QuickBird Satellite Imagery Research Articles

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.

Improving image classification in a complex wetland ecosystem through image fusion techniques

The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram–Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram–Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram–Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.

Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheat fields using multi-spectral QuickBird satellite imagery for site-specific weed management. The research was conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and 2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluate the wild oat patches mapping at the field level, both pixel- and object-based image analyses were tested with six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The results showed that weed patches could be accurately detected with both analyses obtaining global accuracies between 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate for both the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales. The first segmentation scale was classified using the MD and ML algorithms to discriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and 88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentation scale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based on the most accurate results obtained in the field-level study, pixel-based classifications using the MD, ML and SVM algorithms were applied to the wheat fields identified. The results of these broad-level analyses showed that wild oat patches were accurately discriminated in all the wheat fields present in the entire images with accuracies greater than 91% for all the classifiers tested.

Habitat Mapping and Change Assessment of Coastal Environments: An Examination of WorldView-2, QuickBird, and IKONOS Satellite Imagery and Airborne LiDAR for Mapping Barrier Island Habitats

Habitat mapping can be accomplished using many techniques and types of data. There are pros and cons for each technique and dataset, therefore, the goal of this project was to investigate the capabilities of new satellite sensor technology and to assess map accuracy for a variety of image classification techniques based on hundreds of field-work sites. The study area was Masonboro Island, an undeveloped area in coastal North Carolina, USA. Using the best map results, a habitat change assessment was conducted between 2002 and 2010. WorldView-2, QuickBird, and IKONOS satellite sensors were tested using unsupervised and supervised methods using a variety of spectral band combinations. Light Detection and Ranging (LiDAR) elevation and texture data pan-sharpening, and spatial filtering were also tested. In total, 200 maps were generated and results indicated that WorldView-2 was consistently more accurate than QuickBird and IKONOS. Supervised maps were more accurate than unsupervised in 80% of the maps. Pan-sharpening the images did not consistently improve map accuracy but using a majority filter generally increased map accuracy. During the relatively short eight-year period, 20% of the coastal study area changed with intertidal marsh experiencing the most change. Smaller habitat classes changed substantially as well. For example, 84% of upland scrub-shrub experienced change. These results document the dynamic nature of coastal habitats, validate the use of the relatively new Worldview-2 sensor, and may be used to guide future coastal habitat mapping.

Mapping the Slums of Dhaka from 2006 to 2010

Background. Rapid urban growth in low and middle income countries is frequently characterized by informal developments. The resulting social segregation and slums show disparities in health outcomes for the populations of the world’s megacities. To address these challenges, information on the spatial distribution of slums is necessary, yet the data are rarely available. The goal of this study was to use a remote sensing based approach to map urban slums in Dhaka, the second fastest growing megacity in the world. Methods. Slums were mapped through the visual interpretation of Quickbird satellite imagery between the years 2006 and 2010. Ancillary references included the 2005 census and mapping of slums, Google Earth, and geolocated photographs. The 2006 slums were first delineated and filtered in GIS to avoid small, isolated slums. For 2010, changes to the 2006 slums were defined over the latter’s polygons to retain border consistency. Conclusions. The dataset presented here can be considered a stepping stone for further research on slums and urban expansion in Dhaka. The slum distribution dataset is useful to be pooled with other data to reveal trends of informal settlement growth for local health policy advice in Dhaka.

Soil carbon pools in tundra and taiga ecosystems of northeastern Europe

The mean pools of soil carbon were determined for the first time for twelve soil groups (according to the World Reference Base for Soil Resources, 2006) on four test plots with the use of the high-resolution (Landsat and QuickBird) satellite imagery, original field data on more than 200 soil profiles, and literature data included in the soil database. Three test plots belonged to the ecotone between tundra and forest-tundra zones, and the fourth plot characterized the middle taiga zone. Spatial distribution patterns of soil carbon in different soil subgroups and genetic horizons were characterized for the areas with the mosaic soil and vegetation covers. The mean soil carbon content for the first three test plots in permafrost area was estimated at 39.5 kg C/m2, including 28.7 kg C/m2 in the upper soil meter. The mean soil carbon pool of the taiga plot reached 16.7 kg C/m2 (0–100 cm).

