Showing papers in "Remote Sensing in 2011"

Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning

Abstract: The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper.

Remote Sensing of Mangrove Ecosystems: A Review

Abstract: Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations.

Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong

Abstract: In this paper, the effect of urban heat island is analyzed using the Landsat TM data and ASTER data in 2005 as a case study in Hong Kong. Two algorithms were applied to retrieve the land surface temperature (LST) distribution from the Landsat TM and ASTER data. The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island by calculating the ecological evaluation index of sub-urban areas. The results indicate that the effect of urban heat island in Hong Kong is mainly located in three sub-urban areas, namely, Kowloon Island, the northern Hong Kong Island and Hong Kong International Airport. The correlation between LST and NDVI, NDBI also indicates that the negative correlation of LST and NDVI suggests that the green land can weaken the effect on urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM and ASTER thermal bands data) can be applied to examine the distribution of urban heat islands in places such as Hong Kong, the method still needs to be refined with in situ measurements of LST in future studies.

Sky-View Factor as a Relief Visualization Technique

Abstract: Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor—a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs.

Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

Abstract: Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R 2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.

An Object-Based Classification of Mangroves Using a Hybrid Decision Tree—Support Vector Machine Approach

Abstract: Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA), and support vector machine (SVM) classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863) for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.

Analysis of Incidence Angle and Distance Effects on Terrestrial Laser Scanner Intensity: Search for Correction Methods

Abstract: The intensity information from terrestrial laser scanners (TLS) has become an important object of study in recent years, and there are an increasing number of applications that would benefit from the addition of calibrated intensity data to the topographic information. In this paper, we study the range and incidence angle effects on the intensity measurements and search for practical correction methods for different TLS instruments and targets. We find that the range (distance) effect is strongly dominated by instrumental factors, whereas the incidence angle effect is mainly caused by the target surface properties. Correction for both effects is possible, but more studies are needed for physical interpretation and more efficient use of intensity data for target characterization.

Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of the Orbiting Carbon Observatory-2 Mission

Abstract: The global characteristics of retrievals of the column-averaged CO2 dry air mole fraction, XCO2, from shortwave infrared observations has been studied using the expected measurement performance of the NASA Orbiting Carbon Observatory-2 (OCO-2) mission. This study focuses on XCO2 retrieval precision and averaging kernels and their sensitivity to key parameters such as solar zenith angle (SZA), surface pressure, surface type and aerosol optical depth (AOD), for both nadir and sunglint observing modes. Realistic simulations have been carried out and the single sounding retrieval errors for XCO2 have been derived from the formal retrieval error covariance matrix under the assumption that the retrieval has converged to the correct answer and that the forward model can adequately describe the measurement. Thus, the retrieval errors presented in this study represent an estimate of the retrieval precision. For nadir observations, we find single-sounding retrieval errors with values typically less than 1 part per million (ppm) over most land surfaces for SZAs less than 70° and up to 2.5 ppm for larger SZAs. Larger errors are found over snow/ice and ocean surfaces due to their low albedo in the spectral regions of the CO2 absorption bands and, for ocean, also in the O2 A band. For sunglint observations, errors over the ocean are significantly smaller than in nadir mode with values in the range of 0.3 to 0.6 ppm for small SZAs which can decrease to values as small as 0.15 for the largest SZAs. The vertical sensitivity of the retrieval that is represented by the column averaging kernel peaks near the surface and exhibits values near unity throughout most of the troposphere for most anticipated scenes. Nadir observations over dark ocean or snow/ice surfaces and observations with large AOD and large SZA show a decreased sensitivity to near-surface CO2. All simulations are carried out for a mid-latitude summer atmospheric profile, a given aerosol type and vertical distribution, a constant windspeed for ocean sunglint and by excluding the presence of thin cirrus clouds. The impact of these parameters on averaging kernels and XCO2 retrieval errors are studied with sensitivity studies. Systematic biases in retrieved XCO2, as can be introduced by uncertainties in the spectroscopic parameters, instrument calibration or deficiencies in the retrieval algorithm itself, are not included in this study. The presented error estimates will therefore only describe the true retrieval errors once systematic biases are eliminated. It is expected that it will be possible to retrieve XCO2 for cloud free observations and for low AOD (here less than 0.3 for the wavelength region of the O2 A band) with sufficient accuracy for improving CO2 surface flux estimates and we find that on average 18% to 21% of all observations are sufficiently cloud-free with only few areas suffering from the presence of persistent clouds or high AOD. This results typically in tens of useful observations per 16 day ground track repeat cycle at a 1° × 1° resolution. Averaging observations acquired along ~1° intervals for individual ground tracks will significantly reduce the random component of the errors of the XCO2 average product for ingestion into data assimilation/inverse models. If biases in the XCO2 retrieval of the order of a few tenth ppm can be successfully removed by validation or by bias-correction in the flux inversion, then it can be expected that OCO-2 XCO2 data can lead to tremendous improvements in estimates of CO2 surface-atmosphere fluxes.

Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest

Abstract: We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile- or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments.

Toronto’s Urban Heat Island—Exploring the Relationship between Land Use and Surface Temperature

Abstract: The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations of “hot” land uses exacerbate this relationship. Zonal statistics on a thermal remote sensing image for the City of Toronto revealed statistically significant differences between high average temperatures for commercial and resource/industrial land use (29.1 °C), and low average temperatures for parks and recreational land (25.1 °C) and water bodies (23.1 °C). Furthermore, higher concentrations of either of these land uses were associated with more extreme surface temperatures. We also present selected neighborhoods to illustrate these results. The paper concludes by recommending that municipal planners and decision-makers formulate policies and regulations that are specific to the problematic land uses, in order to mitigate extreme heat.

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

Abstract: Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.

The HelioClim Project: Surface Solar Irradiance Data for Climate Applications

Abstract: Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of SSI. The HC-1 database spans from 1985 to 2005; HC-3 began in 2004 and is updated daily. Their quality and limitations in retrieving monthly means of SSI have been studied by a comparison between eleven stations offering long time-series of measurements. A good agreement was observed for each site: bias was less than 10 W/m² in absolute value (5% in relative value) for HC-3. HC-1 offers a similar quality, though it underestimates the SSI for latitudes greater than 45° and less than −45°. Time-series running from 1985 to date can be created by concatenating the HC-1 and HC-3 values and could help in assessing SSI and its changes. Keywords: solar radiation; solar irradiance; solar exposure; climate; Africa; Europe; Atlantic Ocean; remote sensing; long-term analysis; Meteosat

Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007

Abstract: Shallow coastal ecosystems are the interface between the terrestrial and marine environment. The physical and biological composition and distribution of benthic habitats within these ecosystems determines their contribution to ecosystem services and biodiversity as well as their connections to neighbouring terrestrial and marine ecosystem processes. The capacity to accurately and consistently map and monitor these benthic habitats is critical to developing and implementing management applications. This paper presents a method for integrating field survey data and high spatial resolution, multi-spectral satellite image data to map bathymetry and seagrass in shallow coastal waters. Using Quickbird 2 satellite images from 2004 and 2007, acoustic field survey data were used to map bathymetry using a linear and ratio algorithm method; benthic survey field data were used to calibrate and validate classifications of seagrass percentage cover and seagrass species composition; and a change detection analysis of seagrass cover was performed. The bathymetry mapping showed that only the linear algorithm could effectively and accurately predict water depth; overall benthic map accuracies ranged from 57–95%; and the change detection produced a reliable change map and showed a net decrease in seagrass cover levels, but the majority of the study area showed no change in seagrass cover level. This study demonstrates that multiple spatial products (bathymetry, seagrass and change maps) can be produced from single satellite images and a concurrent field survey dataset. Moreover, the products were produced at higher spatial resolution and accuracy levels than previous studies in Moreton Bay. The methods are developed from previous work in the study area and are continuing to be implemented, as well as being developed to be repeatable in similar shallow coastal water environments.

The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images

Abstract: The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome.

