Urban expansion using remote-sensing data and a monocentric urban model

The expansion of urban land use is an indispensable tool of urban policy, particularly in developing countries which are facing rapid urbanization. However, most sophisticated spatial-analysis methods used in developed countries are not applicable to developing countries due to the limited availability of spatially-resolved statistical data. This study examines the applicability of a simple, classic monocentric urban model for capturing urban activities in three Chinese cities in 2003 and 2013. The model estimates traffic cost, goods consumption, area of available residential floor space, and rents for residential floor space by aggregating urban statistics and estimating urban areas using remote sensing data. We find that some predictions of our model are inaccurate due to its simple, static nature; however, the model predicts meaningful temporal trends in key variables and meaningful differences among the population and economic growth trajectories of the three cities. Our results imply that the classic monocentric model is useful for approximating urban activities and for strategic planning of urban traffic policies, as it allows tracing of the causal mechanisms of urban activity and furnishes estimates for several critical statistical measures needed for policy evaluation. Although a monocentric model may not allow detailed representation of urban activity, we show that our simple model nonetheless offers advantages for studying urban planning practices in developing nations in which the availability of statistical data is limited.     

A predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km² (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km²; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D ², adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands.     

The relationship of surface air temperature,heat balance at the surface,and radiative balance at the top of atmosphere over the Asian territory of Russia using reanalysis and remote-sensing data

In this study over the Asian territory of Russia (ATR) (45° N–80° N, 60° E–180° E) for the period of 1979–2010 the temporal variability of the surface air temperature field was investigated. There are several climatic factors which can influence temperature variability including radiative balance at the top of atmosphere (TOA), heat balance at the surface, total cloud cover, and large-scale atmospheric circulation in the Northern Hemisphere. The contribution of these factors to temperature variability is also investigated. It was found that during the past decade, over the ATR, the process of warming prevails mainly in the warm season, but in the cold season it is either not as marked or there is cooling instead. In the winter season there is a positive relationship between temperature anomalies and anomalies in cloudiness, effective radiation, and the Arctic Oscillation (AO) index. During the same period, a negative relationship between anomalies of temperature and anomalies of net radiation at the TOA, net shortwave radiation at the surface, and the Scandinavian (SCAND) index was observed. In the summer season, the relationship between temperature and cloudiness becomes negative and the relationship between temperature and atmospheric circulation indices decreases. For the period 2001–2010, radiative fluxes obtained from reanalysis data sets Japanese Reanalysis Data (JRA-25) and Modern Era-Retrospective Analysis for Research and Applications (MERRA) were compared to satellite remotely sensed data project Clouds and the Earth’s Radiant Energy System (CERES). It was found that there is a good agreement between estimates of the net radiation at the TOA calculated using reanalysis data and satellite data: the difference is about 1.5 W m^?2 and the correlation coefficient is more than 0.7. As for the comparison of radiative fluxes estimates at the surface for clear sky, there is less difference between MERRA and CERES. So, during the period 2001–2010 the relation between atmospheric circulation and surface air temperature variability increased in winter months. Obtained regression models allow us to describe from 27% to 82% of temperature variability in different months if we take into account both circulation and radiative factors.     

Fine-resolution remote-sensing and modelling of Himalayan catchment sustainability

A number of studies have reported on environmental degradation in the Nepal Himalaya as a result of large-scale deforestation and the associated agricultural extension. In contrast to many previous regional scale studies, we consider land cover and its environmental impact on an individual catchment-scale, using fine-resolution Quickbird data and a soil erosion model. First, using a detailed land cover map generated from Quickbird imagery, we establish basic relationships between land cover, dwelling density and topographic variability, which exist in a typical mid-elevation Nepalese catchment, the Pokhare Khola. These data suggest a strongly subsistence type of household economy based predominantly on terraced arable farming. We demonstrate using multitemporal vegetation indices that this farmland has existed in the region since the late 1980s, and that widespread deforestation has not taken place since then, possibly as a result of specific forest conservation policies of the government coupled with efforts by local communities. Further, through the use of soil erosion modelling we demonstrate the role that the terraced farming practices can play in reducing runoff and hence soil nutrient loss, thereby enabling restoration of vegetation in the previously deforested land terrains. Finally, by combining this information with regional land cover data, we show that the findings of this research can be scaled up to draw conclusions about environmental degradation across the Nepal Himalayan region.     

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover.

With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode.

ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commission's Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed.

Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.     

