Detailed Land Cover Map Research Articles

Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

Coupled 1D-2D modelling is a widely used approach to predict water movement in complicated surface and subsurface drainage systems in urban or peri-urban areas. In this study, a hybrid parallel code, H12, is developed for 1D-2D coupled urban flood modelling. Hybrid-1D-2D, or H12, enables street-resolving hyper-resolution simulation over a large area by combining Open Multi-Processing (OpenMP) and Message Passing Interface (MPI) parallelization. Variable grid sizing is adopted for detailed geometric representation of urban surfaces as well as efficient computation. To assess the capability of H12, simulation experiments were carried for the Johnson Creek Catchment (∼40 km2) in Arlington, Texas. The LiDAR-derived digital elevation model (DEM) and detailed land cover map at 1-m resolution are used to represent the terrain and urban features in flood modelling. Hybrid parallelization achieves up to a 79-fold reduction in simulation time compared to the serial run and is more efficient than either OpenMP or MPI alone especially in hyper-resolution simulations.

Changes in the methodology used in the production of the Spanish CORINE: Uncertainty analysis of the new maps

Since 2012 CORINE has been obtained in Spain from the generalization of a more detailed land cover map (SIOSE). This methodological change has meant the production of a new CORINE map, which is different from the existing ones. To analyze how different the new maps are from the previous ones, as well as the advantages and disadvantages of the new methodology, we carried out a comparison of the CORINE obtained from both methods (traditional and generalization) for the year 2006. The new CORINE is more detailed and it is more coherent with the rest of Spanish Land Use Land Cover (LULC) maps. However, problems have been encountered with regard to the meaning of its classes, the fragmentation of patches and the complexity of its perimeters.

Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

Improved Urban Scene Classification Using Full-Waveform Lidar

Full-waveform lidar data provides supplementary radiometric as well as more accurate geometric target information, when compared to discrete return systems. In this research, a wide range of classes in an urban scene; including trees, medium vegetation, low vegetation (grass), water bodies, pitched roofs, flat roofs, asphalt, vehicles, power lines, walls (fences) and concrete are considered. In order to tackle the challenge of distinguishing geometrically similar classes and enhancing the separability of other targets, a new set of features based on deconvolved waveforms is introduced. The positive effect of the proposed feature dataset on classification accuracy in individual classes is shown using two ensemble classifiers (random forests and RUSBoost). Performance of the classifiers is improved by integration with sampling techniques, especially for the under-represented classes. The final output of the proposed method is a highly detailed land cover map of the urban scene, which affords good separability between critical classes.

Fusion of TerraSAR-x and Landsat ETM+ data for protected area mapping in Uganda

TerraSAR-X satellite acquires very high spatial resolution data with potential for detailed land cover mapping. A known problem with synthetic aperture radar (SAR) data is the lack of spectral information. Fusion of SAR and multispectral data provides opportunities for better image interpretation and information extraction. The aim of this study was to investigate the fusion between TerraSAR-X and Landsat ETM+ for protected area mapping using high pass filtering (HPF), principal component analysis with band substitution (PCA) and principal component with wavelet transform (WPCA). A total of thirteen land cover classes were identified for classification using a non-parametric C 4.5 decision tree classifier. Overall classification accuracies of 74.99%, 83.12% and 85.38% and kappa indices of 0.7220, 0.8100 and 0.8369 were obtained for HPF, PCA and WPCA fusion approaches respectively. These results indicate a high potential for a combined use of TerraSAR-X and Landsat ETM+ data for protected area mapping in Uganda.

Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.

The impact of human pressure on landscape patterns and plant species richness in Mediterranean coastal dunes

We analyze the spatial patterns of natural dune cover patches and their plant richness, comparing coastal sites with different levels of human pressure in central Italy. We created a detailed land cover map of dune sites. The spatial pattern of natural dune cover types was characterized by computing a set of patch-based metrics. To quantify patch plant richness, we used 16 m2 vegetation plots, randomly distributed on coastal dune cover types. For each patch, the richness of the entire pool of species and of three guilds (i.e., typical dune, ruderal, and alien species) was considered. We compared different levels of human pressure on coastal dunes focusing on pattern metrics and floristic information by using the nonparametric Kruskal–Wallis test. In sites with high human pressure, we have observed a general simplification in the natural dune spatial pattern and a decline of plant richness but with a specific response for each cover type. Alien and ruderal species presented low richness in all patches. In coastal dunes, the harsh ecological conditions and the strong sea–inland gradient shape the distribution of human activities and control the number of ruderal species. The approach effectively describes fragmentation and biodiversity in dune ecosystems.

