Resolution Aerial Photographs Research Articles

Channel–floodplain connectivity during an extreme flood event: implications for sediment erosion, deposition, and delivery

ABSTRACTThe term connectivity has emerged as a powerful concept in hydrology and geomorphology and is emerging as an innovative component of catchment erosion modeling studies. However, considerable confusion remains regarding its definition and quantification, especially as it relates to fluvial systems. This confusion is exacerbated by a lack of detailed case studies and by the tendency to treat water and sediment separately. Extreme flood events provide a useful framework to assess variability in connectivity, particularly the connection between channels and floodplains. The catastrophic flood of January 2011 in the Lockyer valley, southeast Queensland, Australia provides an opportunity to examine this dimension in some detail and to determine how these dynamics operate under high flow regimes. High resolution aerial photographs and multi‐temporal LiDAR digital elevation models (DEMs), coupled with hydrological modeling, are used to assess both the nature of hydrologic and sedimentological connectivity and their dominant controls. Longitudinal variations in flood inundation extent led to the identification of nine reaches which displayed varying channel–floodplain connectivity. The major control on connectivity was significant non‐linear changes in channel capacity due to the presence of notable macrochannels which contained a > 3000 average recurrence interval (ARI) event at mid‐catchment locations. The spatial pattern of hydrological connectivity was not straight‐forward in spite of bankfull discharges for selected reaches exceeding 5600 m3s–1. Data indicate that the main channel boundary was the dominant source of sediment while the floodplains, where inundated, were the dominant sinks. Spatial variability in channel–floodplain hydrological connectivity leads to dis‐connectivity in the downstream transfer of sediments between reaches and affected sediment storage on adjacent floodplains. Consideration of such variability for even the most extreme flood events, highlights the need to carefully consider non‐linear changes in key variables such as channel capacity and flood conveyance in the development of a quantitative ‘connectivity index’. Copyright © 2013 John Wiley & Sons, Ltd.

Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed)

Plant invasions represent a threat not only to biodiversity and ecosystem functioning but also to the character of traditional landscapes. Despite the worldwide efforts to control and eradicate invasive species, their menace grows. New techniques enabling fast and precise monitoring and providing information on spatial structure of invasions are needed for efficient management strategies to be implemented. We present remote sensing assessment of a noxious invasive species Heracleum mantegazzianum (giant hogweed) that integrates different data sources, spatial and spectral resolutions, and image processing techniques. Panchromatic, multispectral and color very high spatial resolution (VHR) aerial photography (1947–2006, resolution 0.5m), and medium spatial resolution satellite data (Rapid Eye 2010, resolution 5m) were analyzed to assess their potential for hogweed monitoring by using pixel- (both supervised and unsupervised) and object-based image analysis (OBIA, automated hierarchical, iterative, and rule-based). Both point and grid based accuracy assessment was carried out. Described methods of object-based image analysis of VHR data enabled monitoring of hogweed at high classification accuracies measured by various means, regardless of the spectral resolution of the data provided that the data came from the species flowering period. Although the proposed automated processing of VHR data is relatively time-effective and standardized, application over large areas would be rather demanding due to the size of datasets, and multispectral satellite data of medium spatial resolution (lower than the size of individuals) was therefore tested. On such imagery, only larger stands could be identified but still the pixel-based supervised classification achieved moderate accuracy. Depending on the size of the area of interest and the detail needed the very high or medium spatial resolution data (acquired at the species flowering period) are to be used. High accuracies achieved for VHR data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparable precise, fast and efficient.

Modelling the invasion of a Central American Mimosoid tree species (Leucaena leucocephala) in a tropical coastal region of Taiwan

Assessing the progress of alien plant invasion is pivotal for weed management. In this study, we develop a spatially explicit logistic regression imbedded cellular automaton (CA)-based species expansion model to estimate the invasion of a Central American Mimosoid tree Leucaena leucocephala (LELE) in a tropical coastal region of Taiwan. We delineated the LELE infested areas in 1988, 1997 and 2007 using the Satellite Pour L'observation de la Terre (SPOT), and refined the classifications using high spatial resolution aerial photographs or Formosat-2 satellite images (Center for Space and Remote Sensing Research, Taiwan). Dynamics of LELE by referring to 1988–1997 LELE spatial distributions were ingested into a logistic model to build transition rules for CA simulation. The results showed that the enhanced CA model can precisely (95% overall accuracy by comparing with the 2007 LELE distribution) model the population expansion of LELE. This study highlights the strength of this spatially explicit predictive model, and sheds the light on the management of alien invasive species management.

