Ground-truth Fields Research Articles

Shoreline classification maps and ground truth data for the Neuse River Estuary, North Carolina

Estuaries provide essential ecosystem services and economic value but are facing widespread degradation due to changing anthropogenic and climatic factors. In North Carolina, coastal structures, like bulkheads and riprap, are widely used by property owners throughout the Albemarle-Pamlico estuary to stop erosion and reclaim lost land following storm events. While coastal development is tightly governed, limited historical and no up-to-date data report on the spatial distribution of coastal structures throughout the Albemarle-Pamlico estuary. Here we describe the development of a dataset that classifies and catalogues 67 km of shoreline type along the Neuse River Estuary (NRE), a large tributary of the Albemarle-Pamlico. We used available LiDAR digital elevation models (DEMs), aerial imagery, and a ground truthing field campaign to determine shoreline type present along the NRE as of 2020. We validated these results using an intensive manual editing procedure that comparatively examines DEMs, LiDAR derived slope, aerial imagery, and ground truth photography of the shoreline. This dataset is available for public download.

Prediction of motion induced magnetic fields for human brain MRI at 3 T

ObjectiveMaps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we propose a forward simulation strategy to obtain B0 predictions at different head-positions.MethodsFM were predicted by combining (1) a multi-class tissue model for estimation of tissue-induced fields, (2) a linear k-space model for capturing gradient imperfections, (3) a dipole estimation for quantifying lower-body perturbing fields (4) and a position-dependent tissue mask to model FM alterations caused by large motion effects. The performance of the combined simulation strategy was compared with an approach based on a rigid body transformation of the FM measured in the reference position to the new position.ResultsThe transformed FM provided inconsistent results for large head movements (> 5° rotation, approximately), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach.ConclusionThe proposed simulation strategy is able to predict movement-induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM.

Comparative assessment of satellite- and drone-based vegetation indices to predict arthropod biomass in shrub-steppes.

Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority of arthropods are herbivorous, and others depend on these. Quantifying plant productivity with satellite or aerial vehicle imagery is an easy and fast procedure already tested and implemented in agriculture and field ecology. However, the capability of satellite or aerial vehicle imagery for quantifying arthropod biomass and its relationship with plant productivity has been scarcely addressed. Here, we used unmanned aerial vehicle (UAV) and satellite Sentinel-2 (S2) imagery to establish a relationship between plant productivity and arthropod biomass estimated through ground-truth field sampling in shrub steppes. We UAV-sampled seven plots of 47.6-72.3ha at a 4-cm pixel resolution, subsequently downscaling spatial resolution to 50 cm resolution. In parallel, we used S2 imagery from the same and other dates and locations at 10-m spatial resolution. We related several vegetation indices (VIs) with arthropod biomass (epigeous, coprophagous, and four functional consumer groups: predatory, detritivore, phytophagous, and diverse) estimated at 41-48 sampling stations for UAV flying plots and in 67-79 sampling stations for S2. VIs derived from UAV were consistently and positively related to all arthropod biomass groups. Three out of seven and six out of seven S2-derived VIs were positively related to epigeous and coprophagous arthropod biomass, respectively. The blue normalized difference VI (BNDVI) and enhanced normalized difference VI(ENDVI) showed consistent and positive relationships with arthropod biomass, regardless of the arthropod group or spatial resolution. Our results showed that UAV and S2-VI imagery data may be viable and cost-efficient alternatives for quantifying arthropod biomass at large scales in shrub steppes. The relationship between VI and arthropod biomass is probably habitat-dependent, so future research should address this relationship and include several habitats to validate VIs as proxies of arthropod biomass.