Comparison of Two Remote Sensing Based Models for Regional Net Primary Productivity Estimation—A Case Study in Semi-Arid Central Kazakhstan

Modelling net primary productivity (NPP) is an important instrument for analysing carbon exchange between atmosphere and vegetation as well as for quantification of carbon sinks and sources. Remote-sensing-based models allow for regional NPP estimation and are potentially transferable to new regions. Comparative model analyses, however, are lacking, especially for semi-arid environments. In this study, two recent remote-sensing-based NPP models were applied for the first time to a study region in semi-arid Kazakhstan: RBM, a light-use-efficiency model based on MODIS products, and BETHY/DLR, a soil-vegetation-atmosphere-transfer model. Differences in intermediate products, their influence on calculated NPP, as well as output products are evaluated and discussed. BETHY/DLR calculates higher NPP (mean annual NPP 2010 and 2011: 136.87 g C m-2 and 106.69 g C m-2) than RBM (62.14 g C m-2 and 54.61 g C m-2) and shows stronger inter-annual changes. Spatial and seasonal patterns present well phenological differences. Comparison to field data from 2011 showed better results for BETHY/DLR, though both results were highly correlated to the field observations (BETHY/DLR: R2=0.95, RMSE=8.36 g C m-2; RBM: R2=0.98, RMSE=22.49 g C m-2). The parameterization of the light use efficiency is critical for RBM; also MODIS based 16-day time steps might be too long to capture variable climatic conditions. For BETHY/DLR, the MODIS land cover product applied in this study differentiates insufficient classes within the semi-arid environment; a more detailed land cover map is needed to improve the regional analysis.

What has happened to coastal dunes in the last half century? A multitemporal coastal landscape analysis in Central Italy

The current degradation of the Mediterranean coastal landscape highlights the necessity of, performing long term multitemporal analysis to guide land managers seeking to improve coastal, conservation, planning and management. This work sets out to quantify landscape changes for the last, 50 years in one sector of the Italian coast, with emphasis on natural dune cover types, and to analyze, their spatial configuration in relation to the anthropogenic cover types to investigate the processes, underlying these changes. On the basis of detailed land-cover maps (dated 1954, 1986 and 2006), we, assessed landscape changes by transition matrices. The spatial pattern of natural dune cover types, (Beaches with Pioneer annual Vegetation, Herbaceous Dune Vegetation and Woody Dune Vegetation), for the three dates was described by patch-based (Patch Size and Shape Index) and edge-based metrics, (patch shared boundaries with manmade cover types) and compared using the non-parametric, Kruskal–Wallis median test. Multitemporal analyses provided a description of the coastal changes, occurred in the last half century. Particularly notable are consistent intensification of agricultural and, afforestation activities in the inner sectors of the dunes and a widespread urbanization. The natural, dune cover types, originally distributed in few large and elongated patches, have undergone a, substantial reduction of extent and an alteration in their spatial pattern, being now reduced to many, small and regular patches. A significant rise of contacts between natural dune cover types and, urbanized areas and infrastructures was observed. Such landscape analysis could be useful to develop, management strategies for coastal dune areas.

Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data

This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C3 and C4 ) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPARps ) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R(2) >0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry-grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R(2) >0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPARps ) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPARps at relatively high spatial resolution (20m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution.

The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land Surface

The Soil Moisture and Ocean Salinity mission has been launched by the European Space Agency (ESA) in November 2009. It is the worldwide first satellite dedicated to retrieve soil moisture information at the global scale, with a high temporal resolution, and from spaceborne L-band radiometry. This novel technique requires careful calibration, validation, and an in-depth understanding of the acquired data and the underlying processes. In this light, a measurement campaign was undertaken recently in the river catchment of the upper Danube in southern Germany. In May and June 2010, airborne thermal infrared and L-band passive microwave data were collected together with spatially distributed in situ measurements. Two airborne radiometers, EMIRAD and HUT-2D, were used during the campaigns providing two complementary sets of measurements at incidence angles from 0° to 40° and with ground resolutions from roughly 400 m to 2 km. The contemporaneous distributed ground measurements include surface soil moisture, a detailed land cover map, vegetation height, phenology, and biomass. Furthermore, several ground stations provide continuous measurements of soil moisture and soil temperature as well as of meteorological parameters such as air temperature and humidity, precipitation, wind speed, and radiation. All data have undergone thorough postprocessing and quality checking. Their values and trends fit well among each other and with the theoretically expected behavior. The aim of this paper is to present these data which may contribute to potential further studies of soil moisture, brightness temperature, and their spatial variability. The presented data are available to the scientific community upon request to ESA.

