Multi-resolution Imagery Research Articles

Advances in Aerial Image Exploitation

The availability of low cost sensors, high bandwidth communication links and the growing popularity of Unmanned Aerial Reconnaissance Platforms like UAVs have resulted in dramatic new opportunities to conduct remote battlefield surveillance and reconnaissance missions from relatively safe Ground Control Stations. The sensors used in UAV explore the visible, infrared and microwave regions of the spectrum and the images are produced optically, electronically or by film. Aerial Image Exploitation is an innovative image utilization program that uses multi sensor, multi resolution and multi spectral imagery obtained from Aerial Reconnaissance Platform for the purpose of Extraction, Exploitation, Dissemination and Interpretation of Imagery Intelligence. Research in aerial image exploitation is focused to provide a total solution of the problem by combining both hardware and software at the system and subsystem level. Major operational goal of image exploitation technology is to extract the vital intelligence from a large storehouse of available imagery by filtering those which are most likely to produce valuable findings, to combine classical image/signal processing techniques with technologies available in other fields and to use specialized hardware wherever required. The first part of the paper describes the Ground Image Exploitation System and Imagery Intelligence Exploitation System being developed in ADE. The second part of the paper describes some of the basic and advanced research work being carried out in ADE, which is the backbone that lead to the development of these systems. Some of the areas discussed in this paper are adaptive preprocessing of imagery, image mosaicing, terrain classification and high-resolution reconstruction of target region.

Assessment of the shoreline evolution using the CORONA declassified images. A case study in the north of Ilia prefecture, Peloponnese, Greece

A valuable archive of multiresolution imagery covering the period from 1960 to 1980 has been declassified by the United States of America (USA) government on February 22, 1995, and became available for sharing to the scientific community. This imagery is part of USA’s top-secret program with the codename “CORONA” that ran since the Cold War. We tried to assess the accuracy of the declassified datasets and estimate if they can be used in synergy with classical aerial photos to estimate the future shoreline position. Furthermore, their contribution to estimating the shoreline’s long-term changes using statistical tools such as the linear regression rate was examined. Two elongated coasts located in the Pinios and Andravida/Kyllini Municipalities located in the north of Ilias Prefecture, Peloponnese, Greece, were used as test sites. Two models were used that compared the actual and the forecasted. The results show that erosion prevails in the coastal area of the Pinios and Andravida/Kyllini municipalities with a mean EPR rate ranging from −0.16 to −0.43 m / y and from −0.18 to −0.31 m / y according to the actual and the forecasted models, respectively. Moreover, the respective maximum erosion rates are −1.15 and −1.57 m / y (actual model) and −2.04 and −1.89 m / y (forecasted model), whereas the maximum accretion rates are +0.76 and +1.02 m / y (actual model) and +1.68 and +2.00 m / y. The declination of the CORONA declassified imagery ranges from 2.63% to 6.62% in the Andravida/Kyllini territory and from 6.53% to 22.38% in the Pinios area. These results were cross-validated, using the R-squared rates, the root mean square error, and the mean annual error (MAE) statistical techniques. The MAE presented a better correlation as it provided equal rates (0.31 m / y) for both of the EPR datasets.

Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Analisis Perubahan Penggunaan Lahan Sawah dari Tahun 2008 – 2018

Paddy field is a natural resource and have an important role in maintaining the stability of the community's food (rice) production. Banyubiru District is one of the biggest contributors in supplying the rice needs of the people in Semarang Regency. So that the research aims to identify the extent and rate of change in the use of rice fields from 2008 to 2018 in Banyubiru District. The research approach used is based on geographic information systems in analyzing satellite images. The satellite imagery used is multi-resolution imagery such as Landsat 5 TM in 2008, Worldview-2 in 2013, and SPOT 6 in 2018. Image interpretation uses a pixel-based classification method with the maximum likelihood method and on-screen digitization found in ENVI 5.3 and ArcGis software. 10.8. The land use classification system used refers to following the Indonesian National Standard 7654: 201. The results of the study explained that there had been a change in the area of paddy land use in the Banyubiru District from 2008 to 2013 of 68 ha and from 2013 to 2018 of 65 ha. In addition, the rate of land use change from 2008 to 2018 was 14.78 ha / year, so this event can be categorized as a class of rapid land change rate.

Sub-pixel registration of multi-resolution imagery by correlation matching of the bathymetry-related features.