Automated Classification of Urban Areas for Storm Water Management Using Aerial Photography and LiDAR

AbstractDuring urbanization, undisturbed land surfaces are altered to create manufactured landscapes. Classifications of these new urban surfaces are utilized in urban planning, environmental monitoring, and other applications such as storm water management and roof runoff harvesting system design. To evaluate runoff volume and design storm water control devices, areas of different urban surfaces need to be identified and defined as pervious (e.g., undisturbed soils and landscaped areas) and impervious surfaces (e.g., roofs, roads, parking lots, sidewalks, driveways). This study presents a means to facilitate urban surface classification and quantification by analyzing high resolution aerial photographs in conjunction with light detection and ranging (LiDAR) data in a custom application for the geographic information system software. This software processes aerial photographs using red/green/blue (RGB) bands to produce a raster with saturation (S) values. In parallel, LiDAR data are used to distinguish th...

Land transitions in the American plains: Multilevel modeling of drivers of grassland conversion (1956–2006)

This paper examines drivers of land-cover change in the U.S. Great Plains in the last half of the twentieth century. Its central aim is to evaluate the dynamics of grassland preservation and conversion, across the region, and to identify areas of grassland that were never plowed during the period. The research compares land-cover data from 400 sample areas, selected from and nested within 50 counties, to aggregate data from the agricultural and population censuses. The spatially explicit land-cover data were interpreted from aerial photographs taken at three time points (1950s, 1970s and 2000s). Sample areas were chosen using a stratified random design based on the Public Land Survey grid with in the target counties, in several clusters across the region. We modeled the sequences and magnitudes of changes in the interpreted air photo data in a multi-level panel model that included soil quality and slope of sample areas and agricultural activities and employment reported in the U.S. Censuses of Agriculture and Population. We conclude that land retirement programs and production subsidies have worked at cross purposes, destabilizing micro-level patterns of land use in recent decades, increasing levels of switching between cropland and grassland and reducing the size of remaining areas of native grassland in the U.S. Great Plains.

Крупномасштабная карта растительности ключевого участка в лесотундре Кольского полуострова: методические особенности составления и анализ

Large-scale vegetation mapping of key area of 1400 km2 in forest-tundra zone in the middle flow of Teriberkariver (Kola Peninsula) was conducted on the base of satellite images, topographic maps and field geobotanical investigations. Study of Landsat TM images local spectral characteristics and syntaxonomical analysis of vegetation demonstrated that satellite images with 30 m spatial resolution and 6-bands spectral resolution alone can't be used for automatical large-scale classification of forest-tundra zone vegetation. Map of vegetation in scale 1:50 000 resulted from manual processing of both hypsometry and spectral characteristic, on the base of field work points with description of vegetation. Legend of mapped units contains 2 types for tundra, 5 ones for mountain birch forest and 4 ones for wetlands. Quality and correctness of the map were verified by satellite image of extra high spatial resolution Quick Bird (0,65 m/pixel) and aerial photograph for this territory. The map illustrated that distribution of vegetation is primarily caused by unevenness of relief. Timberline lies at 220-240 m a.s.l., altitudinal distances across mountain birch forest and tundra zones are about 40-60 m, tundra vegetation occurs on moraine hills and bedrocks slopes and summits. Pounikkos flark-and-mounds, tuft-and-fen bog complexes occur from 170 to 240 m.s.l. in depressions and valleys. Proportion of tundra vegetation is about 40%, mountain birch forest – 30%, wetlands – 20%, lakes, rivers and springs take 10% of area investigated.

