a scene. If present, a scene-specific thermal profile for clouds is established. In pass two, a unique thermal signature for clouds is developed and used to identify the remaining clouds in a scene. The algorithm appears to be a good cloud discriminator for most areas of the Earth. Some difficulty has appeared in imagery over Antarctica, and snow at high illumination angles is occasionally mistaken for cloud.

- by RICHARD IRIAH
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- Algorithm, Landsat-7 ETM +, Thermal Properties, Cloud Cover

This study focuses on using remote sensing for comparative assessment of surface urban heat island (UHI) in 18 mega cities in both temperate and tropical climate regions. Least-clouded day-and night-scenes of TERRA/MODIS acquired between 2001 and 2003 were selected to generate land-surface temperature (LST) maps. Spatial patterns of UHIs for each city were examined over its diurnal cycle and seasonal variations. A Gaussian approximation was applied in order to quantify spatial extents and magnitude of individual UHIs for inter-city comparison. To reveal relationship of UHIs with surface properties, UHI patterns were analyzed in association with urban vegetation covers and surface energy fluxes derived from high-resolution Landsat ETM+ data. This study provides a generalized picture on the UHI phenomena in the Asian region and the findings can be used to guide further study integrating satellite high-resolution thermal data with land-surface modeling and meso-scale climatic modeling in order to understand impacts of urbanization on local climate in Asia. #

- by Hung Tran
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- Remote Sensing, Energy, Satellite remote sensing, Climate

We have used Landsat-5 TM and Landsat-7 ETMþ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630e690 nm), band 5 (1550e1750 nm) and the ratio between bands 1 (450e520 nm) and 4 (760e900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520e600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.

- by Ricardo Díaz-Delgado
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- Remote Sensing, Wetlands, Time Series, Environmental Management

La biomasa es fundamental para realizar estimaciones de carbono en bosques y conocer su estado de conservación. En este trabajo, se realiza un estudio multitemporal de imágenes satelitales para evaluar su uso en la estimación de biomasa del bosque seco del Parque Chaqueño de Argentina. Se estimó biomasa aérea a partir de datos de campo que se correlacionó con información espectral. El principio de la estación seca resultó ser la mejor época para vincular datos satelitales con estructura del bosque, ya que los pastos y arbustos se ven más afectados por el déficit hídrico que los árboles.

- by Nestor Ignacio Gasparri and +1
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- Forest Structure, Carbon Stock, THERMAL INFRARED REMOTE SENSING DATA, Landsat-7 ETM +

This paper combines participatory activities (PA) with remote sensing analysis into an integrated methodology to describe and explain land-cover changes. A remote watershed on Mindanao (Philippines) is used to showcase the approach, which hypothesizes that the accuracy of expert knowledge gained from remote sensing techniques can be further enhanced by inputs from vernacular knowledge when attempting to understand complex land mosaics and past land-use changes. Six participatory sessions based on focus-group discussions were conducted. These were enhanced by community-based land-use mapping, resulting in a final total of 21 participatory land-use maps (PLUMs) co-produced by a sample of stakeholders with different sociocultural and ecological perspectives. In parallel, seven satellite images (Landsat MSS, Landsat TM, Landsat ETM+, and SPOT4) were classified following standard techniques and provided snapshots for the years 1976, 1996, and 2010. Local knowledge and collective memory contributed to define and qualify relevant land-use classes. This also provided information about what had caused the land-use changes in the past. Results show that combining PA with remote-sensing analy-sis provides a unique understanding of land-cover change because the two methods complement and validate one another. Substantive qualitative information regarding the chronology of land-cover change was obtained in a short amount of time across an area poorly covered by scientific literature. The remote sensing techniques contributed to test and to quantify verbal reports of land-use and land-cover change by stakeholders. We conclude that the method is particularly relevant to data-poor areas or conflict zones where rapid reconnaissance work is the only available option. It provides a preliminary but accurate baseline for capturing land changes and for reporting their causes and consequences. A discussion of the main challenges encountered (i.e. how to combine different systems of knowledge), and options for further methodological improvements, are also provided.

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in 2003, resulting in about 22% of the pixels per scene not being scanned. The SLC failure has seriously limited the scientific applications of ETM+ data. While there have been a number of methods developed to fill in the data gaps, each method has shortcomings, especially for heterogeneous landscapes. Based on the assumption that the same-class neighboring pixels around the un-scanned pixels have similar spectral characteristics, and that these neighboring and un-scanned pixels exhibit similar patterns of spectral differences between dates, we developed a simple and effective method to interpolate the values of the pixels within the gaps. We refer to this method as the Neighborhood Similar Pixel Interpolator (NSPI). Simulated and actual SLC-off ETM+ images were used to assess the performance of the NSPI. Results indicate that NSPI can restore the value of un-scanned pixels very accurately, and that it works especially well in heterogeneous regions. In addition, it can work well even if there is a relatively long time interval or significant spectral changes between the input and target image. The filled images appear reasonably spatially continuous without obvious striping patterns. Supervised classification using the maximum likelihood algorithm was done on both gap-filled simulated SLC-off data and the original "gap free" data set, and it was found that classification results, including accuracies, were very comparable. This indicates that gap-filled products generated by NSPI will have relevance to the user community for various land cover applications. In addition, the simple principle and high computational efficiency of NSPI will enable processing large volumes of SLC-off ETM+ data.