The Evaluation of Spectral Vegetation Indexes and Redundancy Reduction on the Accuracy of Crop Type Detection

Knowledge about crop type distribution is valuable information for effective management of agricultural productivity, food security estimation, and natural resources protection. Algorithms for automatic crop type detection have great potential to positively influence these aspects as well as speed up the process of crop type mapping in larger areas. In the presented study, we used 14 Sentinel-2 images to calculate 12 widely used spectral vegetation indices. Further, to evaluate the effect of reduced dimensionality on the accuracy of crop type mapping, we utilized principal component analysis (PCA). For this purpose, random forest (RF)-supervised classifications were tested for each index separately, as well as for the combinations of various indices and the four initial PCA components. Additionally, for each RF classification feature importance was assessed, which enabled identification of the most relevant period of the year for the differentiation of crop types. We used 34.6% of the ground truth field data to train the classifier and calculate various accuracy measures such as the overall accuracy (OA) or Kappa index. The study showed a high effectiveness of the Modified Chlorophyll Absorption in Reflectance Index (MCARI) (OA = 86%, Kappa = 0.81), Normalized Difference Index 45 (NDI45) (OA = 85%, Kappa = 0.81), and Weighted Difference Vegetation Index (WDVI) (OA = 85%, Kappa = 0.80) in crop type mapping. However, utilization of all of them together did not increase the classification accuracy (OA = 78%, Kappa = 0.72). Additionally, the application of the initial three components of PCA allowed us to achieve an OA of 78% and Kappa of 0.72, which was unfortunately lower than the single-index classification (e.g., based on only NDVI45). This shows that dimensionality reductions did not increase the classification accuracy. Moreover, feature importance from RF indicated that images captured from June and July are the most relevant for differentiating crop types. This shows that this period of the year is crucial to effectively differentiate crop types and should be undeniably used in crop type mapping.

An integrated approach of field, weather, and satellite data for monitoring maize phenology

Efficient, more accurate reporting of maize (Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain.

Environmental flow sustainability in the Lower Limpopo River Basin, Mozambique

Study regionThis study focuses on the Lower Limpopo River basin (LLRB) in Mozambique, Africa. Study focusMaintaining environmental flows necessary for ecosystem sustainability represents a significant challenge to water resource management. In this study the sustainability of LLRB was evaluated by comparing hydrologic availability with ecological and anthropogenic needs. Current river ecological status was scored with a habitat integrity index verified through ground-truthing field surveys and aerial imagery data. Local stakeholder interviews were used to further evaluate the habitat index scores. Deficiencies between water availability and ecological-human requirements were assessed with a water scarcity index. New Hydrological Insights for the RegionFour environmental flow categories defined as “Excellent”, “Fair”, “Poor”, and “Degraded” coincided to approximately 50 %, 39 %, 27 %, and 14 % of the natural mean annual flow, respectively. Stakeholder interview responses indicated annual water shortages currently occur between August and November and coincide with “Poor” and “Degraded” environmental flow conditions. Water supplies appear to meet consumption needs when calculated on an annual basis with the water scarcity index. However, when calculated monthly, there is not enough to meet human water demand between August and October. This deficit period will likely expand from June to November due to projected increases in future water demands. As the greatest water use in the basin is agricultural irrigation, long-term environmental flows sustainability will likely depend upon effective irrigation management.

Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas

The geological and geographical conditions have made the Himalayas one of the regions most vulnerable to disasters. Northern Pakistan is made up of high-relief Himalayan mountains where landslides are the most frequently occurring catastrophic events. Floods, earthquakes, and landslides occur most often in this region, causing deaths and severe infrastructure damages. The studied area is experiencing significant landslide events triggered by destructive seismic activity, extreme seasonal rainfalls, and human activities. In this study, landslide susceptibility mapping is proposed using an integrated model of information value method by optimizing its capabilities and overcoming its limitations to enhance both precision and accuracy of the results. This approach quantifies and estimates the probability of landslide occurrence using statistical principles and techniques. Seven distinct landslide causative factors, including slope angle, distance to stream, drainage density, distance to road, land cover, normalized difference vegetation index, lithology, and distance to faults were evaluated to determine the areas at risk for future slope movements. A reliable landside inventory from a spatial database combined with ground-truthing field surveys was prepared for evaluating the relationship between the landslides and their causative factors. The accuracy and efficiency of the developed model are evaluated using sensitivity analysis of the receiver operator curve (ROC). The ROC-validated results showed an accuracy of 0.845 for model prediction rate and an accuracy of 0.837 for model success rate, indicating that the model is reliable to produce landslide susceptibility map. The results showed the reasonable efficiency of the applied method for assessing landslide susceptibility in the study area and applicable to other regions with similar geomorphological conditions.

Delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques: A comprehensive approach

ABSTRACT Palaeochannels are remnants of rivers or stream channels that flowed in the past and have been currently filled or buried by younger fluviatile sediments. There are a number of techniques for detecting palaeochannels with each of them having its own intrinsic advantages and disadvantages. In the present study, apart from applying different approaches, the capabilities, and effectiveness of the approaches to locate and map palaeochannels are compared. Remote sensing-based approaches are comprised of high-resolution satellite datasets and a LiDAR-derived elevation map for displaying extensive drainage networks of surface feature channels hypothesized to be palaeochannels, palaeorivers, or palaeovalleys. Ground-penetrating radar (GPR) transects along with concurrent visual observations are ground-truthing field approaches and has proved to be one of the most appropriate and accurate methods for locating palaeochannels due to their capability to identify subsurface sedimentary stratigraphy innate to palaeochannels. Nevertheless, visual field observations for ground-truthing of prospective palaeochnnels either exhibiting only subtle alterations in desiccation or vegetation are considered a potent field approach. The present review work lays a strong emphasis on delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques to meet the sustainable groundwater development goals.

Predictively modelling the distribution of the threatened brush-tailed rock-wallaby (

Abstract Context Species Distribution Models (SDM) can be used to investigate and understand relationships between species occurrence and environmental variables, so as to predict potential distribution. These predictions can facilitate conservation actions and management decisions. Oxley Wild Rivers National Park (OWRNP) is regarded as an important stronghold for the threatened brush-tailed rock-wallaby (Petrogale penicillata), on the basis of the presence of the largest known metapopulation of the species. Adequate knowledge of the species’ ecology and distribution in OWRNP is a key objective in the national recovery plan for the species occurring in the Park. Aims To model distribution using key GIS-derived environmental factors for the brush-tailed rock-wallaby in OWRNP and to ground-truth its presence through field surveys in areas of high habitat suitability. Methods We used Maxent to model the distribution of the brush-tailed rock-wallaby within OWRNP on the basis of 282 occurrence records collected from an online database, elicitation of informal records from experts, helicopter surveys and historic records. Environmental variables used in the analysis were aspect, distance to water, elevation, geology type, slope and vegetation type. Key results Vegetation type (37.9%) was the highest contributing predictor of suitable habitat, whereas aspect (4.8%) contributed the least. The model produced an area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.780. The model was able to discriminate between suitable and non-suitable habitat for brush-tailed rock-wallabies. Areas identified in our model as being highly suitable yielded eight new occurrence records during subsequent ground-truthing field surveys. Conclusions Brush-tailed rock-wallaby distribution in OWRNP is primarily associated with vegetation type, followed by distance to water, elevation, geology, slope and aspect. Field surveys indicated that the model was able to identify areas of high habitat suitability. Implications This model represents the first predicted distribution of brush-tailed rock-wallaby in OWRNP. By identifying areas of high habitat suitability, it can be used to survey and monitor the species in OWRNP, and, thus, contribute to its management and conservation within the Park.

NEMA: Automatic Integration of Large Network Management Databases

Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this article, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that NEMA can achieve accuracy of 95%. We further compare with several other existing algorithms, and show that NEMA outperforms them by 7%-15% in numerical matching and achieves the best trade-off for non-numerical matching.

Multiplicity and complexity of food environment in China: full-scale field census of food outlets in a typical district.

Community food environment is closely associated with residents' health status, and it has received substantial research attention in recent years. However, such studies are sparse in China. This study aims to reveal the characteristics of food environment in China. A ground-truthing field census of food outlets was conducted in Xi Hu district, Hangzhou. A novel categorizing system was developed to better characterize food outlets in the study area. Geocoding and food environment analysis were performed by Arc-GIS software. A total of 9274 food outlets were identified, of these, 6161 (66.43%) were food service places and 3113 (33.57%) were food stores. Fast-food restaurants (2692) and convenience stores (1764) were the two most numerous outlet types. The density of food outlets was 29.72/km2 and 113.97/10,000 people. Availability of BMI-unhealthy food outlets was four times greater than that of BMI-healthy ones. Further, 41.86% of total food outlets provided food-delivery services; among them, fast-food restaurants were the highest (65.53%) providers. In the urban area, the average density of food outlets was 104/km2. While, it was only 29/km2 in the rural area. Availability of food outlets was excellent in our study area, though it showed regional imbalance. Food environment tends to be obesogenic and high prevalence of food delivery service might increase this trend. Thus, policy makers should guarantee the smooth development of catering industry in China, so as to ensure a balanced nutrition environment.