Identifying landscape features associated with Rift Valley fever virus transmission, Ferlo region, Senegal, using very high spatial resolution satellite imagery.

IntroductionDynamics of most of vector-borne diseases are strongly linked to global and local environmental changes. Landscape changes are indicators of human activities or natural processes that are likely to modify the ecology of the diseases. Here, a landscape approach developed at a local scale is proposed for extracting mosquito favourable biotopes, and for testing ecological parameters when identifying risk areas of Rift Valley fever (RVF) transmission. The study was carried out around Barkedji village, Ferlo region, Senegal.MethodsIn order to test whether pond characteristics may influence the density and the dispersal behaviour of RVF vectors, and thus the spatial variation in RVFV transmission, we used a very high spatial resolution remote sensing image (2.4 m resolution) provided by the Quickbird sensor to produce a detailed land-cover map of the study area. Based on knowledge of vector and disease ecology, seven landscape attributes were defined at the pond level and computed from the land-cover map. Then, the relationships between landscape attributes and RVF serologic incidence rates in small ruminants were analyzed through a beta-binomial regression. Finally, the best statistical model according to the Akaike Information Criterion corrected for small samples (AICC), was used to map areas at risk for RVF.ResultsAmong the derived landscape variables, the vegetation density index (VDI) computed within a 500 m buffer around ponds was positively correlated with serologic incidence (p<0.001), suggesting that the risk of RVF transmission was higher in the vicinity of ponds surrounded by a dense vegetation cover. The final risk map of RVF transmission displays a heterogeneous spatial distribution, corroborating previous findings from the same area.ConclusionsOur results highlight the potential of very high spatial resolution remote sensing data for identifying environmental risk factors and mapping RVF risk areas at a local scale.

An easy and low-cost method for preprocessing and matching small-scale amateur aerial photography for assessing agricultural land use in Burkina Faso

In recent decades, the Kou watershed in south-western Burkina Faso has suffered from poor water management. Despite the abundance of water, most water users regularly face water shortages because of the increase in the amount of land under irrigation. To help them achieve a more equitable allocation of irrigated land, local stakeholders need an easily managed low-cost tool for monitoring and mapping these irrigated zones. The aim of this study was to develop a fast and low-cost procedure for mosaicing and geo referencing amateur small-scale aerial photographs for land-use surveys. Sets of tens (2009) and hundreds (2007) of low-altitude aerial photographs, with a resolution of 0.4m and 0.8m, respectively, were used to create a detailed land-cover map of typical African small-scale irrigated agriculture. A commercially available stitching tool and GIS allowed geo referenced 'mono-images’ to be constructed; both mosaics were warped on a high-resolution SPOT image with a horizontal root mean square error (RMSE) of about 11m. The RMSE between the two image datasets was 2m. This approach is less sensitive to atmospheric conditions that are non-predictable in programming satellite imagery.

Estimating CO2 Sequestration by Forests in Oita Prefecture, Japan, by Combining LANDSAT ETM+ and ALOS Satellite Remote Sensing Data

CO2 sequestration of the forests in Oita Prefecture, Japan, was estimated using satellite remote sensing data. First, hybrid classification of the optical LANDSAT ETM+ data was performed using GIS to produce a detailed land cover map. CO2 sequestration for each forest type was calculated using the sequestration rates per unit area multiplied by the forest areas obtained from the land cover map This results in 3.57 MtCO2/yr for coniferous, 0.77 MtCO2/yr for deciduous broadleaf, and 2.25 MtCO2/yr for evergreen broadleaf, equivalent to a total of 6.60 MtCO2/yr for all the forest covers in Oita. Then, two different methodologies were used to improve these estimates by considering tree ages: the Normalized Difference Vegetation Index (NDVI) and the stem volume methods. Calculation using the NDVI method shows the limitation of this method in providing detailed estimations for trees older than 15 years, because of NDVI saturation beyond this age. In the stem volume method, tree ages were deduced from stem volume values obtained by using PALSAR backscattering data. Sequestration based on tree age forest subclasses yields 2.96 MtCO2/yr (coniferous) and 0.31 MtCO2/yr (deciduous broadleaf forests). These results show the importance of using not only detailed forest types, but also detailed tree age information for more realistic CO2 sequestration estimates. In so doing, overestimation of the sequestration capacity of forests could be avoided, and the information on the status and location of forest resources could be improved, thereby leading to sounder decision making in sustainable management of forest resources.