- by Suming Jin
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- Image, Heterogeneity, Landscapes, Land Cover

ABSTRAK Penelitian secara khusus bertujuan mengidentifikasi proses akresi dan abrasi sepanjang kawasan pesisir Selatan Provinsi Gorontalo melalui pemetaan dan analisis laju perubahan garis pantai rentang 14 tahun menggunakan teknik sistim informasi geografis pada hasil ekstraksi fitur garis pantai kawasan pesisir Selatan Gorontalo secara spasial temporal menggunakan teknik penginderaan jauh pada dataset citra Landsat tahun 2001 dan tahun 2015. Hasil penelitian menunjukkan bahwa panjang garis pantai Selatan Gorontalo tanpa mengikut-sertakan panjang garis pantai pulau-pulau yang terpisah dari daratan berturut-turut adalah sepanjang 444.28 km tahun 2001 dan 435.25 km tahun 2015 sehingga terdapat fenomena pengurangan garis pantai sepanjang 9.03 km dalam rentang 14 tahun. Intensitas proses akresi dan abrasi berjalan secara bersamaan sepanjang rentang 14 tahun di mana pertumbuhan delta muara sungai di Desa Manawa Kecamatan Patilanggio Kabupaten Pohuwato sangat signifikan menyumbang 58.04% secara keseluruhan luas akresi di pesisir selatan Gorontalo yang berlangsung selama 14 tahun. Baik proses akresi maupun abrasi intensitas lokasinya berkorelasi dengan jenis-jenis tutupan/penggunaan lahan. Korelasi positif mutlak (r=1) pada kedua proses terdapat pada jenis tutupan rawa. Proses akresi yang menjadi proses mendominasi kestabilan sepanjang kawasan pesisir Selatan Gorontalo berkorelasi positif berturut-turut dengan jenis tutupan hutan bakau (r=0.94) dan hutan rawa (r=0.91). Adapun proses abrasi berkorelasi positif signifikan dengan jenis tutupan lahan tambak (r=0.90). Informasi yang diperoleh dari penelitian ini mengindikasikan pentingnya monitoring dan penelitian lanjut yang focus dan detil baik dalam hal dinamika spasial-temporal secara lokal kaitannya dengan jenis dan pola perubahan tutupan lahan, maupun terkait kombinasi penggunaan dataset beresolusi lebih tinggi dalam kajian-kajian ke depan yang relevan dalam rangka pengelolaan kawasan pesisir selatan Gorontalo.

Effective Management and conservation of wildlife populations and their habitats largely depend on our ability to understand and predict species-habitat interactions. Intensive ground surveys cannot keep pace with the rate of land-use change and consequently habitat composition over large areas. We explored how effectively do Remote sensing satellite imagery and GIS modeling technique could be used for assessing habitat suitability of Rhinoceros unicornis and what are the habitat factors influencing rhinoceros distribution in lowland floodplain of Nepal. The Landsat ETM+ satelli te imagery (Path-142& Row-041) of the study area was used for classifying land use/land cover. Image processing and feature extraction was done in Erdas Imagine 8.7. We used supervised classification technique with 350 training points and 40 test points. GI S layers of habitat parameters-continuous distances from grasslands, water body, guard posts, Agriculture/ settlement and categorical land use/land cover map were used as predictor variables. The points of animal presence location were used as suitable pr oxies and Maximum entropy (MAXENT) modeling was run for predicting species potential geographic distribution. The most significant result of the image classification was that the proportion of pure grassland patches in the chitwan National Park is only 7 percent of the total area. Riparian forest, developed as a result of grassland succession, accounts for 8 percent of the park area which otherwise, as a grassland, served as a food source for R. unicornis. The Maxent model based on remotely sensed factors, habitat factors and rhino presence locations resulted in much larger area classified as suitable for Rhinos. The contribution of the variable distance to water (48.6 percent) was highest to impact the model. The model performance was assessed using receiver operating Characteristics (ROC) plots and Jackknife tests.

- by Hemanta Kafley
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- Remote Sensing, Image Classification, Habitat Suitability, Performance Model

There is a great demand for accurate and high-resolution seafloor topography (bathymetry) data. However, at present availability of such data remains spatially incomplete and limited. Bathymetry in shallow water areas is challenging for vessel-based bathymetric surveys and this zone is where remote sensing typically outperforms traditional methods such as echo sounding. The obvious advantages over conventional echo sounding methods include the wide data availability, synoptic surface coverage, and improved spatial resolution. Due to the high cost associated with traditional bathymetric surveys, the availability of up-to-date bathymetric charts in developing countries is rare or limited to a few charts for some areas. In this study, a ratio-transform model was applied on Landsat imagery to derive bathymetry at sites in nine rivers and creeks along the Nigerian coastal area. Accuracy tests comparing the image-derived bathymetry with reference data from echo sounding of Lighthouse Creek in Lagos and hydrographic charts of the other sites confirms good agreement at some sampled locations. The results of this study prove that this remote sensing method can augment existing hydrographic datasets in the country and is a useful reconnaissance tool for hydrographic surveying offices. It also offers a viable interim solution for areas where there is inadequate hydrographic data and scarce resources for extensive surveys using other methods.

- by Chukwuma J Okolie
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- Geomatics, Remote Sensing, Hydrography, Coastal Management

this article talks about a method of determining PM10 accurately and efficiently with teledetection or remote sensing, offering results that closely approach reality. This methodology was implemented in the city of Cuenca-Ecuador which helps to show the critical points of contamination and test the concentrations of PM10 with the normative TULSMA. Furthermore, this research proposes a series of hypotheses and recommendations for future studies that could strengthen this topic and obtain better results which would help to achieve a cleaner and healthier world.

- by Jose Francisco Machado Serrano and +1
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- Teledetección, PM10 Particulate Matter, Landsat-7 ETM +

Satellite images in the thermal infrared can be used for assessing the thermal urban environment as well as for defining heat islands in urban areas. In this study, the thermal environment of major cities in Greece (Athens, Thessaloniki, Patra, Volos and Heraklion) is examined using satellite images provided by the Landsat Enhanced Thematic Mapper (ETM+) sensor on board Landsat 7 satellite corresponding to the daytime and warm period when the surface urban heat island (SUHI) phenomenon is best observed. The spatial structure of the thermal urban environment is analyzed in each case study and the ''hottest'' surfaces within the urban settings are identified and related to the urban surface characteristics and land use. For the needs of the study, the Corine land cover (CLC) database for Greece is also used, in an effort to define more effectively the link between surface emissivities, land surface temperatures and urban surface characteristics.