Landscape resource mapping for wildlife research using very high resolution satellite imagery

Summary Quantifying wildlife‐habitat relationships through resource selection analysis (RSA) has traditionally relied on landscape variables extracted at medium‐to‐coarse scales and general‐purpose digital maps. However, RSA at fine scales, facilitated by accurate positional data obtained using GPS‐tags, requires improved measures of habitat resources. The combination of cutting‐edge remote sensing technology, such as very high spatial resolution (VHSR) satellite imagery and object‐based image analysis (OBIA), can provide landscape maps that are suitable for the extraction of detailed variables at fine scale. We used Quickbird satellite imagery and OBIA to produce a map using the multispectral bands (MULT), and explored the usefulness of the technique for resource identification by combining the panchromatic (highest spatial resolution) and multispectral (spectral information) bands (PAN:MULT) to produce a second map. Each of the mapping methods was used in a heterogeneous braided‐river environment in New Zealand to: (1) classify and delimit ground features using an object‐based accuracy assessment approach; (2) detect ground features of different sizes; (3) extract independent landscape variables at fine scale (within buffers between 20 and 30 m) for separate RSA for introduced hedgehogs (Erinaceus europaeus) and feral cats (Felis catus). Per‐pixel accuracy assessment produced overall accuracies of 82% (PAN:MULT) and 79% (MULT). The per‐object assessment using shrubs as the testing class yielded further information on classification and delimitation of object accuracy, with accuracies of 80% for shrub patches ≥30 m2 in MULT and ≥5 m2 in PAN:MULT. The inclusion of the panchromatic band noticeably improved the identification and delimitation of cover. However, RSA using each of the maps did not yield differences in the best models for cats or hedgehogs. VHSR imagery and OBIA provide a valuable method to identify smaller ground features and thus to produce more detailed landscape maps to study animal habitat use at fine scale. Improvements in ground feature detection achieved by including the panchromatic band may not justify its cost, as it was shown for the studies on cats and hedgehogs presented here. However, the level of detail offered by the panchromatic layer may be useful for addressing other RSA questions or applications at fine scale, such as remote censusing of colonial species.

GEOPROCESSAMENTO DA BACIA DO CÓRREGO VINHEDO EM UBERLÂNDIA-MG

The present article it is about an analysis of the use and occupation of land in the Basin Stream Vinhedo located in Uberlândia, Minas Gerais. To this end, it becomes necessary to provide planning tools that enable to analyze and evaluate the impact of human occupation on the banks of urban streams. The use of geo-technologies like Remote Sensing fits into this kind of work as an indispensable tool in urban planning, since these allow based on the use of satellite images and tools of digital mapping and characterization of watersheds in the country. In this perspective, were used in this work Quickbird satellite imagery of 2007 and Google images of 2010, as well as the tool of digital image processing Spring, in order to make a preliminary diagnosis of urbansprawlfor the period of 2010, Basin Stream Vinhedo, through the characterization of land cover and use, identifying the major biomes and types of construction sites, which ultimately helping to identify and/or prevent potential problems, both environmental and social character, and their possible causes, it helps in creating and implementing measures to prevent or resolve these problems.