Urban Sprawl Analysis and Modeling in Asmara, Eritrea

Abstract: The extension of urban perimeter markedly cuts available productive land. Hence, studies in urban sprawl analysis and modeling play an important role to ensure sustainable urban development. The urbanization pattern of the Greater Asmara Area (GAA), the capital of Eritrea, was studied. Satellite images and geospatial tools were employed to analyze the spatiotemporal urban landuse changes. Object-Based Image Analysis (OBIA), Landuse Cover Change (LUCC) analysis and urban sprawl analysis using Shannon Entropy were carried out. The Land Change Modeler (LCM) was used to develop a model of urban growth. The Multi-layer Perceptron Neural Network was employed to model the transition potential maps with an accuracy of 85.9% and these were used as an input for the ‘actual’ urban modeling with Markov chains. Model validation was assessed and a scenario of urban land use change of the GAA up to year 2020 was presented. The result of the study indicated that the built-up area has tripled in size (increased by 4,441 ha) between 1989 and 2009. Specially, after year 2000 urban sprawl in GAA caused large scale encroachment on high potential agricultural lands and plantation cover. The scenario for year 2020 shows an increase of the built-up areas by 1,484 ha (25%) which may cause further loss. The study indicated that the land allocation system in the GAA overrode the landuse plan, which caused the loss of agricultural land and plantation cover. The recommended policy options might support decision makers to resolve further loss of agricultural land and plantation cover and to achieve sustainable urban development planning in the GAA.

Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements

Abstract: Light Detection and Ranging (LiDAR) remote sensing has demonstrated potential in measuring forest biomass. We assessed the ability of LiDAR to accurately estimate forest total above ground biomass (TAGB) on an individual stem basis in a conifer forest in the US Pacific Northwest region using three different computer software programs and compared results to field measurements. Software programs included FUSION, TreeVaW, and watershed segmentation. To assess the accuracy of LiDAR TAGB estimation, stem counts and heights were analyzed. Differences between actual tree locations and LiDAR-derived tree locations using FUSION, TreeVaW, and watershed segmentation were 2.05 m (SD 1.67), 2.19 m (SD 1.83), and 2.31 m (SD 1.94), respectively, in forested plots. Tree height differences from field measured heights for FUSION, TreeVaW, and watershed segmentation were −0.09 m (SD 2.43), 0.28 m (SD 1.86), and 0.22 m (2.45) in forested plots; and 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively, in a plot containing young conifers. The TAGB comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall, LiDAR TAGB estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR TAGB underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons.

Roads as drivers of change: Trajectories across the Tri-National Frontier in MAP, the Southwestern Amazon

Abstract: Regional studies of land cover change are often limited by available data and in terms of comparability across regions, by the transferability of methods. This research addresses the role of roads and infrastructure improvements across a tri-national frontier region with similar climatic and biophysical conditions but very different trajectories of forest clearing. The standardization of methodologies and the extensive spatial and temporal framework of the analysis are exciting as they allow us to monitor a dynamic region with global significance as it enters an era of increased road connectivity and massive potential forest loss. Our study region is the MAP frontier, which covers Madre de Dios in Peru, Acre in Brazil, and Pando in Bolivia. This tri-national frontier is being integrated into the global economy via the paving of the Inter-Oceanic Highway which links the region to ports in the Atlantic and Pacific, constituting a major infrastructure change within just the last decade. Notably, there are differences in the extent of road paving among the three sides of the tri-national frontier, with paving complete in Acre, underway in Madre de Dios, and incipient in Pando. Through a multi-temporal analysis of land cover in the MAP region from 1986 to 2005, we found that rates of deforestation differ across the MAP frontier, with higher rates in Acre, followed by Madre de Dios and the lowest rates in Pando, although the dominant land cover across the region is still stable forest cover (89% overall). For all dates in the study period, deforestation rates drop with distance from major roads although the distance before this drop off appears to relate to development, with Acre influencing forests up to around 45 km out, Madre de Dios to about 18 km out and less of a discernable effect or distance value in Pando. As development occurs, the converted forest areas saturate close to roads, resulting in increasing rates of deforestation at further distances and patch consolidation of clearings over time. We can use this trend as a basis for future change predictions, with Acre providing a guide to likely future development for Madre de Dios, and in time potentially for Pando. Given the correspondence of road paving to deforestation, our findings imply that as road paving increases connectivity, flows of people and goods will accelerate across this landscape, increasing the likelihood of dramatic future changes on all sides of the tri-national frontier

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

Abstract: The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.

SAR-Based Wind Resource Statistics in the Baltic Sea

Abstract: Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of wind speed from SAR wind maps and from 10 meteorological masts, established specifically for wind energy in the study area, are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a root mean square error of 1.17 m s−1, bias of −0.25 m s−1, standard deviation of 1.88 m s−1 and correlation coefficient of R2 0.783. Wind directions from a global atmospheric model, interpolated in time and space, are used as input to the geophysical model function CMOD-5 for SAR wind retrieval. Wind directions compared to mast observations show a root mean square error of 6.29° with a bias of 7.75°, standard deviation of 20.11° and R2 of 0.950. The scale and shape parameters, A and k, respectively, from the Weibull probability density function are compared at only one available mast and the results deviate ~2% for A but ~16% for k. Maps of A and k, and wind power density based on more than 1000 satellite images show wind power density values to range from 300 to 800 W m−2 for the 14 existing and 42 planned wind farms.