- by Professor Constantinos Cartalis
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- Engineering, Remote Sensing, Solar Energy, Land Cover
- by Ankit Gupta
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- Nasa, Isro, Landsat TM, Landsat

Urbanisation and development over the past 3-4 decades have reduced the amount of pristine rainforest cover in Brunei Darussalam to less than 50% of the country’s land area. Low-density urban sprawl is most pronounced in the Brunei-Muara District, where commercial shop blocks, housing estates and infrastructure are built at rapid rates. By-products of this process include increasing number of decaying, dilapidated urban spots and bare areas (exposed rock and soil) created by abandoned or stalled projects, which quickly become bad lands. The drastic change in land cover is expected to have a significant impact on the local climatic regime as the heat and water absorption and distribution capacities of rainforest differ significantly from that of bare ground, landscaped green spaces, built features, and even secondary forest. While carbon dioxide emission has dominated the discourse on climate change, land cover/land use change is increasing being recognised as a major contributor to global climate change. This is particularly pronounced in developing regions, where the original land cover in the recent past was pristine natural vegetation. This paper aims to assess the potential impact of urbanisation in Brunei Darussalam on climate change by measuring the change in relative heat distribution pattern (as exemplified by urban heat islands) and vegetation indices, using multi-year satellite imagery, in particular, Landsat thermal and near-infrared bands. The remote sensing study is augmented by ground measurements of ambient heat profiles in selected land cover types, particularly in different categories of green spaces. It is hoped that the study will be beneficial to land use and urban planning and its management, particularly in the refinement and enforcement of green space requirements.

Forest Fire can cause considerable environmental damage and brings about significant change in the ecosystem of a region. The selected study area, Idukki Wildlife Sanctuary is prone to forest fires. The present study aims to demarcate forest fire risk zones of Idukki Wildlife Sanctuary in Kerala using GIS and Remote Sensing techniques. ArcGIS 9.3 and ERDAS Imagine 9.1 software tools are used for this study. The considered factors are land cover type, slope, distance from road, distance from settlement and elevation. The index maps of the selected factors are overlaid by GIS tools to prepare the risk map. A Fire Risk Index (FRI) method is used to prepare the forest fire risk zone map. In the prepared map, the area has been classified into five categories; very high, high, moderate, low, and very low risk zones. To validate the proposed method, the obtained results are compared with fire incidents of past 10 years. The obtained results indicate high accuracy.

- by Vinod PG
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- Biochemistry, Geomatics, Remote Sensing, Environmental Studies

This investigation is intended to estimate the annual soil loss in Wadi Bin Hammad watershed, and to examine the spatial patterns of soil loss and intensity, as an essential procedure for proper planning of conservation measures. To achieve these objectives, the revised universal soil loss equation (RUSLE) model has been applied in a geographical information system framework. After computing the RUSLE parameters (R, K, LS, C and P) soil erosion risk and intensity maps were generated, then integrated with physical factors (terrain units, elevation, slope, and land uses/cover) to explore the influence of these factors on the spatial patterns of soil erosion loss. The estimated potential annual average soil loss is 40.4 ton ha-1year-1, and the potential erosion rates from calculated class ranges from 0.0 to 100 ton ha-1year-1. Soil erosion risk assessment indicates that 14.63 % of the catchment is prone to high to extreme soil losses higher than 75 ton ha-1year-1. The lower and middle parts of the catchment suffer from high, severe, to extreme soil erosion. While 57.83 % of the basin still undergoes very low , low and moderate levels of soil loss of less than 75 ton ha-1year-1. The present results provide a vital database necessary to control soil erosion in order to ensure sustainable agriculture in the southern highlands region of Jordan.

- by IAEME Publication
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- Jordan, Land Use Change, Rs, Erosivity

Flooding due to excessive rainfall in a short period of time is a frequent hazard in the flood plains of monsoon Asia. In late September 2000, a devastating flood stuck Gangetic West Bengal, India. This particular event has been selected for this study. Instead of following the conventional approach of flooded area delineation and overall damage estimation, this paper seeks to identify the rural settlements that are vulnerable to floods of a given magnitude. Vulnerability of a rural settlement is perceived as a function of two factors: the presence of deep flood water in and around the settlement and its proximity to an elevated area for temporary shelter during an extreme hydrological event. Landsat ETM C images acquired on 30 September 2000 have been used to identify the non-flooded areas within the flooded zone. Particular effort has been made to differentiate land from water under cloud shadow. ASTER digital elevation data have been used to assess accuracy and rectify the classified image. The presence of large numbers of trees around rural settlements made it particularly difficult to extract the flooded areas from their spectral signatures in the visible and infrared bands. ERS-1 synthetic aperture radar data are found particularly useful for extracting the settlement areas surrounded by trees. Finally, all information extracted from satellite imageries are imported into ArcGIS, and spatial analysis is carried out to identify the settlements vulnerable to river inundation. 3700 J. SANYAL AND X. X. LU detailed spatial database for flood prevention and mitigation in the developing countries. In recent years, efforts have been made to use remote sensing and geographic information systems (GISs) for creating national-level flood hazard maps for Bangladesh . Population density and other socio-economic data have been integrated with hydrologic information to identify priority zones for implementing anti-flood measures . These studies were undertaken on a regional scale using coarse-resolution AVHRR imageries from NOAA satellites. The results of such investigations would only be useful for nationallevel macro planning.