Deforestation in Uyo Urban Area: A GIS and Remote Sensing Approach

This study measured and analyzed deforestation in Uyo and examined the possible effects of the deforestation on the Environment. When and where have forest lands changed in Uyo from 1969 – 2004? Aerial photographs taken from 1969 – 2001 which use was complemented with the Quick bird satellite imagery for 2004 were applied. The objectives of this study were to: interpret and digitize remote sensing data for Uyo for the periods 1969, 1978, 1988, 2001 and 2004; evaluate the deforestation changes that had taken place in Uyo over the periods of the study and to assess the effect of deforestation on the Uyo environment. Along the remote sensing technology, the geographical information system (GIS) technology was applied to carry out this research. Field studies were also carried out to arrive at appropriate results. Forestlands continually dropped in land area during the period while industrial lands, institutional, residential and transportation lands recorded increases in land areas. Reclassifying the land uses into built-up and forest lands revealed that the built-up lands constantly rose while the forest lands kept dropping. The built-up lands increased by 40.34% between 1969 and 1978, 5.63% between 1978 and 1988, and 111.45% between 1988 and 2001 and 0.16% between 2001 and 2004 at the expense of the forest portion of the town which fell by 34.08% between 1969 and 1978, 8.57% between 1978 and 1988, 47.34% between 1988 and 2001 and 0.17% between 2001 and 2004. Furthermore, between 1969/1978, the average annual rates of deforestation in Uyo Urban area were 3.79%, 0.86% between 1978/1988, 3.64% between 1988/2001 and 0.06% between 2001/2004. The largest quantity, percentage and average annual rates of loss of forest lands were made between 1988 and 2001. Increase in population, per capita income, and land use activities and by extension urban expansion were found to be the major factors causing deforestation in Uyo. The fieldwork revealed that deforestation has caused many environmental problems in Uyo like flooding, gully erosion, scarcity of agricultural land and poverty. Efforts being made to reduce the problems included conservation of the ravine area which was prone to gully erosion and other hazards, construction of the Nkemba Trough to drain most of the flood water from the area, importation of food from other Local Government areas and other States to supplement food supply in the area. The Government, people and non-governmental organizations (NGOs) in Akwa Ibom State should co-operate to assist in mitigating the deforestation in Uyo.Keywords: Deforestation, Reforestation, Environmental problems, Uyo.

Influence of land use on urban climate of a small town

This study aimed to quantify the influence of anthropogenic activity in changing the microclimate of the urban area of a small town. To this end, the temperature and relative humidity were monitored over six days using a direct-reading thermo-hygrometers placed in mini weather stations. The points where these instruments were installed were spatialized in geographic information system environment using Quickbird satellite imagery. It was defined areas of influence with 450 m radius from each sampling point, which were vectorized and classified according to urban land use. Analysis of Variance indicated a statistically significant difference in temperature and relative humidity between sites under different human activity levels. Later a linear correlation between the temperature and relative humidity with the anthropogenic levels, evidenced that anthropic areas are closely related to local climatic variation. The interference of green areas works inversely, increasing the definition of microclimate throughout the day. The effects of land use on the microclimate are cumulative, i.e, at the end of the day the correlation of the microclimate with land use reached a maximum.

Dinâmica do uso e cobertura da terra no campus da Universidade Federal de Lavras de 1964 a 2009

Neste trabalho, objetivou-se identificar, quantificar e analisar as mudanças ocorridas no campus da Universidade Federal de Lavras, município de Lavras, MG. Imagens do ano de 2009 do satélite QUICKBIRD e fotografias aéreas verticais dos anos de 1985, 1979, 1971 e 1964 foram usadas para produzir uma série de cartas de uso e cobertura da terra. O trabalho teve início com a ortorretificação da imagem de satélite QUICKBIRD e das fotografias aéreas. A identificação e a definição das classes de uso e cobertura da terra foram obtidas a partir de levantamentos de campo em 2009. A partir dessas informações, foram então construídos os mapas de uso e cobertura da terra. Por fim, foram feitas a quantificação e a análise das mudanças ocorridas no período estudado. Os resultados mostraram que: em 1964 a "área urbanizada" do campus que somava 6,24 ha, aumentou para 65,79 ha em 2009 e que o aumento mais expressivo dessa classe ocorreu entre os anos de 1964 (6,24 ha) e 1971 (24,4 ha). O menor valor de área de "vegetação natural" encontrado no campus foi de 38,38 ha em 1971, sendo que, a partir de 1979, essa situação foi alterada e em 2009 o campus apresentou 113,18 ha dessa categoria. Em relação à classe "água", observou-se que antes de 1971 não existia nenhuma represa no campus. A maior parte do campus, antes utilizada para "atividades agrícolas", teve uma redução significativa dessa categoria, de 384,19 ha em1964, para 271,16 ha em 2009.

Land Loss Due to Recent Hurricanes in Coastal Louisiana, U.S.A.

