Digital Orthophoto Quadrangles Research Articles

Changes in the vegetation of New Orleans from the disturbance impacts of Hurricane Katrina in 2005

The catastrophic flooding of New Orleans following landfall of Hurricane Katrina in August of 2005 left behind a unique urban environment for investigating disaster impacts on vegetation disturbance and recovery. The goal of this study was to analyze the changes in urban tree canopy cover (both live standing and downed) and the extent of vegetation as shrubs or herbaceous cover on blocks located within the most flood-impacted neighborhoods in 2005 of New Orleans, down to the scale of individual property lots. Remote sensing aircraft images from the U. S. Geological Survey's Digital Orthophoto Quadrangles (DOQs) imagery and the National Agriculture Imagery Program (NAIP) were used to generate high-resolution (1-m) vegetation cover maps for 2004, 2005 (post-Katrina), and 2019 for deeply flooded neighborhoods from 2005. Segmentation and object-based classification applied to all color infrared (CIR) imagery was used to generate and compare high-resolution maps of large tree canopies, shrubs, and herbaceous cover within property lots. Results showed that the Lower Ninth Ward and the Seventh Ward neighborhoods suffered the highest damage levels to canopy cover from the impacts of Hurricane Katrina, losing around 15% in overall tree cover, and sustaining severe damaged on as much as 75% of the neighborhoods' pre-2005 tree cover. Nevertheless, the Lower Ninth Ward has had the highest rate of regrowth of vegetation cover (as of 2019), mainly owing to unmaintained tall shrub cover that in 2019 commonly exceeded 2 m in height on many of the neighborhood's blocks. On a city-wide scale, New Orleans's urban forest is significantly more fragmented today into small tree canopies than it was in 2004.

Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu’s method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.

Automated HRSI georegistration using orthoimage and SRTM: Focusing KOMPSAT-2 imagery

Conventional manual georegistration has been labor-intensive and time-consuming to process globally acquired high-resolution satellite imagery (HRSI) including the Korea Multi-Purpose Satellite-2 (KOMPSAT-2). While an image-to-image matching-based method can provide an automated procedure, geometric and pixel value differences such as the apparent leaning of the terrain limits an accurate georegistration. This study utilizes the projection of orthoimages into the HRSI space using external elevation data to reduce the geometric difference, and an edge matching is applied to overcome the pixel value differences. Either rigorous or replacement sensor modeling is then carried out for georegistration with robust outlier removal. An experiment for a KOMPSAT-2 strip using Digital Orthophoto Quadrangles and the Shuttle Radar Topography Mission improved the horizontal positional accuracy from 70 to only a few meters for the test data set.

A landscape of shifting‐mosaic steady state in Lassen Volcanic National Park, California

AbstractIt is generally perceived that landscape patterns or textures in a given protected area are spatially stationary. The findings of this study suggest that this common perception is only partially correct. Over the course of 52 years, equilibrium in landscape shifting was detected using digital data for the Lassen Volcanic National Park (USA). Vertical aerial photographs taken of the park in 1941 were geo‐referenced with the digital orthophoto quarter quadrangle (DOQQ) images of the same area from 1993 to identify landscape compositions and to measure change. Spatial analysis was used to observe pattern changes over time. The results suggested that landscape development maintained equilibrium while patches were in various stages of a successional sequence. The total area of each landscape component held steady, although over time patches throughout the landscape changed—a shifting‐mosaic steady state (SMSS). These findings reflect the limitations of contemporary environmental conservation theory. They also suggest the importance of considering landscape change in policies that currently govern park planning and management.

UAV-based multi-sensor data fusion processing

This article presents a mathematical model for an Unmanned Aerial Vehicle (UAV)-based, multi-sensor data integration. As background, this article first discusses the design and the implementation of a low-cost civilian UAV system, including its field flight validation, system calibration and mapping accuracy evaluation. Then, the photogrammetry-based mathematical model is developed. This multi-sensor, mathematical model reveals that the boresight matrix in a low-cost UAV system does not remain constant. This contradicts the practice in traditional airborne mapping systems where the boresight matrix is assumed to be a constant over an entire mission. Thus, to achieve highly accurate mapping, exterior orientation parameters (EOPs) for each video frame in a low-cost UAV mapping system have to be estimated. This model can achieve a planimetric mapping accuracy using 1-2 pixels when compared with the United States Geological Survey digital orthophoto quadrangles (USGS DOQ) orthophoto.

Performances of Different Global Positioning System Devices for Time-Location Tracking in Air Pollution Epidemiological Studies

Background:People’s time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people’s time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies.Methods:We evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps.Results:The battery life of our tested instruments ranged from <9 hours to 48 hours. The acquisition of location time after startup ranged from a few seconds to >20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%–95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%–80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas.Conclusions:All the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects’ time location patterns in epidemiological studies.