Relaxation vegetation index in non-linear modelling of ground plant cover by satellite remote-sensing data

Abstract

In order to obtain a model equation for the calculation of percentage plant cover by multi-spectral radiances remotely-sensed by satellites, a regression procedure is used to connect space remote-sensing data to ground plant cover measurement. A traditional linear regression model using the normalized difference vegetation index (NDVI) is examined by remote-sensing data of the SPOT satellite and ground measurement of LCTA project for a test site at Hohenfels. Germany. A relaxation vegetation index (RVI) is proposed in a non-linear regression modelling to replace the NDVI in linear regression modelling to get a better calculation of percentage plant cover. The definition of the RVI is

where X _i is raw remote-sensing data in channel i. Using the RVI, the correlation coefficient between calculated and observed percentage plant cover for a test scene in 1989 reaches 0·9 while for the NDVI it is only 0·7; the coefficient of multiple determination R ² reaches 0·8 for the RVI while it is only 0·5 for the NDVI. Numerical testing shows that the ability of using the RVI to predict percentage plant cover by space remote-sensing data for the same scene or the scene in other years is much stronger than the NDVI.     

Solid modelling using a volume encoding data structure

A strong emphasis has lately been put in research to develop methods for Solid Modelling using surface or volume encoding data structures instead of conventional edge-face-vertex or polyhedral representations for 3-D objects.This article describes a Solid Modelling system using a volume encoding data structure called OCTREE which divides a solid into cubes.     

Semantic data modelling and visualisation using Noetica

Noetica is a tool for structuring knowledge about concepts and the relationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalised structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in “free” semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics.

Visualisation and query tools are provided for this data model. Visualisation can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualise the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).     

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.     

A demonstration of pharmacokinetics and physiological modelling using a microcomputer for data capture and analysis

We describe a simple and inexpensive demonstration of mass transport and exchange using dye clearance from a hydrodynamic model. A microcomputer was used for data acquisition and storage, non-linear least squares curve fitting, compartmental analysis and parameter estimation. The system is useful for demonstrating the indicator-dilution technique for fluid volume measurement and compartmental analysis in pharmacokinetics.     

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R² = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.     

Spatiotemporal variability of snow cover on Tibet,China using MODIS remote-sensing data

ABSTRACT

Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.     

A generalized search scheme for automatic registration of remote-sensing data

A generalized search scheme for automatic registration of level-1 data of multiresolution and multi-sensor remote-sensing data was proposed. The robustness and time efficiency of the automatic registration process is critically dependant on the search scheme for identification of control points. The proposed method consists of three levels of search ranging from coarser to finer. This process was found to be capable of registering images to sub-pixel level which are independent of rotation and scale variations, and also translation that vary by few metres to kilometres. In order to reduce the low pass effect due to multiple transformations involved in the multi-level registration process, a direct correspondence between the reference image and target image was established so that a single resampling needs to be applied. This correspondence also helps to generate products at any desired pixel size and to keep the original resolution intact. In this scheme mutual information (MI) is used as a similarity measure and a non-rigid transformation, thin plate spline (TPS), is used for achieving sub-pixel registration accuracies. MI is found to be better for identification of match points even for images that are radiometrically nonlinear. Unlike global transformation methods, use of non-rigid transformations such as TPS achieves sub-pixel accuracy in the moderate hilly regions as well as high hilly regions where relief displacements are high, provided sufficient number of control points are generated. However TPS transformation demands accurate control points. A robust method for detection of inaccurate control points was adopted and explained in the paper. The scheme was tested on a number of combinations of remote sensing data of the same resolution and different resolution datasets, namely Cartosat-1 with Liss-4 of Resourcesat-1, Landsat Thematic Mapper (TM) with IRS-1C/1D and Cartosat-1 with Enhanced Thematic Mapper (ETM). The results are presented along with accuracies achieved.     

Multiscale comparison of remote-sensing data for linear woody vegetation mapping

Wooded hedgerows do not cover large areas but perform many functions that are beneficial to water quality and biodiversity. A broad range of remotely sensed data is available to map these small linear elements in rural landscapes, but only a few of them have been evaluated for this purpose. In this study, we evaluate and compare various optical remote-sensing data including high and very high spatial resolution, active and passive, and airborne and satellite data to produce quantitative information on the hedgerow network structure and to analyse qualitative information from the maps produced in order to estimate the true value of these maps. We used an object-based image analysis that proved to be efficient for detecting and mapping thin elements in complex landscapes. The analysis was performed at two scales, the hedgerow network scale and the tree canopy scale, on a study site that shows a strong landscape gradient of wooded hedgerow density. The results (1) highlight the key role of spectral resolution on the detection and mapping of wooded elements with remotely sensed data; (2) underline the fact that every satellite image provides relevant information on wooded network structures, even in closed landscape units, whatever the spatial resolution; and (3) indicate that light detection and ranging data offer important insights into future strategies for monitoring hedgerows.     