Watersheds Morphometry and Structural Interpretation of Lineaments Extracted from SRTM Data in the Mayo-Kani Division Far-Nord Region, (Cameroon)

In the Far-North Region of Cameroon, the high rate of weathering and erosion of rocks do not allow to map the hydrological and geological structures in the field. This paper aims to test the combined use of field topographic data and Shuttler Radar Topography Mission (SRTM) data to define watershed morphometry, highlight the different erosion factors, and characterize the structural lineaments of the Mayo-Kani Division, within the Far-North Region of Cameroon. All these are done using; watersheds, hydrographic network, slopes, manual lineaments and automatic lineaments maps. These treatment techniques used (filtering, enhancement and slope analyses) allows: to morphologically characterize the ten identified Mayo-Kani watersheds, to measure the erodibility rate of the watersheds using the Universal Soil Loss Equation (USLE) and to compute the fracture maps. This last operation was validated using ground truth field observations and the literature review. Fracture networks were analyzed using statistical analysis techniques by studying fracture length distribution laws.

Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California

Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e.g., from MODIS-NDVI (Moderate Resolution Imaging Spectroradiometer—Normalized Difference Vegetation Index) time-series, which has usually limited their application to scenarios with few land uses or crops covering areas larger than actual parcel sizes. As an alternative, this paper proposes a general and robust procedure to map crop phenology at the level of individual crop parcels, and validates its feasibility in a complex and diverse cropland area located in central California. A first calibration phase consisted of evaluating the three curve-fitting models implemented in the TIMESAT software (i.e., asymmetric Gaussian (AG), double logistic (DL), and adaptive Savitzky–Golay (SG) filtering) and reporting the model and its settings that best adjusted to the MODIS-NDVI profile of each crop studied. Next, based on the selected crop-specific models and with a crop map previously obtained from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-temporal images, the procedure mapped four crop calendar events (i.e., start, end, middle, and length of the season) and five phenology-related metrics (i.e., base, maximum, amplitude, derivatives, and integrals of the NDVI values) of the study region by object-based image analysis (OBIA) of the MODIS-NDVI time-series. To mitigate the impact of mixed pixels, the OBIA procedure was designed to automatically apply a restrictive criterion based on the coverage of MODIS-NDVI pixels in each crop parcel: (1) using only the MODIS-NDVI pixels that were placed 100% within each crop parcel (i.e., “pure” pixels); or (2) if no “pure” pixels exist in any crop parcel, using only pixels with coverage percentages greater than 50%, and in such cases, reporting the mixing percentage in the output file. The calibration phase showed that the performance of the SG filtering was superior in most crops, with the exception of rice, while the AG model was intermediate in all of the cases. Differences between the dates of the start and end of the season that were observed in 120 ground-truth fields and the ones estimated by the crop-specific models were in a range of 11 days (for the corn fields) and 22 days (for the vineyard fields) on average. The OBIA procedure was also validated in 240 independent parcels with “pure” MODIS-NDVI pixels, reporting 89% and 82% of accuracy when mapping the start and end of the season, respectively. Our results revealed different growth patterns of the studied crops, especially of the crop calendar events of herbaceous (i.e., corn, rice, sunflower, and tomato) and woody crops (i.e., almond, walnut, and vineyard), of the NDVI derivatives of rice and the other studied herbaceous crops, and of the NDVI integrals of vineyard and the other studied woody crops. The resulting maps and tables provide valuable geospatial information for every parcel over time with several applications in cropland management, irrigation scheduling, and ecosystem modeling.

Identifying important ecological areas for potential rainwater harvesting in the semi-arid area of Chifeng, China.