Multispectral imaging plays a significant role in coastal mapping and monitoring applications. For tasks involving the integration of multiple overlapped images, precise co-registration of the multisource satellite images is a crucial preliminary step. However, due to the limited terrestrial area and insufficient landscape features, the traditional methods become less efficient or even invalid in offshore island environments. This study addresses the problem by exploring the feasibility of using bathymetry information for geometric registration of satellite imagery. Instead of using the ground control points (GCPs) or extracting the tie points from the landscape features, the band ratio values are extracted from the multispectral images and are subsequently matched between different images through a correlation-based similarity measure. By searching the optimum correlation within the positioning uncertainty radius, the translation between two satellite images is estimated. Thus, the geometric inconsistency between the multispectral images of different sources and resolutions is effectively reduced. This result is obtained by using the ample bathymetry features without the aid of the GCPs and the in-situ bathymetry data. The experimental results using GeoEye-1, Sentinel-2, and Landsat-8 images at Ganquan Island show that for an island setting with a limited terrestrial area, the developed method achieves sub-pixel registration accuracy (less than 2 m) in planimetry. The effect of the nonlinearity and outliers are accounted for using the Spearman correlation measure. The improvement in image alignment enables the integration of multispectral images of different sources and resolutions for producing an accurate and consistent interpretation for coastal comparative and synergistic applications.

Multiresolution Satellite-Derived Bathymetry in Shallow Coral Reefs: Improving Linear Algorithms with Geographical Analysis

da Silveira, C.B.L.; Strenzel, G.M.R.; Maida, M.; Araujo, T.C.M., and Ferreira, B.P., 2020. Multiresolution satellite-derived bathymetry in shallow coral reefs: Improving linear algorithms with geographical analysis. Journal of Coastal Research, 36(6), 1247–1265. Coconut Creek (Florida), ISSN 0749-0208.Bathymetric maps are one of the first steps for most hydrological and ecological studies of the seascape, as depth is a determinant factor in the distribution of organisms, patterns of wave exposure, and coastal circulation. The coral reefs of Tamandare-Brazil, located at Costa dos Corais marine protected area (MPA), encompass a mosaic of interconnected habitats of complex geomorphology, including coral reefs, algal, and seagrass beds. These coastal habitats are subjected to chronic impacts such as sedimentation, reef erosion, and increasing human use, leading to habitat loss. Despite their social and ecological importance and the conservation measures in place, bathymetric and habitat maps of this coast are lacking. Indeed, in situ surveys are not always feasible in shallow coral reef areas. The present study offers a detailed bathymetric mapping of the area using multiresolution satellite imagery. One Landsat-8 (December 2016) and one WorldView-03 (February 2017) imagery were used to derive medium (30-m) and high-resolution (2-m) bathymetry of the study area. Single-beam echo sounder surveys were performed to obtain field data to calibrate the depth-retrieving algorithms: linear model, ratio band model, principal component analyses of the transformed bands, and geographically weighted regressions (GWR). For both resolution datasets, results showed that the algorithms' RMS were significantly improved by the GWR technique (RMS > 0.9) because of adaptability to the bottom heterogeneity found in complex areas such as coral reefs. Specific geomorphological reef zones were recognizable in the resulting bathymetric maps, such as intrareef lagoon, reef crest, fore-reef, and reef flat. This research concluded that affordable methods such as single-beam data coupled with satellite imagery through GWR can provide the required inputs for mapping shallow areas with complex relief. Such results may be further used to habitat mapping, necessary to inform the multiple-use zonation foreseen in MPA management plans.

Integrating Multiresolution and Multitemporal Sentinel-2 Imagery for Land-Cover Mapping in the Xiongan New Area, China

Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%).

Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach

Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000–2018 across all the investigated lakes.Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement.All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Remote sensing images have long been preferred to perform building damage assessments. The recently proposed methods to extract damaged regions from remote sensing imagery rely on convolutional neural networks (CNN). The common approach is to train a CNN independently considering each of the different resolution levels (satellite, aerial, and terrestrial) in a binary classification approach. In this regard, an ever-growing amount of multi-resolution imagery are being collected, but the current approaches use one single resolution as their input. The use of up/down-sampled images for training has been reported as beneficial for the image classification accuracy both in the computer vision and remote sensing domains. However, it is still unclear if such multi-resolution information can also be captured from images with different spatial resolutions such as imagery of the satellite and airborne (from both manned and unmanned platforms) resolutions. In this paper, three multi-resolution CNN feature fusion approaches are proposed and tested against two baseline (mono-resolution) methods to perform the image classification of building damages. Overall, the results show better accuracy and localization capabilities when fusing multi-resolution feature maps, specifically when these feature maps are merged and consider feature information from the intermediate layers of each of the resolution level networks. Nonetheless, these multi-resolution feature fusion approaches behaved differently considering each level of resolution. In the satellite and aerial (unmanned) cases, the improvements in the accuracy reached 2% while the accuracy improvements for the airborne (manned) case was marginal. The results were further confirmed by testing the approach for geographical transferability, in which the improvements between the baseline and multi-resolution experiments were overall maintained.

Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery

Despite rapid advances and large-scale initiatives in forest mapping, reliable cross-border information about the status of forest resources in Central Asian countries is lacking. We produced consistent Central Asia forest cover (CAFC) maps based on a cost-efficient approach using multi-resolution satellite imagery from Landsat and MODIS during 2009–2011. The spectral-temporal metrics derived from 2009–2011 Landsat imagery (overall accuracy of 0.83) was used to predict sub-pixel forest cover on the MODIS scale for 2010. Accuracy assessment confirmed the validity of MODIS-based forest cover map with a normalized root-mean-square error of 0.63. A general paucity of forest resources in post-Soviet Central Asia was indicated, with 1.24% of the region covered by forest. In comparison to the CAFC map, a regional map derived from MODIS Vegetation Continuous Fields tended to underestimate forest cover, while the Global Forest Change product matched well. The Global Forest Resources Assessments, based on individual country reports, overestimated forest cover by 1.5 to 147 times, particularly in the more arid countries of Turkmenistan and Uzbekistan. Multi-resolution imagery contributes to regionalized assessment of forest cover in the world’s drylands while developed CAFC maps (available at https://data.zef.de/) aim to facilitate decisions on biodiversity conservation and reforestation programs in Central Asia.

Spatiotemporal Variation in Suspended Sediment Concentrations and Related Factors of Coastal Waters Based on Multispatial Satellite Data in Gyeonggi Bay, Korea

ABSTRACT Eom, J.; Choi, J.-K.; Won, J.-S.; Ryu, J.-H.; Doxaran, D.; Ruddick, K., and Lee, S., 2017. Spatiotemporal variation in suspended sediment concentrations and related factors of coastal waters based on multispatial satellite data in Gyeonggi Bay, Korea. The variations in suspended sediment concentration (SSC) in turbid coastal waters around Gyeonggi Bay, Korea were analyzed using multiresolution ocean-color satellite imagery. Geostationary Ocean Color Imager (GOCI) and Land Satellite 7 (Landsat-7) Enhanced Thematic Mapper Plus (ETM+) images were atmospherically corrected, and an empirical algorithm was employed to generate maps of SSCs in the study area and investigate daily and annual variabilities in coastal water turbidity. SSC values were highest around 6 hours before high tide and around low tide, and the maximum values had strong positive relations with the tidal range near the sand ridge and channel (R2 values of 0.74 and 0.72, respectively), which implies that the main driver of the diurnal...

Multiresolution Supervised Classification of Panchromatic and Multispectral Images by Markov Random Fields and Graph Cuts

The problem of supervised classification of multiresolution images, which are composed of a higher resolution panchromatic channel and of several coarser resolution multispectral channels, is addressed in this paper by proposing a novel contextual method based on Markov random fields. The method iteratively exploits a linear mixture model for the relationships between data at different resolutions and a graph cut approach to Markovian energy minimization to generate a contextual classification map at the highest resolution available in the input data set. The estimation of the parameters of the method is performed by extending recently proposed techniques based on the expectation-maximization and Ho–Kashyap's algorithms. The method is experimentally validated with semisimulated and real data involving both IKONOS and Landsat-7 ETM+ images, and the results are compared with those generated by previous approaches to the classification of multiresolution imagery.

Soil Phosphorus and Nitrogen Predictions Across Spatial Escalating Scales in an Aquatic Ecosystem Using Remote Sensing Images

The incorporation of remote sensing (RS) data into digital soil models has shown success to improve soil predictions. However, the effects of multiresolution imagery on modeling of biogeochemical soil properties in aquatic ecosystems are still poorly understood. The objectives of this study were the following: 1) to develop prediction models for soil total phosphorus (TP) and total nitrogen (TN) utilizing RS images and environmental ancillary data at three different resolutions; 2) to identify controlling factors of the spatial distribution of soil TP and TN; and 3) to elucidate the effects of different spatial resolutions of RS images on inferential modeling. Soil cores were collected $(n=108)$ from the top 10 cm in a subtropical wetland: Water Conservation Area-2A, the Florida Everglades, USA. The spectral data and derived indices from RS images, which have different spatial resolutions, included the following: MODIS (500 m resampled to 250 m), Landsat ETM+ (30 m), and SPOT (10 m). Block kriging and random forest (RF) were employed to predict soil TP and TN using RS-image-derived spectral input variables, environmental ancillary data, and soil observations. The RF models showed $R^{2}$ between 0.90 and 0.93 and root mean square error between 100.4 and 115.9 $\hbox{mg}\cdot\hbox{kg}^{-1}$ for TP and between 1.45 and 1.52 $\hbox{g}\cdot\hbox{kg}^{-1}$ for TN. Soil TP was mainly predicted from RS-derived spectral indices that infer on biotic/vegetation characteristics, whereas soil TN was predicted using a combination of biotic/vegetation, topographic, and hydrologic variables. Results suggest that the spectral data informed soil models have excellent predictive capabilities in this aquatic ecosystem. Interestingly, there was no noticeable distinction among different spatial resolutions of RS images to develop prediction models for soil TP and TN in terms of error assessment. However, the variability and complexity of soil TP and TN variations were much better expressed with the finer resolution ${\rm RF}_{\rm SPOT}$ model than the coarser resolution ${\rm RF}_{\rm MODIS}$ model as demonstrated using entropy.

Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey

This research investigates the accuracy of pixel- and object-based classifi cation techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to fithe optimum data source for the identifi cation of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classifi cation (OBC). Post-classifi cation methods were applied to the output of pixel-based classifi cation to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classifi cation. OBC results provided comparatively the best performance for both parcel identifi cation and area estimation at 10 m and fi ner spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identifi cation and area estimation. In general, parcel identifi cation effi ciency was strongly correlated with spatial resolution while the classifi cation algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classifi cation algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns.

Invasive Saltcedar (Tamarisk spp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery

Saltcedar is commonly recognized as one of the most threatening invasive species in the United States and has the potential to cause great environmental harm over the coming decade. Accurate mapping of saltcedar distribution and abundance in a timely manner plays a central role in assisting with effective control. Current studies have mostly concentrated on large-area detection with coarse-resolution remote sensing data. In this study, a comprehensive test was designed and carried out to examine the ability to integrate multitemporal and multiresolution imagery for differentiating saltcedar from other riparian vegetation types in the Rio Grande basin of Texas, including very high spatial resolution (QuickBird), hyperspectral resolution imagery (AISA), and moderate resolution satellite imagery (Landsat TM). Two types of analyses were fulfilled. First, five pixel-based classification methods were adopted for assessing the effectiveness of QuickBird and AISA for discerning saltcedar, respectively; that is, the maximum likelihood classifier (MLC), neural network classifier (NNC), support vector machine (SVM), spectral angle mapper (SAM), and maximum matching feature (MMF). Second, Landsat TM imagery was synthesized from AISA and tested for mapping the abundance of saltcedar with four linear spectral unmixing methods and three back-propagation neural network methods. Results indicate that AISA outperformed QuickBird imagery in differentiating saltcedar from other riparian vegetation species. SVM achieved the highest classification accuracy among the five classifiers. Linear spectral unmixing methods exhibited similar mapping accuracy to neural network methods in estimating the abundance of saltcedar at a spatial resolution of 30 by 30 m2 but with significantly better computing efficiency.

Linear features for automatic registration and reliable change detection of multi-source imagery

Change detection analysis usually involves multi-spectral, multi-source and/or multi-resolution imagery that has been captured at different times. Accurate co-registration of these datasets is a prerequisite step for a reliable change detection procedure. This paper introduces a new approach for automatic image registration using the Modified Iterated Hough Transform (MIHT) and linear features, which have been chosen since they can be reliably extracted from imagery with varying geometric and radiometric properties. Finally, edge detection and subtraction of the registered images are used as the basis for the change-detection procedure. Experimental results from real data proved the feasibility of the suggested approach.

Dynamics of a Coupled System: Multi-Resolution Remote Sensing in Assessing Social-Ecological Responses during 25 Years of Gas Field Development in Arctic Russia

Hydrocarbon exploration has been underway in the north of West Siberia for several decades. Giant gas fields on the Yamal Peninsula are expected to begin feeding the Nord Stream pipeline to Western Europe in late 2012. Employing a variety of high- to very high-resolution satellite-based sensors, we have followed the establishment and spread of Bovanenkovo, the biggest and first field to be developed. Extensive onsite field observations and measurements of land use and land cover changes since 1985 have been combined with intensive participant observation in all seasons among indigenous Nenets reindeer herders and long-term gas field workers during 2004–2007 and 2010–2011. Time series and multi-resolution imagery was used to build a chronology of the gas field’s development. Large areas of partially or totally denuded tundra and most forms of expanding infrastructure are readily tracked with Landsat scenes (1985, 1988, 2000, 2009, 2011). SPOT (1993, 1998) and ASTER (2001) were also used. Quickbird-2 (2004) and GeoEye (2010) were most successful in detecting small-scale anthropogenic disturbances as well as individual camps of nomadic herders moving in the vicinity of the gas field. For assessing gas field development the best results are obtained by combining lower resolution with Very High Resolution (VHR) imagery (spatial resolution < 5 m) and fieldwork. Nenets managing collective and privately owned herds of reindeer have proven adept in responding to a broad range of intensifying industrial impacts at the same time as they have been dealing with symptoms of a warming climate. Here we detail both the spatial extent of gas field growth and the dynamic relationship between Nenets nomads and their rapidly evolving social-ecological system.