Spatial Analysis of High Resolution Aerial Photographs to Analyze the Spread of Mountain Pine Beetle Infestations

Mountain pine beetle (Dendroctonus Ponderosae) infestations in Western Canada have reached alarming proportions. The spread of attacks has significantly impacted pine tree stocks, the forest ecosystem in general and the overall socio-economic condition of residents in communities that depend on the forest industry. The ability to track these attacks and anticipate their trajectories will allow forest managers to target intervention measures and improve mitigation efforts. Spatial analysis methods are well suited to characterize the dynamics of the mountain pine beetle attacks. This study implemented a multi-scale spatiotemporal analysis to describe the patterns of three consecutive attack years (2000-2002) for a 19 km2 study site in British Columbia, Canada. In the first stage, baseline complete spatial randomness (CSR) was examined using average nearest neighbour analysis and quadrat counts on infestation data extracted from 19 cm resolution black and white aerial photographs. In the second stage, global and local Moran’s I spatial autocorrelation statistics, Getis-Ord General G global high/low clustering and Getis-Ord G_i^* test statistic techniques were used to evaluate the spread patterns over time. The results show a significant clustering of attack sites in the first two years, with considerable changes in the attack spread in the third year. These changes were independent of the two levels of spatial resolution used. Knowledge about infestation patterns can be used to anticipate needed social and environmental assistance for affected communities. Further, the information can aid overall sustainable forestry management.

Tidal channel distribution in relation to surface sedimentary facies based on remotely sensed data

Tidal channel networks are important factors influencing the morphodynamics of tidal flats and surface sedimentary facies. Here we investigate the relationship between channel distribution and sedimentary facies in Geunso Bay tidal flat, Korea. The tidal channel networks were extracted from a high spatial resolution aerial photograph, and for each sedimentary facies the pattern of tidal channel distribution was compared in terms of fractal analysis, channel density, and distance from the channel. The tidal channels in each sediment facies had relatively constant meandering patterns, but the density and complexity were distinguishable. The second fractal dimension was 1.87 in the mud flat, 1.41 in the mixed flat, and about 1.30 in the sand flat. The channel density was 0.036–0.06 m/m2 in the mud flat and 0–0.024 m/m2 in the mixed and sand flat areas. This implies that the tidal channels in the mud flat area represent a complex and dendritic pattern with high density compared to those in the mixed or sand flat areas. The results were used to test the applicability of adjusting sedimentary facies classification generated from interpolation of survey data. We quantitatively estimated the pattern of tidal channel distribution for each sedimentary facies based on a high spatial resolution aerial photograph. We suggest that tidal channel network features can be useful to surface sedimentary facies classification in tidal flats.

Mapping land cover in urban residential landscapes using very high spatial resolution aerial photographs

Accurate information on existing residential landscapes is essential for framing ordinances and monitoring residential water use in the Urban Greenspace Ecosystem. We classified residential landscapes of New Mexico's largest city, Albuquerque, to explore the spatial distribution of residential greenspace and its composition among zip codes and median incomes. Geographic Information System (GIS) vector files including parcels, city limits, zip codes and land-use maps, were integrated with ownership information. The database was stratified by Albuquerque's 16 zip codes. Four hundred eighty residential landscapes were selected randomly for study. Very high spatial resolution (0.15m) 2008 true color aerial photographs and the object-oriented supervised classification module in ENVI EX were used to identify residential features. Spatial and textural variables, created by image segmentation, were classified using the K-Nearest Neighbor (K-NN) algorithm embedded in ENVI EX. Classification accuracy was 89%. Larger greenspace, tree, shrub, and grass areas were in larger parcels. Landscapes in lower income groups and older zip codes include larger greenspace and tree cover because of mature tree sizes, while grass dominated landscapes of higher income groups and newer zip codes. This knowledge of residential vegetation distribution could serve as a basis for policy makers, planners, and water conservation officers wishing to enact ordinances and regulations that govern the urban residential landscape.