- by Olatunji Aboyeji
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- Environmental Engineering, Civil Engineering, Remote Sensing, Spatial Analysis

The expansion of built-up areas in the city is one of the major research subjects in urban planning and development. It indicates how much the built-up area is increasing and the rate of increase over time. Also, the specific regions which are potential for future urban development can be enumerated. This paper uses two methods for built-up area extraction. The first method incorporates the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) to extract built-up area from LANDSAT-7-LANDSAT-8 imagery. The second method added Normalized Difference Water Index (NDWI), Short Wave Infrared (SWIR), and Thermal Infrared (TIRS) for built-up area extraction. This study’s primary goal is to identify the difference of built-up areas over time. Three years of LANDSAT-7 and LANDSAT-8 imagery (1999, 2013, and 2018) of Khulna city, Bangladesh, has been used in this study. The difference between the two conventional methods has also been reviewed. The satellite image analysis results reveal that the built-up area expansion from 1999 to 2013 is significantly higher than from 2013 to 2018. Results from the two different methods indicate that the use of NDWI, SWIR, and TIRS can increase the accuracy of extracting built-up areas.

- by Iron Gesu
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- Remote Sensing, QuickBird, Landsat-7 ETM +, Landsat 8 TIRS

This study evaluated the potential of using the Surface Energy Balance Algorithm for Land (SEBAL) as a means for estimating evapotranspiration (ET) for local and regional scales in Southern Idaho. The original SEBAL model was refined during this study to provide better estimation of ET in agricultural areas and to make more reliable estimates of ET from other surfaces as well, including mountainous terrain. The modified version of SEBAL used in this study, termed as SEBALm (lD stands for Idaho) includes standardization of the two SEBAL "anchor" pixels, the use of a water balance model to track top soil moisture, adaptation of components of SEBAL for better prediction of the surface energy balance in mountains and sloping terrain, and use of the ratio between actual ET and alfalfa reference evapotranspiration (ETr) as a means for obtaining the temporal integration of instantaneous ET to daily and seasonal values.

- by Ricardo Trezza
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- Seasonality, Surface energy balance, Soil moisture, Evapotranspiration

The Kathmandu Valley of Nepal epitomizes the growing urbanization trend spreading across the Himalayan foothills. This metropolitan valley has experienced a significant transformation of its landscapes in the last four decades resulting in substantial land use and land cover (LULC) change; however, no major systematic analysis of the urbanization trend and LULC has been conducted on this valley since 2000. When considering the importance of using LULC change as a window to study the broader changes in socio-ecological systems of this valley, our study first detected LULC change trajectories of this valley using four Landsat images of the year 1989, 1999, 2009, and 2016, and then analyzed the detected change in the light of a set of proximate causes and factors driving those changes. A pixel-based hybrid classification (unsupervised followed by supervised) approach was employed to classify these images into five LULC categories and analyze the LULC trajectories detected from them. Our results show that urban area expanded up to 412% in last three decades and the most of this expansion occurred with the conversions of 31% agricultural land. The majority of the urban expansion happened during 1989–2009, and it is still growing along the major roads in a concentric pattern, significantly altering the cityscape of the valley. The centrality feature of Kathmandu valley and the massive surge in rural-to-urban migration are identified as the primary proximate causes of the fast expansion of built-up areas and rapid conversions of agricultural areas.

- by Asif Ishtiaque and +1
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- Remote Sensing, Urbanization in Developing Areas, Nepal, Remote sensing and GIS applications in Landscape Research

In this study, Landsat 7 ETM+ data sets were used to study the geomorphology and mineralogy of different dune types in the eastern part of Abu Dhabi in United Arab Emirates. The extraction and mapping of the dominant mineral phase of the investigated sand dunes were carried out using image processing integrated with field techniques. Band ratios 6/4 and 5/7 were very useful for mineralogical distinction of (i) high mafic content areas (ratio 5/7), and (ii) carbonate and quartz content (ratio 6/4). The results of the present study show that these dunes are classified into several main classes based on composition. The first class is low dunes composed of a mixture of quartz and carbonate, the second class is carbonate-rich, the third quartz-rich and the fourth mafic mineral-rich. Morphologically, the dunes in the investigated area belong to two main types. The first is linear and is trending NE–SW. This type shows transitional change in mineralogy from carbonate-rich components to iron oxide-rich components. This mineral gradient was observed on Landsat images as spectral variations and color tones, and was confirmed from ground truth data. The second type is star dunes and appears in images as radially symmetrical to mound shape that characterize multidirectional wind systems. The results of this study show that multispectral data can be used to differentiate between different dune types and their associated mineralogy, and to reveal information on the dynamic processes shaping dunes, such as prevailing wind directions.

- by Fares Howari and +2
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- Geology, Geomorphology, Mineralogy, Image Processing

Retrospective understanding of the magnitude and pace of urban expansion is necessary for effective growth management in metropolitan regions. The objective of this paper is to quantify the spatial-temporal patterns of urban expansion in the Greater Kumasi Sub-Region (GKSR)—a functional region comprising eight administrative districts in Ghana, West Africa. The analysis is based on Landsat remote sensing images from 1986, 2001 and 2014 which were classified using supervised maximum likelihood algorithm in ERDAS IMAGINE. We computed three complementary growth indexes namely; Average Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII) and Urban Expansion Differentiation Index to estimate the amount and intensity of expansion over the 28-year period. Overall, urban expansion in the GKSR has been occurring at an average annual rate of 5.6 percent. Consequently, the sub-region's built-up land increased by 313km 2 from 88km 2 in 1986 to 400km 2 in 2014. The analysis further show that about 72 percent of the total built-up land increase occurred in the last 13 years alone, with UEII value of 0.605 indicating a moderate intensity of urban expansion. Moreover, the metropolitan-core of the sub-region, being the focal point of urban development and the historical origins of expansion, accounted for over half of the total built-up land increase over the 28-year period. Over the last decade and half however, urban expansion has spilled into the neighbouring peripheral districts, with the highest intensity and fastest rate of expansion occurring in districts located north and north east of the sub-regional core. We recommend a comprehensive regional growth management strategy grounded in effective strategic partnerships among the respective administrative districts to curb unsustainable urban expansion.