Identifying Spatial Units of Human Occupation in the Brazilian Amazon Using Landsat and CBERS Multi-Resolution Imagery

Every spatial unit of human occupation is part of a network structuring an extensive process of urbanization in the Amazon territory. Multi-resolution remote sensing data were used to identify and map human presence and activities in the Sustainable Forest District of Cuiabá-Santarém highway (BR-163), west of Pará, Brazil. The limits of spatial units of human occupation were mapped based on digital classification of Landsat-TM5 (Thematic Mapper 5) image (30m spatial resolution). High-spatial-resolution CBERS-HRC (China-Brazil Earth Resources Satellite-High-Resolution Camera) images (5 m) merged with CBERS-CCD (Charge Coupled Device) images (20 m) were used to map spatial arrangements inside each populated unit, describing intra-urban characteristics. Fieldwork data validated and refined the classification maps that supported the categorization of the units. A total of 133 spatial units were individualized, comprising population centers as municipal seats, villages and communities, and units of human activities, such as sawmills, farmhouses, landing strips, etc. From the high-resolution analysis, 32 population centers were grouped in four categories, described according to their level of urbanization and spatial organization as: structured, recent, established and dependent on connectivity. This multi-resolution approach provided spatial information about the urbanization process and organization of the territory. It may be extended into other areas or be further used to devise a monitoring system, contributing to the discussion of public policy priorities for sustainable development in the Amazon.

Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery1

Claggett, Peter R., Judy A. Okay, and Stephen V. Stehman, 2010. Monitoring Regional Riparian Forest Cover Change Using Stratified Sampling and Multiresolution Imagery. Journal of the American Water Resources Association (JAWRA) 46(2):334‐343. DOI: 10.1111/j.1752‐1688.2010.00424.xAbstract: The Chesapeake Bay watershed encompasses 165,760 km2 of land area with 464,098 km of rivers and streams. As part of the Chesapeake Bay restoration effort, state and federal partners have committed to restoring 26,000 miles (41,843 km) of riparian forest buffers. Monitoring trends in riparian forest buffers over large areas is necessary to evaluate the efficacy of these restoration efforts. A sampling approach for estimating change in riparian forest cover from 1993/1994 to 2005 was developed and implemented in Anne Arundel County, Maryland, to exemplify a method that could be applied throughout the Bay watershed. All stream reaches in the county were stratified using forest cover change derived from Landsat imagery. A stratified random sample of 219 reaches was selected and forest cover change within the riparian buffer of each sampled reach was interpreted from high‐resolution aerial photography. The estimated footprint of gross change in riparian forest cover (i.e., the sum of gross gain and gross loss) for the county was 1.83% (SE = 0.22%). Stratified sampling taking advantage of a priori knowledge of locations of change proved to be a practical and efficient protocol for estimating riparian forest buffer change at the county scale and the protocol would readily extend to much broader scale monitoring.

Interactive, Internet Delivery of Visualization via Structured Prerendered Multiresolution Imagery

We present a novel approach for latency-tolerant delivery of visualization and rendering results where client-side frame rate display performance is independent of source dataset size, image size, visualization technique or rendering complexity. Our approach delivers pre-rendered, multiresolution images to a remote user as they navigate through different viewpoints, visualization or rendering parameters. We employ demand-driven tiled, multiresolution image streaming and prefetching to efficiently utilize available bandwidth while providing the maximum resolution user can perceive from a given viewpoint. Since image data is the only input to our system, our approach is generally applicable to all visualization and graphics rendering applications capable of generating image files in an ordered fashion. In our implementation, a normal web server provides on-demand images to a remote custom client application, which uses client-pull to obtain and cache only those images required to fulfill the interaction needs. The main contributions of this work are: (1) an architecture for latency-tolerant, remote delivery of precomputed imagery suitable for use with any visualization or rendering application capable of producing images in an ordered fashion; (2) a performance study showing the impact of diverse network environments and different tunable system parameters on end-to-end system performance in terms of deliverable frames per second.