Coral colonisation of a shallow reef flat in response to rising sea level: quantification from 35 years of remote sensing data at Heron Island, Australia

Observations made on Heron Island reef flat during the 1970s–1990s highlighted the importance of rapid change in hydrodynamics and accommodation space for coral development. Between the 1940s and the 1990s, the minimum reef-flat top water level varied by some tens of centimetres, successively down then up, in rapid response to local engineering works. Coral growth followed sea-level variations and was quantified here for several coral communities using horizontal two-dimensional above water remotely sensed observations. This required seven high spatial resolution aerial photographs and Quickbird satellite images spanning 35 years: 1972, 1979, 1990, 1992, 2002, 2006 and 2007. The coral growth dynamics followed four regimes corresponding to artificially induced changes in sea levels: 1972–1979 (lowest growth rate): no detectable coral development, due to high tidal currents and minimum mean low-tide water level; 1979–1991 (higher growth rate): horizontal coral development promoted by calmer hydrodynamic conditions; 1991–2001(lower growth rate): vertical coral development, induced by increased local sea level by ~12 cm due to construction of new bund walls; 2001–2007 (highest growth rate): horizontal coral development after that vertical growth had become limited by sea level. This unique time-series displays a succession of ecological stage comprising a ‘catch-up’ dynamic in response to a rapid local sea-level rise in spite of the occurrences of the most severe bleaching events on record (1998, 2002) and the decreasing calcification rates reported in massive corals in the northern part of the Great Barrier Reef.

UAS remote sensing missions for rangeland applications

Rangelands cover about 50% of the earth's land surface, are in remote areas and have low population densities, all of which provide an ideal opportunity for remote sensing applications from unmanned aircraft systems (UAS). In this article, we describe a proven workflow for UAS-based remote sensing, and discuss geometric errors of image mosaics and classification accuracies at different levels of detail. We report on several UAS missions over rangelands in Idaho and New Mexico, USA, where we acquired 6–8 cm resolution aerial photography and concurrent field measurements. The geometric accuracies of the image mosaics were in the 1–2 m range, and overall classification accuracies for vegetation maps ranged from 78–92%. Despite current FAA regulations that restrict UAS operations to distances within line-of-sight of the UAS, our results show that UAS are a viable platform for obtaining very high-resolution remote sensing products for applied vegetation mapping of rangelands.

하천 현장업무 의사지원을 위한 GNSS와 공간영상 활용방안에 관한 연구

본 연구는 4대 강 전체에 대한 현황을 파악하고 행정적 관리체계 지원을 위하여 첨단 공간정보기술인 GIS(Geographic Information System), RS(Remote Sensing), GNSS(Global Navigation Satellite System), 항공레이저측량(LiDAR)과 실시간 네트워크 기술을 융합하여 3차원 영상 GIS기반 하천현장 정보시스템을 개발하였다. 3차원 영상 GIS기반 하천 현장 정보시스템은 25cm의 고해상 항공사진과 항공레이저측량 및 수심측량자료를 획득하여 4대강 중 선도 사업지역인 영산강(榮山江) 전체에 대한 정밀 공간정보를 표출하고 GNSS를 이용한 실시간 이동물체에 대한 위치좌표 트래킹기법과 연계하여 헬기, 배, 버스 등 이동수단 상에서 현장을 모니터링을 할 수 있는 기법을 개발하였다. 이를 통하여 4대강 현장에 대한 모든 정보를 한눈에 모니터링 할 수 있어 넓은 현장에 대한 사실적 설명과 국민적 신뢰성 확보 뿐만 아니라 4대 강 살리기 사업의 홍보 및 보고를 통하여 하천 현장업무상의 의사지원 체계 마련이 가능할 것으로 사료된다. This Study has developed the information system of the rivers based on 3D image GIS by converging the latest information technology of GIS(Geographic Information System), RS(Remote Sensing), GNSS(Global Navigation Satellite System), aerial laser survey(LiDAR) with real time network technology in order to understand the current situation of all the four major rivers and support the administrative management system. The said information system acquires the high resolution aerial photographs of 25cm, aerial laser survey and water depth surveying data to express precise space information on the whole Youngsan River which is the leading project site out of the four river sites. Monitoring the site is made available on the transporting means such as a helicopter, boat or a bus in connection with locational coordinate tracking skill for the moving objects in real time using GNSS. It makes monitoring all the information on the four river job sites available at a glance, which can obtain the reliability of the people to such vast areas along with enhancing the recognition of the people by publicity of four Rivers Revitalizing Project and reports thereof.