- by Ransford Acheampong
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- Geography, Spatial Analysis, Urban Planning, Ghana

- by Ricardo Trezza
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- Seasonality, Surface energy balance, Soil moisture, Evapotranspiration

Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012-2018; r = 0.88), Landsat 8 (2012-2018; r = 0.63) and Landsat 5,7,8 (1988-2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43ha) to 2011 (83ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

Monitoring fire effects at landscape level is viable from remote sensing platforms providing repeatable and
consistent measurements. Previous studies have estimated fire severity using optical and synthetic aperture radar
(SAR) sensors, but to our knowledge, none have compared their effectiveness. Our study carried out such a
comparison by using change detection indices computed from pre- and post-fire L-band space borne SAR datasets
to estimate fire severity for seven fires located on three continents. Such indices were related to field estimated
fire severity through empirical models, and their estimation accuracy was compared. Empirical models based on
the joint use of optical and radar indices were also evaluated. The results showed that, optical based indices
provided more accurate fire severity estimates. On average, overall accuracy increased from 61% (SAR) to 76%
(optical) for high biomass forests. For low biomass forests (i.e., above ground biomass levels below the L-band
saturation point), radar indices provided comparable results, with overall accuracy being only slightly lower when
compared to optical indices (69% vs. 73%). The joint use of optical and radar indices decreased the estimation
error and reduced misclassification of unburnt forest by 9% for eucalypt and 3% for coniferous forests.
Additional keywords: Landsat, ALOS PALSAR, L-band, radar, accuracy assessment, radar-optical synergy, CBI

Integrated watershed management (IWSM) was implemented to address issues of poverty and land resource degradation in the 14 500 ha upper Agula watershed, in semiarid Eastern Tigray (Ethiopia), an area known for poverty and resource degradation caused by natural and man-made calamities. The purpose of this study was to assess the impact of IWSM and determine the land use and cover dynamics that it has induced.

- by Seppe Deckers and +4
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- Engineering, Remote Sensing, Catchment Management, Land Use Change

Türkiye’nin Karadeniz’e olan kıyı kesiminde en savunmasız kısmı Sakarya ili içerisinde yer almaktadır. 50km uzunluğundaki kıyı şeridi bu havzada milyonlarca yıldır Sakarya Nehri’nin taşıdığı çökelti ile oluşmuştur (Kutoğlu vd. 2010). 1996 yılında Sakarya nehri yakınındaki Karasu ilçesinde, nehrin 1km doğusuna balıkçı limanı kurulmuştur. Projenin ilerleyen aşamalarında bu liman 1.5km uzunluğunda dalgakıranı olan bir limana dönüştürülmüş ve inşaatı 2008 yılında tamamlanmıştır. İnşaat sırasında, kıyı şeridi çizgisinin kıyı kesiminde bulunan evlere doğru yaklaştığı gözlemlenmiştir. Daha sonra şiddetini giderek arttıran kıyı erozyonu, 2010 yılının Ocak ayında kıyı kesiminin ön plandaki evlere zarar vermiştir.
Bu çalışmada Karasu kıyı şeridindeki değişikliklerin, nesne tabanlı görüntü analizi yaklaşımları kullanılarak elde edilen sınıflandırma sonuçlarının zamansal analiz çalışmalarına yer verilmiştir. Bu amaçla, test alanı için 1987, 2001, 2006 ve 2010 yıllarına ait Landsat 5 ve Landsat 7 uydu görüntüleri kullanılmıştır. Bu görüntüler, eCognition yazılımın ana adımları olan segmentasyon ve sınıflandırma aşamalarından geçirilerek işlenmiştir. Diğer yandan, nesne tabanlı sınıflandırma sonuçları, piksel tabanlı sınıflandırma sonuçları, referans vektör haritaları ve ekran üzerinden manuel vektörleştirme sonuçları ile karşılaştırılmıştır. Belirtilen sınıflandırma sonuçlarının doğruluk analizleri sunulmuş ve yorumlanmıştır.
Bu sonuçlara göre; Landsat görüntülerinin zamansal analiz için uygun veri kaynağı olmalarının yanında, yüksek doğruluk isteyen analizlerde kullanılamadığı, nesne-tabanlı sınıflandırma tekniği ile kıyı şeridinin yarı-otomatik olarak çıkarımının hızlı bir şekilde yapılabildiği ve bu çıkarımların ekran üzerinden manuel vektörleştirme sonuçlarına benzer olduğu görülmüştür. Çalışmada kıyı şeridine dik oluşturulan üç kesitin değerlendirilmesi sonucunda, nesne-tabanlı sınıflandırma yaklaşımı ile 2006 ve 2010 Landsat görüntüleri kullanılarak kıyı erozyonunun çıkarılamadığı, diğer görüntülerde ise, karşılaştırılan tüm yaklaşımların birbirlerine yakın sonuçlar verdiği belirlenmiştir. Çıkarılan sonuçlar doğrultusunda, bu tür kıyı erozyonu çalışmalarında yüksek çözünürlüklü görüntülerin kullanılması ile zamansal analizin başarı ile yapılabileceği ve Coğrafi Bilgi Sistemi (CBS) ortamına entegre edilebileceği görülmüştür.

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R 2 between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.