HiTempo: a platform for time-series analysis of remote-sensing satellite data in a high-performance computing environment

Course resolution earth observation satellites offer large data sets with daily observations at global scales. These data sets represent a rich resource that, because of the high acquisition rate, allows the application of time-series analysis methods. To research the application of these time-series analysis methods to large data sets, it is necessary to turn to high-performance computing (HPC) resources and software designs. This article presents an overview of the development of the HiTempo platform, which was designed to facilitate research into time-series analysis of hyper-temporal sequences of satellite image data. The platform is designed to facilitate the exhaustive evaluation and comparison of algorithms, while ensuring that experiments are reproducible. Early results obtained using applications built within the platform are presented. A sample model-based change detection algorithm based on the extended Kalman filter has been shown to achieve a 97% detection success rate on simulated data sets constructed from MODIS time series. This algorithm has also been parallelized to illustrate that an entire sequence of MODIS tiles (415 tiles over 9 years) can be processed in under 19 minutes using 32 processors.     

Field-based remote-sensing for experimental intertidal ecology: Case studies using hyperspatial and hyperspectral data for New South Wales (Australia)

Many ecological processes in intertidal areas act at very small (mm to cm) spatial scales so manipulative ecological experiments are done at scales at which these processes are proposed to occur. Remote-sensing using field-based sensors enable ecologists to capture variability that must be measured to test hypotheses about small-scale processes.We present two case studies where remote-sensing from field-based platforms has been used to acquire data for manipulative ecological experiments in intertidal areas. The first study, from a rock platform, investigates the spatial scales of variability in chlorophyll (as an index of algal biomass) in experimental plots prior to and after the removal of encrusting macro-algae. Digital colour-infrared (CIR) imagery of the rock platform was obtained before the removal of the encrusting macro-algae and on 1, 44, 75, 117 and 160 days after removal. Amount of chlorophyll was derived from the CIR data using a simple ratio of reflectances at near-infrared (NIR) and red wavelengths. Block Mean Square (BMS) analysis showed that there was little variability in amounts of chlorophyll at very small (≤ 2.26 cm²) scales, prior to and after removal of encrusting macro-algae. The greatest variability occurred at the largest block-size analysed (36.19 cm²). This identifies the spatial scale at which micro-algal food resources are variably scattered on the shore. Results discriminate variability at about 60–80 mm distances. Distributions of micro-algae are caused by a complicated interplay of scale-dependent ecological processes, including grazing. These findings provide a basis for the design of manipulative experiments at appropriate scales to unravel these processes.The second study used field reflectance spectra (350–1050 nm) to: i) determine whether the amount of algae in soft sediments could be changed by manipulating the amounts of light reaching the sediment surface and ii) test the hypothesis that the strength of in vivo absorption features, as indices of the amount of chlorophyll and other pigments, was correlated with measures of sediment-stability (erosion threshold and erosion rate). Several different measures of the strength of absorption features were investigated to determine the best predictor of stability of the sediment. Reflectance spectra showed that amounts of algae had been successfully manipulated. Derivative reflectance at individual wavelengths, indicative of absorption by chlorophyll-a and chlorophyll-c, were the best predictors of sediment-stability.These case studies demonstrate some novel uses of field-based remote-sensing to provide information at the spatial scales relevant to many intertidal ecologists. Such techniques should have an increasingly important role in ecological field experimentation where non-destructive sampling of photosynthetic pigments is desired.     

Landscape modelling using integrated airborne multi-spectral and laser scanning data

By integrating multi-spectral and elevation data from airborne sensors (CASI and ALTM) and adopting a parcel-based approach, a progression is achieved from land-cover classification to landscape modelling. This work involved data integration, per-parcel classification, knowledge-based correction and the derivation of landscape objects. For a 1 km 2 study area, a 14 land-cover class vector dataset was generated in which the parcels relate to landscape objects and contain information on their structure and 'terrain' context. At a 1 m spatial resolution, the correspondence between land-cover mapped using the airborne sensor data and identified by Countryside Survey 2000 field surveyors was 88%.