Multi sensor data fusion for change detection

When performing post-classification comparison using images of different sensors, change detection is still possible even if images have different resolutions. However, in this case, change pixels are detected in the pixel size of coarser resolution image. This problem can be solved using higher resolution aerial photographs or panchromatic images if available. In multi sensor image fusion, images of higher resolution can be used to increase the resolutions of the multispectral images. In this study IKONOS panchromatic (1 m) and multispectral bands (4 m), taken in 2003, is fused using a trous image fusion algorithm to get 1 m multispectral image. Analogously, 1 m resolution aerial photograph (degraded down from its original 0.5 m resolution) and 30 m resolution ETM+ images, both taken in 2000, are fused to get 1 m resolution ETM+ multispectral image. Once both multispectral image data has 1 m resolution, post-classification comparison method is applied to detect changes occurred on the coastal zone in city of Trabzon. Preliminary results show that a trous fusion algorithm could keep the original spectral content of multispectral images in the fused products. Overall classification accuracies for fused ETM+ and IKONOS images are obtained as 92.40 and 95.2%, respectively. The area of filled earth on costal zone due to highway construction is detected as 186023 m2, in 1 m2 precision. Key words: Data fusion, change detection, a trous algorithm, aerial photograph, multispectral image.

Generating land cover boundaries from remotely sensed data using object-based image analysis: Overview and epidemiological application

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1m) and a medium resolution Landsat image (30m) time series in the context of a pesticide spray drift exposure application.

Landscape dynamics at the public–private interface: A case study in Colorado

The Rocky Mountain West is experiencing rapid land use change in the form of residential development. Public land managers, like the U.S. Forest Service, Bureau of Land Management, and National Park Service are increasingly concerned with ecological and management implications of private land use adjacent to public lands. Using fine-grain land use data, collected from high resolution aerial photographs, we quantify human land use composition, patterns, and trends at the interface of public and private land in the Southern Rocky Mountain Ecoregion (SRE) of Colorado. We developed two metrics for assessing land use impacts. The first quantifies the amount of land cover modified (HM c) by human land use and the second estimates the amount of wildlife habitat that is functionally modified (HM fd). In 2005, we estimate that human land use modifies 8% of the total land cover in the Colorado SRE and that 13% of the study area was functionally modified. Agriculture had the greatest impact in terms of area of modified land cover (282,305 ha). Residential (exurban) land use had a smaller footprint (HM c) but the close proximity to public lands, dispersed spatial pattern (high HM fd), and rapid increase in rural and exurban housing density observed in the SRE have important implications. This research demonstrates how fine scale land cover data can be used to examine land use pattern and composition as well as track land use change.

Object Classification of Aerial Images With Bag-of-Visual Words

This letter presents a Bag-of-Visual Words (BOV) representation for object-based classification in land-use/cover mapping of high spatial resolution aerial photograph. The method is introduced to handle the special characteristics of aerial images, i.e., variability of spectral and spatial content. Specifically, patch detection and description are used to divide and represent various subregions of objects comprising multiple homogeneous components. Moreover, the BOV representation is constructed with the statistics of the occurrence of visual words, which are learned from the training data set. A combination of spectral and texture features is verified to be a satisfactory choice through the evaluations of various patch descriptors. Furthermore, a threshold-based method is employed to reduce the impact of outliers on classification in test data. Experiments based on aerial-image data set show that the proposed BOV representation yields better classification performance than the low-level features, such as the spectral and texture features.

Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps

AbstractIn recent years, fluvial remote sensing has seen considerable progress in terms of methods capable of system scale characterisation of river catchments. One key development is automated grain size mapping. It has been shown that high resolution aerial photography can be used to automatically produce grain size maps over entire rivers. However, current aerial grain size mapping procedures all require field calibration data. The collection of such data can be costly and problematic in the case of remote areas. This paper presents a method developed to remove the need for field based calibration data. Called ‘aerial photosieving’, this method consists of using the same very high resolution aerial imagery intended for grain size map production to visually measure particle sizes on‐screen in order to provide calibration data. The paper presents a rigorous comparison of field‐based photosieving calibration data and aerial photosieving calibration data. Statistical tests are used to demonstrate that aerial photosieving gives similar results when compared with field‐based data with only a slight systematic overprediction. The new aerial photosieving method therefore simplifies the overall procedure required for the production of grain size maps and thus improves the cost‐effectiveness and potential availability of this new fluvial remote sensing technology. Copyright © 2010 John Wiley &Sons, Ltd.

Individual Object Change Detection for Monitoring the Impact of a Forest Pathogen on a Hardwood Forest

Sudden oak death (SOD) has caused widespread mortality in a number of tree and shrub species throughout coastal California. As a result, canopy changes are directly visible from remotely sensed imagery. To quantify changes in horizontal canopy structure to the oak woodlands in China Camp State Park, California, USA, a heavily hit area, we developed a novel change detection technique that tracks changes to individual objects. Using 4-band, 1 m spatial resolution aerial photography, we classified four annual images (2000 to 2003) with object-based image analysis (OBIA) and employed a GIS for our change detection technique. We identified 352 gaps that contained SOD mortality in 2000 and persisted through 2003. Their median areas and perimeters did not change significantly in that time. However, those gaps that increased in size tended to be smaller than those that decreased, indicating increased mortality in newly infected areas. Our new change detection method allowed us to monitor these gaps one-by-one, revealing ecologically meaningful results that would otherwise be obscured in a landscape-scale analysis.

Genesis of active sand‐filled polygons in lower and central Beacon Valley, Antarctica

AbstractNonsorted polygons with sand‐filled wedges were investigated in Beacon Valley, Antarctica (77.82°S, 160.67°E) using field observations coupled with 2‐m resolution aerial photography. A gasoline‐powered concrete breaker was employed to expose the sediments of four polygon centres and six wedges from geomorphic surfaces containing tills of two different ages. The excavated polygons ranged from 9 to 16 m in diameter; the sand‐filled wedges ranged from 0.2 to 2.5 m in width. The top of ice‐bonded permafrost ranged from 12 to 62 cm in depth in the polygon centres and from 64 to >90 cm in wedges. One active thermal‐contraction fissure generally was apparent at the surface, but excavations revealed numerous inactive fissures. The wedges contain sand laminations averaging 3 mm in width when viewed in cross‐section. Although most of the polygons were of the sand‐wedge type, some contained ice veins up to 1 cm in width and could be classed as composite wedges. Three stages of polygon development were observed, including well‐developed polygons on Taylor II surfaces (ca. 117 ka), moderately developed polygons on Taylor III surfaces (ca. 200 ka) and poorly expressed polygons on Taylor IVa and older (ca. >1.1 Ma) surfaces. This retrogressive development may be due to sublimation of ice‐bonded permafrost following thermal cracking. With the drop in ice content, the thermal coefficient of expansion of the permafrost may be lowered, which would result in a reduction in tensile stresses. Copyright © 2009 John Wiley & Sons, Ltd.

Co-registration and correlation of aerial photographs for ground deformation measurements

We describe and test a procedure to accurately co-register and correlate multi-temporal aerial images. We show that this procedure can be used to measure surface deformation, and explore the performance and limitations of the technique. The algorithms were implemented in a software package, COSI-Corr (available from the Caltech Tectonics Observatory website). The technique is validated on several case examples of co-seismic deformation. First, we measure co-seismic ground deformation due to the 1992, Mw 7.3, Landers, California, earthquake from 1 m resolution aerial photography of the National Aerial Photography Program (United States Geological Survey). The fault ruptures are clearly detected, including small kilometric segments with fault slip as small as a few tens of centimeters. We also obtained similar performance from images of the fault ruptures produced by the 1999 Mw 7.1 Hector Mine, California, earthquake. The measurements are shown to be biased due to the inaccuracy of the Digital Elevation Model, film distortions, scanning artifacts, and ignorance of ground displacements at the location of the tie points used to co-register the multi-temporal images. We show that some of these artifacts can be identified and corrected.