An Adaptive Contextual SEM Algorithm for Urban Land Cover Mapping Using Multitemporal High-Resolution Polarimetric SAR Data

This paper presents a semi-supervised Stochastic Expectation-Maximization (SEM) algorithm for detailed urban land cover mapping using multitemporal high-resolution polarimetric SAR (PolSAR) data. By applying an adaptive Markov Random Field (MRF) with the spatially variant Finite Mixture Model (SVFMM), spatial-temporal contextual information could be effectively explored to improve the mapping accuracy with homogenous results and preserved shape details. Further, a learning control scheme was proposed to ensure a robust semi-supervised mapping process thus avoiding the undesired class merges. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area were used to evaluate the proposed method. Common PolSAR distribution models such as Wishart, G0p, Kp and KummerU were compared through this contextual SEM algorithm for detailed urban land cover mapping. Comparisons with Support Vector Machine (SVM) were also conducted to assess the potential of our parametric approach. The results show that the Kp, G0p and KummerU models could generate better urban land cover mapping results than the Wishart model and SVM.

Integrating geographical data and phenological characteristics derived from MODIS data for improving land cover mapping

The study developed a feasible method for large-area land cover mapping with combination of geographical data and phenological characteristics, taking Northeast China (NEC) as the study area. First, with the monthly average of precipitation and temperature datasets, the spatial clustering method was used to divide the NEC into four ecoclimate regions. For each ecoclimate region, geographical variables (annual mean precipitation and temperature, elevation, slope and aspect) were combined with phenological variables derived from the moderate resolution imaging spectroradiometer (MODIS) data (enhanced vegetation index (EVI) and land surface water index (LSWI)), which were taken as input variables of land cover classification. Decision Tree (DT) classifiers were then performed to produce land cover maps for each region. Finally, four resultant land cover maps were mosaicked for the entire NEC (NEC_MODIS), and the land use and land cover data of NEC (NEC_LULC) interpreted from Landsat-TM images was used to evaluate the NEC_MODIS and MODIS land cover product (MODIS_IGBP) in terms of areal and spatial agreement. The results showed that the phenological information derived from EVI and LSWI time series well discriminated land cover classes in NEC, and the overall accuracy was significantly improved by 5.29% with addition of geographical variables. Compared with NEC_LULC for seven aggregation classes, the area errors of NEC_MODIS were much smaller and more stable than that of MODIS_IGBP for most of classes, and the wall-to-wall spatial comparisons at pixel level indicated that NEC_MODIS agreed with NEC_LULC for 71.26% of the NEC, whereas only 62.16% for MODIS_IGBP. The good performance of NEC_MODIS demonstrates that the methodology developed in the study has great potential for timely and detailed land cover mapping in temperate and boreal regions.

Sub-class Clustering of Land Cover over Asia considering 9-year NDVI and Climate Data

In this paper an attempt has been made to classify Asia land cover considering climatic and vegetative characteristics. The sub-class clustering based on the 13 MODIS land cover classes (except water) over Asia was performed with the climate map and the NOVI derived from SPOT 5 VGT D10 data. The unsupervised classification for the sub-class clustering was performed in each land cover class, and total 74 clusters were determined over the study area. Via these clusters, the annual variations (from 1999 to 2007) of precipitation rate and temperature were analyzed as an example by a simple linear regression model. The various annual variations (negative or positive pattern) were represented for each cluster because of the various climate zones and NOVI annual cycles. Therefore, the detailed land cover map as the classification result by the sub-class clustering in this study can be useful information in modelling works for requiring the detailed climatic and vegetative information as a boundary condition.