Palaneasu-Lovejoy, M.; Kranenburg, C.; Barras, J.A., and Brock, J.C., 2013. Land loss due to recent hurricanes in coastal Louisiana, U.S.A.. In: Brock, J.C.; Barras, J.A., and Williams, S.J. (eds.), Understanding and Predicting Change in the Coastal Ecosystems of the Northern Gulf of Mexico, Journal of Coastal Research, Special Issue No. 63, pp. 97–109, Coconut Creek (Florida), ISSN 0749-0208.The aim of this study is to improve estimates of wetland land loss in two study regions of coastal Louisiana, U.S.A., due to the extreme storms that impacted the region between 2004 and 2009. The estimates are based on change-detection-mapping analysis that incorporates pre and postlandfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional-water classifications using a combination of high-resolution (<5 m) QuickBird, IKONOS, and GeoEye-1, and medium-resolution (30 m) Landsat Thematic Mapper satellite imagery. This process was applied in two study areas: the Hackberry area located in the southwestern part of chenier plain that was impacted by Hurricanes Rita (September 24, 2005) and Ike (September 13, 2008), and the Delacroix area located in the eastern delta plain that was impacted by Hurricanes Katrina (August 29, 2005) and Gustav (September 1, 2008). In both areas, effects of the hurricanes include enlargement of existing bodies of open water and erosion of fringing marsh areas. Surge-removed marsh was easily identified in stable marshes but was difficult to identify in degraded or flooded marshes. Persistent land loss in the Hackberry area due to Hurricane Rita was approximately 5.8% and increased by an additional 7.9% due to Hurricane Ike, although this additional area may yet recover. About 80% of the Hackberry study area remained unchanged since 2003. In the Delacroix area, persistent land loss due to Hurricane Katrina measured approximately 4.9% of the study area, while Hurricane Gustav caused minimal impact of 0.6% land loss by November 2009. Continued recovery in this area may further erase Hurricane Gustav's impact in the absence of new storm events.

Estimating Basal Area of Spruce and Fir in Post-fire Residual Stands in Central Siberia Using Quickbird, Feature Selection, and Random Forests

The occurrence and spatial arrangement of post-fire alive residual stands affect the recolonization of trees and animals of burned areas in boreal ecosystems. Because the analysis of residual stands in the field is prohibitively expensive we lack understanding on how residual stands are distributed and why. Here, we explore the use of high-resolution Quickbird satellite imagery in conjunction with in-situ measurements and machine learning techniques to map basal area of spruce and fir for two fire areas in Central Siberia, and analyze the distribution of residual stands with respect to topography.First, an advanced feature selection algorithm which combines a genetic algorithm with guided local search is wrapped around the Random Forests regression technique, to identify suitable variable subsets out of a large number of candidate variables that were derived from Quickbird data. Second, we train and apply Random Forests using the derived variable subsets to the two fire areas to generate spatially explicit estimates of basal area for spruce and fir. Third, we analyze species specific differences and the relationship between basal area and topography using a high resolution digital elevation model from ASTER data.Our results show that the main gradients of species specific basal area can be reproduced using Quickbird data but stress the importance of variable selection. We find associations of residual stands with topography - depressions and channels exhibit larger prevalence of residual stands than ridges or plateaus, the latter being more often subject to severe fires. We further found that the relationship between basal area and elevation tends to be reversed inside the burned area in comparison to the surrounding unburned forest. Our results suggest that local topography may control the sensitivity of ecological processes to a changing fire regime with more severe fires, and highlight the synergistic use of high resolution satellite remote sensing and machine learning methods for fire ecological applications.