- by Kelley O'Neal
- •
- Remote Sensing, Geomatic Engineering, First-Order Logic, Boreal Forest

The North-Western part of Bangladesh has been experiencing extreme weather and frequent drought conditions compare to
the other parts of the country. In this paper, we used MYD13Q1 and MOD11 of Moderate Resolution Imaging Spectroradiometer
(MODIS)/Terra in order to extract Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Finally, these both
information helped to derive Vegetation Health Index (VHI) during high summer periods in 2000, 2008 and 2014 to assess drought
vulnerability in terms of agriculture. Mainly NDVI (Normalized Difference Vegetation Index) and LST (Land Surface temperature)
were the main geospatial data to map Vegetation Health Index (VHI). From the VHI analysis, we found that about 147956 (29% of the
total), 173194 (34% of the total) and 191352 (37% of the total) hectare lands as extreme, high and moderate drought areas respectively
in the study area. In addition to this analysis, Naogoan and ChapaiNabanganj districts were found as the extreme to high drought
vulnerable areas in terms of agriculture.

- by Shareful Hassan
- •
- Remote Sensing, Drought, Desertification, Bangladesh

Seasonally continuous long-term information on surface water and flooding extent over subcontinental scales is critical for quantifying spatiotemporal changes in surface water dynamics. We used seasonally continuous Landsat TM/ETM+ data and generic random forest-based models to synoptically map the extent and dynamics of surface water and flooding (1986–2011) over the Murray–Darling Basin (MDB). The MDB is a large semi-arid basin with competing demands for water that has recently experienced one of the most severe droughts in the southeast of Australia. We used a stratified random probability sampling design with 500 sample pixels each observed across time to assess the accuracy of the surface water maps. We further developed models to map flooded forest at a riparian site that experienced severe tree dieback. Water indices and bands 5 and 6 were among the top 10 explanatory variables most important for mapping surface water. Surface water extent per season per year showed high inter-annual and seasonal variability, with low extent and variability during the Millennium Drought (1999–2009). Accuracy assessment yielded an overall classification accuracy of 99.9% (±0.02% standard error) with 87% (±3%) and 96% (±2%) producer's and user's accuracy of water, respectively. User's and producer's accuracies of water were higher for Landsat 7 than Landsat 5 data. Both producer's and user's accuracies of water were lower in wet years compared to dry years. The approach presented here can be further developed for global application and is relevant to areas with competing water demands. Quantifying the uncertainty of the accuracy assessment and providing an unbiased accuracy estimate are imperative steps when remotely sensed products are intended to be used for follow on applications.

- by Mark Broich and +1
- •
- Australia, Arid environments, Surface Water Modeling, Environmental Remote Sensing

This research was conducted in the Afzar sub-catchment area of Ghara-Aghaj River, a semi-arid region in SW Iran, using a Geographic Information System (GIS) to compare the Erosion Potential Method (EPM) and Pacific Southwest Interagency Committee (PSIAC) models in erosion-potential mapping and sediment-yield assessment. Data layers used in this study were generated from topographic maps, Landsat ETM C imagery, aerial photographs, field surveys and barometric and pluviometric data; factor-class evaluation was used to determine EPM and PSIAC parameters. A raster-based Geographic Information System (GIS) was applied to generate the erosion-severity and sedimentyield maps. Output data was verified by field observation and by comparison with a Global Assessment of Soil Degradation (GLASOD) map. Comparison of the EPM and PSIAC results with field observations and the GLASOD map showed that although the results of the two erosion potential maps correspond in most areas, the results of EPM model were not as reliable as the PSIAC in identifying areas with very high erosion potential. It is suggested the EPM model should be used for rapid mapping of erosion-potential in regions with limited data layers, but field verifications indicated that PSIAC results were the more reliable. q

- by Majid Tangestani
- •
- Geology, Geochemistry, Geophysics, Field Survey

Abstract
In this modern era of science and technology, when people are more cautious
about their surroundings, new paths are being invented to see the world from
different point of view. People are starting to avoid their presence for
collecting data. Rather they are using the eyes form the sky to know their
surroundings and collecting information about the world. This technology is
known as satellite system and it is one of the reliable source of data now a
days. Almost every developed countries of the world is using this technology
to collect data of the earth surface. Popular Satellite system like ESA
(European Space Agency) and USGS (United States Geological Survey) is
providing free satellite image and data that is very helpful for different
research and educational organizations. The data extraction is very simple.
Though fully accurate data cannot be found for some environmental or
technical error like fog, haze and cloud, but still the data that can be found is
reliable. Different research and educational organization is using these data
and this science is mainly known as Remote Sensing. Also the satellite image
is very important for different GIS analysis. In this report, the main focus is
collecting satellite data, preprocessing it and identifying the built up area from
it. It is a task which is under Remote Sensing course but in this case ArcGIS
software was used because it is a reliable software, both for raster data like
satellite image and also for vector data.

- by Sk Nafiz Rahaman
- •
- Landsat, Landsat-7 ETM +, NDBI, Landsat 8

Remote sensing of Urban Heat Islands (UHIs) usually used Land Use and Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), normalized difference built-up index (NDBI) and Vegetation-Impervious Surface Area (ISA)-Soil (V-I-S) model separately in the past. The purpose of this paper is to examine the relationship between surface thermal patterns with land cover types, NDVI and VIS model as a whole. LST was derived using three different algorithms from remotely sensed images at two different scales and LUCC map was obtained by SVM classification method. The results demonstrate that LST retrieval from Landsat ETM+ image by improved MWA algorithm is more suitable for analyzing relationship between urban structures with urban heat islands in Xuzhou.

- by Junshi Xia
- •
- Remote Sensing, Land use and land cover, Land Cover, Surface Area

Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools ocean waters in the equatorial Pacific and changes rainfall patterns across the world. This change in weather pattern has resulted in increased rainfall over Southern Africa causing flash floods resulting in extensive socioeconomic loses, casualties and environmental damage. This study employs remote sensing and geographical information systems (GIS) data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers' plans for future occurrences. To achieve these objectives, the study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geological Survey (USGS) and NASA's Earth Observatory websites in order to show the spatial dimensions of the damage and the flooded area. Results of the study revealed notable damages to social and natural environments as well as flood risk zones and watercourses in the study area. The paper concludes by outlining policy recommendations in the form of the need for building drainage ditches on the flat plains identified in this study to accommodate flood flows, the design of a comprehensive Regional Emergency Information System (REIS) with support from the governments in the study area and the neighboring countries.