Global water shortage is becoming increasingly severe, so the identification and protection of potential areas for harvesting water play important roles in alleviating drought. Suitable sites for potential water harvesting require a high runoff potential. Avoiding soil erosion caused by high surface runoff, however, is also necessary. We therefore developed a procedure for the continuous accounting of runoff potential based on the Soil Conservation Service curve number and potential risks of water and soil loss based on the universal soil loss equation to evaluate the potential for water harvesting. Suitable sites for rainwater harvesting covered 24.90% of the semi-arid area of Chifeng, southeastern Inner Mongolia, China. The best areas accounted for 8.4% of the study area. The southern part of the Greater Hinggnan Mountains in northern Chifeng had a large rainwater harvesting area, and the western and eastern parts of the Chifeng area belonging to Horqin Sandy Land and Hunshandake Sandy Land, respectively, had smaller rainwater-harvesting areas. The eight reservoirs in the Xilamulun River Basin were further analyzed as an example. Derived sites investigated by ground-truth field verification indicated a method accuracy of 87.5%. This methodology could be effective in other areas with similar requirements due to the increasing demand for water resources and requirements for the protection of soil-water resources.

Lithological mapping of Kanjamalai hill using hyperspectral remote sensing tools in Salem district, Tamil Nadu, India

This study uses advanced spaceborne thermal emission and reflection radiometer (ASTER) hyperspectral remote sensing techniques to discriminate rock types composing Kanjamalai hill located in the Salem district of Tamil Nadu, India. Kanjamalai hill is of particular interest because it contains economically viable iron ore deposits. ASTER hyperspectral data were subjected to principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) to improve identification of lithologies remotely and to compare these digital data results with published geologic maps. Hyperspectral remote sensing analysis indicates that PCA (R∶G∶B=2∶1∶3), MNF (R∶G∶B=3∶2∶1), and ICA (R∶G∶B=1∶3∶2) provide the best band combination for effective discrimination of lithological rock types composing Kanjamalai hill. The remote sensing-derived lithological map compares favorably with a published geological map from Geological Survey of India and has been verified with ground truth field investigations. Therefore, ASTER data-based lithological mapping provides fast, cost-effective, and accurate geologic data useful for lithological discrimination and identification of ore deposits.

A new method for crop classification combining time series of radar images and crop phenology information

Agricultural land cover is characterized by strong variations within relatively short time intervals. These dynamics are challenging for land cover classifications on the one hand, but deliver crucial information that can be used to improve the classifiers performance on the other hand. Since up to date mapping of crops is crucial to assess the impact of agricultural land use on the ecosystems, an accurate and complete classification of crop types is of high importance. In the presented study, a new multitemporal data based classification approach was developed that incorporates knowledge about the phenological changes on crop lands. It identifies phenological sequence patterns (PSP) of the crop types based on a dense stack of Sentinel-1 data and accurate information about the plant's phenology. The performance of the developed methodology has been tested for two different vegetation seasons using over 200 ground truth fields located in northern Germany. The results showed that a dense time series of Sentinel-1 images allowed for high classification accuracies of grasslands, maize, canola, sugar beets and potatoes (F1-score above 0.8) using PSP as well as standard (Random Forest and Maximum Likelihood) classification method. The PSP approach clearly outperformed standard methods for cereal crops, especially for spring barley where the F1-score varied between zero and 0.43 for standard approaches, while PSP achieved values as high as 0.74 and 0.79 for both vegetation seasons. The PSP based approach also outperformed for oat, winter barley and rye. Furthermore, the PSP classification is more resilient to differences in farming management and conditions of growth since it takes information about each crop types' growing stage and its growing period into consideration. The results also indicate, that the PSP approach was more sensitive to subtle changes such as the proportion of weeds within a field.

Determining the number of measurements required to estimate crop residue cover by different methods