Mapping wild pear trees (Pyrus bourgaeana) in Mediterranean forest using high-resolution QuickBird satellite imagery

Recent advances in spatial and spectral resolution of satellite imagery as well as in processing techniques are opening new possibilities of fine-scale vegetation analysis with interesting applications in natural resource management. Here we present the main results of a study carried out in Sierra Morena, Cordoba (southern Spain), aimed at assessing the potential of remote-sensing techniques to discriminate and map individual wild pear trees (Pyrus bourgaeana) in Mediterranean open woodland dominated by Quercus ilex. We used high spatial resolution (2.4 m multispectral/0.6 m panchromatic) QuickBird satellite imagery obtained during the summer of 2008. Given the size and features of wild pear tree crowns, we applied an atmospheric correction method, Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and six different fusion ‘pan-sharpening’ methods (wavelet ‘à trous’ weighted transform, colour normalized (CN), Gram–Schmidt (GS), hue–saturation–intensity (HSI) colour transformation, multidirection–multiresolution (MDMR), and principal component (PC)), to determine which procedure provides the best results. Finally, we assessed the potential of supervised classification techniques (maximum likelihood) to discriminate and map individual wild pear trees scattered over the Mediterranean open woodland.

Using Satellite Imagery to Assess Macrophyte Response to Water-level Manipulations in the Saskatchewan River Delta, Manitoba

QuickBird satellite imagery and object-based classification were used to map emergent macrophyte response in wetlands that were partially drawn down during a three-year period (2007–2010) in the Saskatchewan River Delta, Manitoba, Canada. Generalized logistic models were used to classify vegetation genera in three drawdown and three high-water (control) wetlands. User accuracy of models averaged 74 %, and was lower in partial drawdown wetlands (72 %) than in high-water wetlands (77 %). In partial drawdown wetlands, emergent macrophytes (especially Typha spp. and Carex spp.) senesced significantly after the first year of lowered water levels but senescent areas decreased over following years. Plants were not completely dead in dewatered areas and emergent stands recovered by the third year after drawdown. Open water area in the partial drawdown wetlands gradually decreased due to clonal expansion of Sparganium spp. Vegetation areas in high-water wetlands remained unchanged throughout the study period. Although partial water-level drawdowns temporarily influenced the growth of emergent macrophytes they did not expose the seed bank and did not result in substantial, prolonged changes in spatial arrangement of vegetation areas. The combination of high-resolution satellite imagery, object-based classification and logistic modeling was useful for documenting vegetation response to management in these remote, boreal wetland systems.

Planejamento ambiental em uma unidade de conservação no município de Beberibe – Ceará

The object of this work is a Cliffs National Monument Beberibe, located on the beaches of Morro Branco and Fontes in Beberibe - Ceara. We chose the area of the Monument for study because of its geo-environmental units having a large landscape, showing the importance to the coast, since they are equipped with environmental weaknesses. The main objective is a proposal for environmental planning for the Monument and surrounding area through an environmental zoning. Cartography and Remote Sensing technical tools are used by the physical geography that allow a better interpretation and representation of the phenomena that affect the geographic space. These techniques has helped in areas of environmental planning as a way to better match the natural resources and human action. The work was based on methodological Theory geosystems. The techniques involved in data collection agencies, interpretation of Quickbird satellite imagery (2004), field surveys and preparation of thematic maps in MicroStation and ArcView software. As a result delimited to a buffer zone to the Monument including the strip of beach and backshore, dunes, lagoon and Tracua Tray Pre-coast. We proposed the creation of the APA and Praia das Fontes to finalize an environmental zoning for the area of the Monument covering its buffer zone, with the following areas: Intangible, Early, Extensive Use and Special Use, to BB from the beach sources, the areas proposed were: Environmental Preservation, Conservation, Environmental Occurrence, Environmental Restoration, Urban Occupation, Occupation Special Residential and Hotel Occupancy and Urban Expansion. This zoning will be discussed along with the sectors that are involved in the study area.

Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data. A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0–1,301 g∙C∙m−2∙yr−1 depending on vegetative conditions and management intensity. The average annual net primary production per unit turfgrass cover by golf course grass (1,100.5 g∙C∙m−2) was much higher than that by regular lawn grass (771.2 g∙C∙m−2). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability.