Broad-scale spatial pattern of forest landscape types in the Guiana Shield

Detecting broad scale spatial patterns across the South American rainforest biome is still a major challenge. Although several countries do possess their own, more or less detailed land-cover map, these are based on classifications that appear largely discordant from a country to another. Up to now, continental scale remote sensing studies failed to fill this gap. They mostly result in crude representations of the rainforest biome as a single, uniform vegetation class, in contrast with open vegetations. A few studies identified broad scale spatial patterns, but only when they managed to map a particular forest characteristic such as biomass. The main objective of this study is to identify, characterize and map distinct forest landscape types within the evergreen lowland rainforest at the sub-continental scale of the Guiana Shield (north-east tropical South-America 10° North-2° South; 66° West-50° West). This study is based on the analysis of a 1-year daily data set (from January 1st to December 31st, 2000) from the VEGETATION sensor onboard the SPOT-4 satellite (1-km spatial resolution). We interpreted remotely sensed landscape classes (RSLC) from field and high resolution remote sensing data of 21 sites in French Guiana. We cross-analyzed remote sensing data, field observations and environmental data using multivariate analysis. We obtained 33 remotely sensed landscape classes (RSLC) among which five forest-RSLC representing 78% of the forested area. The latter were classified as different broad forest landscape types according to a gradient of canopy openness. Their mapping revealed a new and meaningful broad-scale spatial pattern of forest landscape types. At the scale of the Guiana Shield, we observed a spatial patterns similarity between climatic and forest landscape types. The two most open forest-RSLCs were observed mainly within the north-west to south-east dry belt. The three other forest-RSLCs were observed in wetter and less anthropized areas, particularly in the newly recognized “Guianan dense forest arch”. Better management and conservation policies, as well as improvement of biological and ecological knowledge, require accurate and stable representations of the geographical components of ecosystems. Our results represent a decisive step in this way for the Guiana Shield area and contribute to fill one of the major shortfall in the knowledge of tropical forests.

CORINE land cover and floristic variation in a Mediterranean wetland

The aims of the present study were to: (1) investigate whether CORINE land cover classes reflect significant differences in floristic composition, using a very detailed CORINE land cover map (scale 1:5000); (2) decompose the relationships between floristic assemblages and three groups of explanatory variables (CORINE land cover classes, environmental characteristics and spatial structure) into unique and interactive components. Stratified sampling was used to select a set of 100-m(2) plots in each land cover class identified in the semi-natural wetland surrounding a lake in central Italy. The following six classes were considered: stable meadows, deciduous oak dominated woods, hygrophilous broadleaf dominated woods, heaths and shrublands, inland swamps, canals or watercourses. The relationship between land cover classes and floristic composition was tested using several statistical techniques in order to determine whether the results remained consistent with different procedures. The variation partitioning approach was applied to identify the relative importance of three groups of explanatory variables in relation to floristic variation. The most important predictor was land cover, which explained 20.7% of the variation in plant distribution, although the hypothesis that each land cover class could be associated with a particular floristic pattern was not verified. Multi Response Permutation Analysis did not indicate a strong floristic separability between land cover classes and only 9.5% of species showed a significant indicator value for a specific land cover class. We suggest that land cover classes linked with hygrophilous and herbaceous communities in a wetland may have floristic patterns that vary with fine scale and are not compatible with a land cover map.

An analysis of coastal topography and land cover changes at Haeundae Beach, South Korea

Environmental changes and increased development have led to sharp rises in coastal erosion. To understand and address this important social, environmental, and economic problem, a long-term data collection and monitoring system is necessary. This study analyzed changes in coastal topography and land cover for the past 60 years at Haeundae Beach, the most well-known beach in South Korea. First, aerial photographs were used to analyze the coastal topography. The coastline was extracted from digitized images, and tide levels were corrected using sounding and global positioning system (GPS) survey data. The beach area was then calculated and analyzed in terms of change in area over the 60-year study period. Second, land-cover changes were analyzed using detailed land-cover maps created by on-screen digitizing techniques and soil loss in the area near Haeundae Beach was estimated and compared with land use. Results showed that the coastline has moved farther inland and that in general the beach area has decreased. The beach area in 2005 was 35% less than that in 1947. The interception of the sand supply by artificial developments is thought to be a major cause of the coastal erosion. The study results can be applied to long-term coastal monitoring and environmental change projects and are expected to be available as part of a coastal information system.