Crop residue left after harvest plays an important role in controlling against soil erosion and in increasing soil organic matter content of agricultural soils. Crop residue management is a practice of great importance in southwestern Ontario, where soil management practices have an effect on Great Lakes water quality. The use of remote sensing data to measure and monitor crop residue can be fast and efficient. However, remote sensing–based studies need calibration and validation using field observations. The objective of this study was to determine the optimal number of ground-truthing field measurements (i.e., digital photographs) required to estimate residue levels. To do so, we compared the residue estimates derived from digital photographs with those derived from the standard line-transect method. Residue was measured from 18 fields located in southern Ontario, and data collected included percentage of crop residue using line-transect and photographic grid methods. Results were analyzed using linear regression, correlation tests, ANOVA, and means tests. Analyses were also conducted to retrospectively determine the minimum number of line transects or digital photos required to estimate crop residue cover at specified levels of power. Results showed that (1) percentage of crop residue estimates derived from using digital photographs were strongly correlated (<i>r</i> = 0.91, <i>p</i> &lt; 0.001, <i>R</i>² = 0.83, and <i>n</i> = 90) to those derived from using line transects; (2) counting 50 to 100 points per digital photograph was sufficient to accurately estimate the percentage of residue cover; and (3) there was greater variability in the results for soybean (<i>Glycine max</i> [L.] Merr.) than for corn (<i>Zea mays</i> L.), with the highest variability for medium-level soybean residue. Overall, the digital photograph method to estimate percentage of residue was found to be a suitable alternative to the line-transect method, which is more time consuming and labor intensive. Determining the optimal numbers of measurements to estimate crop residue cover is important to those wishing to use digital photo capture methods to record, archive, and measure residue for remote sensing calibration and validation or for handheld mobile device applications.

DEPTH MAP ESTIMATION IN LIGHT FIELDS USING AN STEREO-LIKE TAXONOMY

The light field or LF is a function that describes the amount of light traveling in every direction (angular) through every point (spatial) in a scene, this LF can be captured in several ways, using arrays of cameras, or more recently using a single camera with an special lens, that allows the capture of angular and spatial information of light rays of a scene (LF). This recent camera implementation gives a different approach to find the dept of a scene using only a single camera. In order to estimate the depth, we describe a taxonomy, similar to the one used in stereo Depth-map algorithms. That consist in the creation of a cost tensor to represent the matching cost between different disparities, then, using a support weight window, aggregate the cost tensor, finally, using a winner-takes-all optimization algorithm, search for the best disparities. This paper explains in detail the several changes made to an stereo-like taxonomy, to be applied in a light field, and evaluate this algorithm using a recent database that for the first time, provides several ground-truth light fields, with a respective ground-truth depth map.

Urban Sprawl Analysis in Jalingo Metropolis, Taraba State Nigeria

This study analyzed spatial and temporal land use /land cover change in Jalingo Metropolis, Taraba State, north east Nigeria. This was carried out with the use of land sat images from 1990-2015. Satellite images were obtained from the United State Geological Survey (USGS) database online resources. The images were georeferenced and processed. Supervised image classification technique was employed. Ground truthing field survey was carried out to complement information about the types of land use and land-cover classes in the area. The change detection and change matrixes of the classified satellite images were analyzed using percentage to see the rate of change in a simple form. Land use maps were obtained through supervised classifications of satellite imageries and presented in Tables. The findings of the study show that Jalingo Metropolis has witnessed unprecedented spatial growth over the years. The town has grown from 6.01km2 in 1990 to 55.7km2 in 2005 and 101.16 in 2015. This growth was as a result of change in the status of the metropolis from Local Government headquarters to state capital which brought about the establishment of government ministries and agencies, with wide range of opportunities. Despite the existence of master plan, implementation has not been satisfactory for a number of reasons. This has resulted in proliferation of haphazard and uncontrolled development (including slums and squatters), environmental degradation, lack of basic amenities and transportation problems within and around the town. The study recommends the need to update the urban database of Jalingo town using GIS and Remote Sensing techniques to plan and monitor urban land use changes in the area.

Remote sensing of Qatar nearshore habitats with perspectives for coastal management

A framework is proposed for utilizing remote sensing and ground-truthing field data to map benthic habitats in the State of Qatar, with potential application across the Arabian Gulf. Ideally the methodology can be applied to optimize the efficiency and effectiveness of mapping the nearshore environment to identify sensitive habitats, monitor for change, and assist in management decisions. The framework is applied to a case study for northeastern Qatar with a key focus on identifying high sensitivity coral habitat. The study helps confirm the presence of known coral and provides detail on a region in the area of interest where corals have not been previously mapped. Challenges for the remote sensing methodology associated with natural heterogeneity of the physical and biological environment are addressed. Recommendations on the application of this approach to coastal environmental risk assessment and management planning are discussed as well as future opportunities for improvement of the framework.