Support vector machines to map rare and endangered native plants in Pacific islands forests

It is critical to know accurately the ecological and geographic range of rare and endangered species for biodiversity conservation and management. In this study, we used support vector machines (SVM) for modeling rare species distribution and we compared it to another emerging machine learning classifier called random forests (RF). The comparison was performed using three native and endemic plants found at low- to mid-elevation in the island of Moorea (French Polynesia, South Pacific) and considered rare because of scarce occurrence records: Lepinia taitensis (28 observed occurrences), Pouteria tahitensis (20 occurrences) and Santalum insulare var. raiateense (81 occurrences). We selected a set of biophysical variables to describe plant habitats in tropical high volcanic islands, including topographic descriptors and an overstory vegetation map. The former were extracted from a digital elevation model (DEM) and the latter is a result of a SVM classification of spectral and textural bands from very high resolution Quickbird satellite imagery. Our results show that SVM slightly but constantly outperforms RF in predicting the distribution of rare species based on the kappa coefficient and the area under the curve (AUC) achieved by both classifiers. The predicted potential habitats of the three rare species are considerably wider than their currently observed distribution ranges. We hypothesize that the causes of this discrepancy are strong anthropogenic disturbances that have impacted low- to mid-elevation forests in the past and present. There is an urgent need to set up conservation strategies for the endangered plants found in these shrinking habitats on the Pacific islands.

Morphotectonic evolution of triangular facets and wine-glass valleys in the Noakoh anticline, Zagros, Iran: Implications for active tectonics

The Noakoh anticline is located in Kermanshah province and is part of the Simply Folded Belt of Zagros. Boundaries of 97 triangular facets and 67 wine-glass (W-G) valleys, which formed on anticline limbs, were delineated using Quickbird satellite imagery. The strata dip (D), area (A), base length (BL), topographic slope (S) of facets, the maximum width (M), outlet width (O) and ratio of maximum width to outlet width (W index) of W-G valleys were analysed in detail. Noakoh anticline was subdivided into 9 tectonic zones on the basis of dip, topographic slopes and width of limbs. Results show that there are strong positive correlations between means of D-BL and S-BL pairs. Poor positive correlations exist between means of D-A and S-A pairs. Among W-G valley metrics, the W index has strong relations with D and S parameters. Based on the results, steep facets with long bases and well developed W-G valleys with narrow outlets and wide upper parts are associated with more rotated limbs having steep slopes. Facets on the northeastern slope have more forest cover, micro-organism activity, karstic features and soil cover, whereas facets on relatively drier southwestern slope are characterized by physical weathering processes and minor karstic landforms. This study demonstrates that, apart from tectonic activity as a major control on the morphometry of facets and valleys, climate and slope aspect have also acted as secondary factors on the development of the studied landforms.

Urban Expansion and Loss of Agricultural Land in Uyo Urban Area: Implications for Agricultural Business

Urban expansion in Uyo Urban Area was analyzed using aerial photographs taken from 1969 &ndash; 2001 which use was complemented with the Quick bird satellite imagery for 2004. The objectives of this study were to evaluate the urban expansion that had taken place in Uyo Urban Area over the periods of the study and to assess its influence on agricultural lands. The geographical information system (GIS) technology and field studies were applied to carry out this research. The analysis showed that Agricultural lands continually dropped in land area during the period while the built-up lands constantly rose. The built-up lands increased by 40.34% between 1969 and 1978, 5.63% between 1978 and 1988, and 111.45% between 1988 and 2001 and 0.16% between 2001 and 2004 at the expense of the agricultural land which fell by 0.33% between 1969 and 1978, 2.24% between 1978 and 1988, 58.03% between 1988 and 2001 and 4.26% between 2001 and 2004. Furthermore, between 1969/1978, the average annual rates of urban growth in Uyo Urban area were 4.48%, 0.56% between 1978/1988, 8.57% between 1988/2001 and 0.05% between 2001/2004 while the average annual rates of loss of agricultural lands were 0.04%, 0.22%, 4.46% and 1.42%. The largest quantity, percentage and average annual rates of gains or losses of land by the urban and the agricultural areas were made between 1988 and 2001. The loss of agricultural land brought in hunger and poverty in Uyo and boosted agricultural business in the neighbouring states.Keywords: Urban, Expansion, urban Expansion, Agricultural Land, Home Garden, Agricultural Business