- by Kia Alexander
- •
- Geology, Remote Sensing, Climate Change, Mozambique

Over the last few decades, Casablanca city became the biggest industrial, commercial center in Morocco with
rapid urbanization and explosive population growth, more than 4 million people. Urban expansion has reached to suburban
areas due to population growth and socio economic development, not to mention the rapid increase of transportation.
Result of these changes causes a change of microclimate in urban areas. The most evident phenomenon is the increase of
urban surface temperature as compared with suburban areas, “heat island” is formed in the atmospheric boundary above
urban area. It could make serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal
pollution). Thermal infrared remote sensing bands, proved its capability in monitoring temperature field. The purpose of this
study is to evaluate the use of Landsat TM, ETM+, OLI and TIRS data for indicating temperature differences in urban areas, in
order to achieve a spatiotemporal study, using data between 1984 and 2014, and showing the relationship between urban
expansion and the heat island effect during time, producing maps that shows the distribution of urban temperature. Results
can be combined with land use/ land cover maps or thermal-land cover and operated as reference for urban planning and
future solutions to reduce heat island effect.

- by rida azmi and +2
- •
- Remote sensing and GIS applications in Landscape Research, Urban Heat Island Effect, Land Surface Temperature, Landsat-7 ETM +

Using forest inventory data and Landsat ETM+ data, linear fixed-effects models and linear mixed-effects models are developed based on the allometric growth model. The surface area of the normalized difference vegetation index (NDVIsa) is developed from the triangulated irregular network (TIN) with the aid of image-processing and the three-dimensional analysis extensions of Environmental Systems Research Institute's ArcView GIS software. The NDVIsa is used as the predictor, and it implies the area of trees for both site and area. Linear fixed-effects and linear mixed-effects models based on the allometric growth model are developed to fit the relationships between either biomass or volume and NDVIsa. Linear mixed-effects model with both intercept and slope having random-effects best fits the data, and the relatively high R 2 (about 0.57) was achieved. This linear mixed-effects model is significantly different from the linear fixed-effects model and the linear mixed-effects model with only intercept having random-effects. The best fitted linear mixed-effects model discovers different spatial characteristics of biomass and volume of trees across the whole state of Georgia. The Piedmont ecoregion has positive allometric characteristics, the Upper Coastal Plain has negative allometry, whereas the other three ecoregions have isometric characteristics. At last, the linear mixed-effects models were compared with the extreme situation, fitting linear fixed-effects models within each region. Model diagnostics indicated that the linear mixed-effects models were the best modeling approach.

- by Marguerite Madden and +1
- •
- Forest Ecology And Management, Forest Ecology, Biological Sciences, Environmental Sciences

Farmers in northern Laos have been experiencing a rapid transformation from subsistence agricultural production to intensive cash crop cultivation over the last decade. In particular, this transformation has been accompanied by conversion of secondary forest areas which were formerly part of the upland swidden system into permanent agricultural land. Introduction of new crops such as sugar cane, maize, and rubber are providing new economic opportunities to upland farmers who have traditionally been dependent on upland rice cultivation for household food consumption. While this commercialisation of upland agricultural production is being promoted by the government to alleviate rural poverty, not all upland communities are able to capitalise on the emerging opportunities. The current research examines land-use change patterns and the driving forces behind farmer's decisions regarding changing land use and selecting crops, particularly in areas along the new North-South Economic Corridor that passes through Luang Namtha and Bokeo provinces in northwestern Laos. The study incorporates spatial analysis, using LandSat ETM+ and ASTER satellite images from 2000 and 2005 to understand the recent trend of landuse change in Sing and Vieng Phoukha districts of Luang Namtha province as well as Houay Xay district of Bokeo province. To analyse key factors that influenced community land and resource management practices, and farmers' decisions, the research also incorporated techniques such as cognitive mapping, group interviews and household interviews. Policy review and local stakeholder discussions were used to understand how government policies affect land and resource use in the study sites.

- by Sithong Thongmanivong and +1
- •
- Spatial Analysis, Rural Poverty, Cognitive Map, Land Use Change

"This research paper compares the result of Object based and Pixel based classification techniques for glacier change detection on Landsat Thematic mapper (TM) and Enhanced Thematic Mapper (ETM+) imageries. The objective of this study is to see which classification method performs better for change detection in mountainous regions. Northern face of Himalayan region, The study area is undergoing climate change in the form of rapid melting of glacial ice mass, expansion of the existing lakes and creation of new lakes. This results in Glacial Lake Outburst Flood (GLOF) and breach or outburst from ice
and ‘moraine dams’ causing devastating floods
downstream. The global warming phenomenon worldwide has resulted in a significant decrease in glacial cover.
Glacier’s change monitoring
and permafrost-related hazards have long been studied using remote sensing data and techniques to assess the damage. The world is facing a serious problem of handling the climate change issue and its effects on humans as well as on natural resources. Glaciers are considered as one of the best indicators of climate change [1]. Landsat TM/ETM+ images were used for glacier
change monitoring of Turkey’s mountains project, Mount
Suphan. The results show that about ¾ of total area of suphan glacier has been lost in 23 years. Traditional image classification methods use only the spectral information at pixel level without considering the shape of underlying objects [2]. However, object-based image classification process uses spectral and spatial dimensions (shape of feature) in order to perform classification. In this study, multi temporal Landsat TM and ETM+ image from 1990 to 2010 have been used. Initially, the traditional pixel-based classification was performed on Landsat thematic layers and layers developed from indices like NDVI and NDSII. Then object-based classification of these images was carried out. The comparison of the classification results (both qualitative and quantitative) show that the object-based approach gives about 10-15% higher accuracy, much better results in terms of area estimation and change detection of snow covered areas as compared to traditional pixel-based classification. The results also indicate that object based classification is more useful in mountainous regions to avoid confusion among classes produced by shadows."