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

Abstract: In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification

Abstract: The objective of this study is to compare WorldView-2 (WV-2) and QuickBird-2-simulated (QB-2) imagery regarding their potential for object-based urban land cover classification. Optimal segmentation parameters were automatically found for each data set and the obtained results were quantitatively compared and discussed. Four different feature selection algorithms were used in order to verify to which data set the most relevant object-based features belong to. Object-based classifications were performed with four different supervised algorithms applied to each data set and the obtained accuracies and model performances indexes were compared. Segmentation experiments carried out involving bands exclusively available in the WV-2 sensor generated segments slightly more similar to our reference segments (only about 0.23 discrepancy). Fifty seven percent of the different selected features and 53% of all the 80 selections refer to features that can only be calculated with the additional bands of the WV-2 sensor. On the other hand, 57% of the most relevant features and 63% of the second most relevant features can also be calculated considering only the QB-2 bands. In 10 out of 16 classifications, higher Kappa values were achieved when features related to the additional bands of the WV-2 sensor were also considered. In most cases, classifications carried out with the 8-band-related features generated less complex and more efficient models than those generated only with QB-2 band-related features. Our results lead to the conclusion that spectrally similar classes like ceramic tile roofs and bare soil, as well as asphalt and dark asbestos roofs can be better distinguished when the additional bands of the WV-2 sensor are used throughout the object-based classification process.

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

Abstract: Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

LIDAR and SODAR Measurements of Wind Speed and Direction in Upland Terrain for Wind Energy Purposes

Abstract: Detailed knowledge of the wind resource is necessary in the developmental and operational stages of a wind farm site. As wind turbines continue to grow in size, masts for mounting cup anemometers—the accepted standard for resource assessment—have necessarily become much taller, and much more expensive. This limitation has driven the commercialization of two remote sensing (RS) tools for the wind energy industry: The LIDAR and the SODAR, Doppler effect instruments using light and sound, respectively. They are ground-based and can work over hundreds of meters, sufficient for the tallest turbines in, or planned for, production. This study compares wind measurements from two commercial RS instruments against an instrumented mast, in upland (semi-complex) terrain typical of where many wind farms are now being installed worldwide. With appropriate filtering, regression analyses suggest a good correlation between the RS instruments and mast instruments: The RS instruments generally recorded lower wind speeds than the cup anemometers, with the LIDAR more accurate and the SODAR more precise.

Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data

Abstract: Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC.

Mapping Infrared Data on Terrestrial Laser Scanning 3D Models of Buildings

Abstract: A new 3D acquisition and processing procedure to map RGB, thermal IR and near infrared images (NIR) on a detailed 3D model of a building is presented. The combination and fusion of different data sources allows the generation of 3D thermal data useful for different purposes such as localization, visualization, and analysis of anomalies in contemporary architecture. The classic approach, which is currently used to map IR images on 3D models, is based on the direct registration of each single image by using space resection or homography. This approach is largely time consuming and in many cases suffers from poor object texture. To overcome these drawbacks, a “bi-camera” system coupling a thermal IR camera to a RGB camera has been setup. The second sensor is used to orient the “bi-camera” through a photogrammetric network also including free-handled camera stations to strengthen the block geometry. In many cases the bundle adjustment can be executed through a procedure for automatic extraction of tie points. Terrestrial laser scanning is adopted to retrieve the 3D model building. The integration of a low-cost NIR camera accumulates further radiometric information on the final 3D model. The use of such a sensor has not been exploited until now to assess the conservation state of buildings. Here some interesting findings from this kind of analysis are reported. The paper shows the methodology and its experimental application to a couple of buildings in the main Campus of Politecnico di Milano University, where IR thermography has previously been carried out for conservation and maintenance purposes.

Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner

Abstract: Laser measurements have been used in a fluvial context since 1984, but the change detection possibilities of mobile laser scanning (MLS) for riverine topography have been lacking. This paper demonstrates the capability of MLS in erosion change mapping on a test site located in a 58 km-long tributary of the River Tenojoki (Tana) in the sub-arctic. We used point bars and river banks as example cases, which were measured with the mobile laser scanner ROAMER mounted on a boat and on a cart. Static terrestrial laser scanner data were used as reference and we exploited a difference elevation model technique for describing erosion and deposition areas. The measurements were based on data acquisitions during the late summer in 2008 and 2009. The coefficient of determination (R2) of 0.93 and a standard deviation of error 3.4 cm were obtained as metrics for change mapping based on MLS. The root mean square error (RMSE) of MLS‑based digital elevation models (DEM) for non-vegetated point bars ranged between 2.3 and 7.6 cm after correction of the systematic error. For densely vegetated bank areas, the ground point determination was more difficult resulting in an RMSE between 15.7 and 28.4 cm.

Spatial and Temporal Land Cover Changes in the Simen Mountains National Park, a World Heritage Site in Northwestern Ethiopia

Abstract: The trend of land cover (LC) and land cover change (LCC), both in time and space, was investigated at the Simen Mountains National Park (SMNP), a World Heritage Site located in northern Ethiopia, between 1984 and 2003 using Geographical Information System (GIS) and remote sensing (RS). The objective of the study was to generate spatially and temporally quantified information on land cover dynamics, providing the basis for policy/decision makers and resource managers to facilitate biodiversity conservation, including wild animals. Two satellite images (Landsat TM of 1984 and Landsat ETM+ of 2003) were acquired and supervised classification was used to categorize LC types. Ground Control Points were obtained in field condition for georeferencing and accuracy assessment. The results showed an increase in the areas of pure forest (Erica species dominated) and shrubland but a decrease in the area of agricultural land over the 20 years. The overall accuracy and the Kappa value of classification results were 88 and 85%, respectively. The spatial setting of the LC classes was heterogeneous and resulted from the biophysical nature of SMNP and anthropogenic activities. Further studies are suggested to evaluate the existing LC and LCC in connection with wildlife habitat, conservation and management of SMNP.

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Abstract: Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.

Terrestrial Laser Scanner Resolution: Numerical Simulations and Experiments on Spatial Sampling Optimization

Abstract: An empirical approach is proposed in order to evaluate the largest spot spacing allowing the appropriate resolution to recognize the required surface details in a terrestrial laser scanner (TLS) survey. The suitable combination of laser beam divergence and spot spacing for the effective scanning angular resolution has been studied by numerical simulation experiments with an artificial target taken from distances between 25 m and 100 m, and observations of real surfaces. The tests have been performed by using the Optech ILRIS-3D instrument. Results show that the discrimination of elements smaller than a third of the beam divergence (D) is not possible and that the ratio between the used spot-spacing (ss) and the element size (TS) is linearly related to the acquisition range. The zero and first order parameters of this linear trend are computed and used to solve for the maximum efficient ss at defined ranges for a defined TS. Despite the fact that the parameters are obtained for the Optech ILRIS-3D scanner case, and depend on its specific technical data and performances, the proposed method has general validity and it can be used to estimate the corresponding parameters for other instruments. The obtained results allow the optimization of a TLS survey in terms of acquisition time and surface details recognition.

Remote Sensing Images in Support of Environmental Protocol: Monitoring the Sugarcane Harvest in São Paulo State, Brazil

Abstract: Traditional manual sugarcane harvesting requires the pre-harvest burning practice which should be gradually banned by 2021 for most of Sao Paulo State, Brazil, on cultivated sugarcane land (terrain slope ≤12%) according to State Law number 11241. To forward the end of this practice to 2014, a “Green Ethanol” Protocol was established in 2007. The present work aims at analyzing five years of continuous sugarcane harvest monitoring, based on remote sensing images, to evaluate the effectiveness of the Protocol, thus helping decision makers to establish public policies to meet the Protocol’s expected goals. During the last five crop years, sugarcane acreage expanded by 1.5 million ha, which was compensated by a correspondent increase in the green harvested land. However, no significant reduction was observed in the amount of pre-harvest burned land over the same period. Based on the current trend, this goal is likely to be achieved one or two years later (2015–2016), which will be five or six years ahead of 2021 as the goal in the State Law number 11241 states. We thus conclude that the“Green Ethanol” Protocol has been effective with a positive impact on the increase of GH, especially on recently expanded sugarcane fields.