- by Saad Ali
- •
- Physical Geography, Image Processing, Remote Sensing, Climate Change

The main task of this article is the evaluation of the IHS color transformation fusion, color composite ratios, and principal component analysis techniques for lithologic discrimination of the basement rocks exposed at Buwatah area, Western Arabian Shield, Saudi Arabia. Landsat ETM+ images were prepared and used to perform this task using PCI Geomatica software. IHS fusion technique was conducted through four main processing steps: (1) registration of the multispectral image (7, 4, and 2 in RGB) to the panchromatic image and then resample it to the same spatial resolution as that of the panchromatic image; (2) transformation of the three multispectral bands from RGB to IHS space; (3) substitution of the intensity value from the high spatial resolution panchromatic band; and finally (4) back transformation to RGB. The band ratios 5/7, 3/1, and 4/3 displayed in RGB, respectively, were used to produce the color composite ratio image. The first principal component (PC1), the second principal component (PC2), and the third principal component (PC3), displayed in RGB, respectively, were used to construct the color composite principal component image. The resultant images successfully discriminated the exposed rock units in the study area and a lithologic map has been constructed that is subjected to precise field verification. The stratigraphy of the area under consideration starts with metavolcanics and associated volcaniclastics as an oldest rock unit, followed by granodiorite-diorite, pink granite, biotite granite, acidic and basic dykes, and Cenozoic volcanics. A new rock unit (biotite granite) has been introduced that was not represented in previous mapping of the considered sector. The biotite granite is verified by field and petrographical studies.

- by Mohammed Qari
- •
- Geology, Principal Component Analysis, Saudi Arabia, Spatial resolution

1] Mangroves are salt tolerant plants that grow within the intertidal zone along tropical and subtropical coasts. They are important barriers for mitigating coastal disturbances, provide habitat for over 1300 animal species and are one of the most productive ecosystems. Mozambique's mangroves extend along 2700 km and cover one of the largest areas in Africa. The purpose of this study was to determine the countrywide mean tree height spatial distribution and biomass of Mozambique's mangrove forests using Landsat ETM+ and Shuttle Radar Topography Mission (SRTM) data. The SRTM data were calibrated using the Landsat derived land-cover map and height calibration equations. Stand-specific canopy height-biomass allometric equations developed from field measurements and published height-biomass equations were used to calculate aboveground biomass of the mangrove forests on a landscape scale. The results showed that mangrove forests covered a total of 2909 km 2 in Mozambique, a 27% smaller area than previously estimated. The SRTM calibration indicated that average tree heights changed with geographical settings. Even though the coast of Mozambique spans across 16 degrees latitude, we did not find a relationship between latitude and biomass. These results confirm that geological setting has a greater influence than latitude alone on mangrove production. The total mangrove dry aboveground biomass in Mozambique was 23.6 million tons and the total carbon was 11.8 million tons. Citation: Fatoyinbo, T. E., M. Simard, R. A. Washington-Allen, and H. H. Shugart (2008), Landscape-scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data,

- by Robert A. Washington-Allen and +1
- •
- Remote Sensing, Plant Ecology, Multidisciplinary, Mangrove forest
- by Yanjun Shen
- •
- Remote Sensing, Arid Land Ecology, Desertification, Geomatic Engineering

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing "more crop per drop" (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya

- by Chandrashekhar Biradar
- •
- Analytical Chemistry, Remote Sensing, Sensors, Water Use

Flooding is a major problem facing Southern African region. The region has been experiencing flood for the past two decades. This flood event has been exacerbated in recent years by global weather pattern known as La Niña which cools ocean waters in the equatorial Pacific and changes rainfall patterns across the world. This change in weather pattern has resulted in increased rainfall over Southern Africa causing flash floods resulting in extensive socioeconomic loses, casualties and environmental damage. This study employs remote sensing and geographical information systems (GIS) data to visualize the impact of climate change caused by flooding in the Southern African region in order to assist decision makers' plans for future occurrences. To achieve these objectives, the study used Digital Elevation Model (DEM), temporal Landsat Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites data obtained from the United States Geological Survey (USGS) and NASA's Earth Observatory web-sites in order to show the spatial dimensions of the damage and the flooded area. Results of the study revealed notable damages to social and natural environments as well as flood risk zones and watercourses in the study area. The paper concludes by outlining policy recommendations in the form of the need for building drainage ditches on the flat plains identified in this study to accommodate flood flows, the design of a comprehensive Regional Emergency Information System (REIS) with support from the governments in the study area and the neighboring countries. Building such system, the paper con

- by Prof. Yaw A . Twumasi
- •
- Remote Sensing, Climate Change, Mozambique, Southern Africa

In 2003 scan-line corrector (SLC) of the Landsat
7 Enhanced Thermal Mapper Plus (ETM+) sensor suffer
ed major failure. Efforts that
NASA made to correct the SLC malfunction have not b
een successful and the problem appears to be perman
ent. Without the operating SLC
there are gaps in images, ranging as large as 14 pi
xels near the edges. Since 2003, a number of gap fi
lling methods have been developed. First
method being reviewed fill gaps for display only, c
alled Focal Analysis. Second method, which is more
complex, is convenient for scientific
analysis, called Localized Linear Histogram Match (
LLHM) and uses geo statistical approach to fill gap
pixels. First method, Focal Analysis, is not
satisfactory for any further analysis, where as the
other method, LLHM, delivers good results achieved
by geo statistical calculation.