Estimating impervious surfaces using linear spectral mixture analysis with multitemporal ASTER images

Impervious surface is a key indicator of urban environmental quality and degree of urbanization. Therefore, estimation and mapping of impervious surfaces by using remote sensing digital images has attracted increasing attention recently. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and image analysis must take into account this environmental characteristic. In this paper, three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, acquired on 3 October 2000, 16 June 2001 and 5 April 2004, respectively, were used to test the seasonal sensitivity of impervious surface estimation. The study area was the city of Indianapolis (Marion County), Indiana, USA. Linear spectral mixture analysis (LSMA) was applied to generate high-albedo, low-albedo, vegetation and soil fraction images (endmembers), and impervious surfaces were then estimated by adding high- and low-albedo fraction images. In addition, land use/land cover (LULC) and land surface temperature (LST) maps were generated and used to create image masks to remove non-impervious pixels. The accuracy of the impervious surface maps was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. Three accuracy indicators, the root mean square error (RMSE), mean average error (MAE) and correlation coefficient (R 2), were calculated and compared to analyse the seasonal sensitivity of impervious surface estimation. Our results indicate that vegetation phenology has a fundamental impact on impervious surface estimation. The summer (June) image was better for impervious surface estimation than the spring (April) and autumn (October) images. The LULC and LST image masks can significantly increase the accuracy of impervious surface estimation. The mean LST was found appropriate to be set as the threshold for the various image masks. A summer image was most appropriate because there was full growth of vegetation, and mapping of impervious surfaces was more effective with a contrasting spectral response from green vegetation. The mixing space, based on the four endmembers, was perfectly three-dimensional. By contrast, there was significant amount of bare soil and ground and non-photosynthetic vegetation in the spring and autumn images. Plant phenology caused changes in the variance partitioning and impacted the mixing space characterization, leading to a less accurate estimation of the impervious surfaces.

GEOMATRIC RECTIFICATION FOR VIDEO IMAGE FROM MUAV BASED ON DLT

In recent years,the Unmanned Aerial Vehicles(UAVs) have aroused increasing interests among experts for such civilian applications as disaster monitoring,surveillance and spatial decision-making support.The high-resolution orthoimage generated from the UAVs video image stream can be used as a basic map for planners and decision-makers to make decisions by obtaining high precision geospatial data through registering,compiling,measuring and analyzing the orthoimage.This paper presents the methods for producing the true orthoimage from UAVs streams.The IOPs are calculated by using iteration algorithm having the EOPs obtained from GPS and INS system as initial values,and these parameters are used for rectification of UAVs image.Experimental results show that all the orthoimages are of high accuracy under the condition that DEM/DSM,DOQQ and UAVs video image have the same resolution.

SSTL UK-DMC SLIM-6 Data Quality Assessment

Satellite data from the Surrey Satellite Technology Limited (SSTL) United Kingdom (UK) Disaster Monitoring Constellation (DMC) were assessed for geometric and radiometric quality. The UK-DMC Surrey Linear Imager 6 (SLIM-6) sensor has a 32-m spatial resolution and a ground swath width of 640 km. The UK-DMC SLIM-6 design consists of a three-band imager with green, red, and near-infrared bands that are set to similar bandpass as Landsat bands 2, 3, and 4. The UK-DMC data consisted of imagery registered to Landsat orthorectified imagery produced from the GeoCover program. Relief displacements within the UK-DMC SLIM-6 imagery were accounted for by using global 1-km digital elevation models available through the Global Land One-km Base Elevation (GLOBE) Project. Positional accuracy and relative band-to-band accuracy were measured. Positional accuracy of the UK-DMC SLIM-6 imagery was assessed by measuring the imagery against digital orthophoto quadrangles (DOQs), which are designed to meet national map accuracy standards at 1 : 24 000 scales; this corresponds to a horizontal root-mean-square accuracy of about 6 m. The UK-DMC SLIM-6 images were typically registered to within 1.0-1.5 pixels to the DOQ mosaic images. Several radiometric artifacts like striping, coherent noise, and flat detector were discovered and studied. Indications are that the SSTL UK-DMC SLIM-6 data have few artifacts and calibration challenges, and these can be adjusted or corrected via calibration and processing algorithms. The cross-calibration of the UK-DMC SLIM-6 and Landsat 7 Enhanced Thematic Mapper Plus was performed using image statistics derived from large common areas observed by the two sensors.

Distribution of debris flows in Glacier National Park, Montana, U.S.A.

The spectacular scenery of Glacier National Park is the result of glacial erosion as well as post-glacial mass wasting processes. Debris flow magnitude and frequency have been established through extensive fieldwork across seven separate drainage basins in the eastern portion of the park. This paper summarizes the investigation of the hypotheses that debris flow distribution in the Glacier National Park, east of the Continental Divide is (a) not random; and (b) concentrated adjacent to the Continental Divide. The location of 2317 debris flows were identified and mapped from sixty-three 1-m resolution Digital Orthophoto Quarter Quadrangles and their spatial distribution was then analyzed using ArcView Spatial Analyst GIS software. The GIS analysis showed that the debris flows are not randomly distributed nor are they concentrated directly adjacent to the Divide. While the Continental Divide provides orographic enhancement of precipitation directly adjacent to the Divide, the debris flows are not concentrated there due to a lack of available weathered regolith. The most recent Little Ice Age glaciation removed the debris directly adjacent to the Divide, and without an adequate debris supply, these steep slopes experience few debris flows. Both abundant water and an adequate debris supply are necessary to initiate slope failure, resulting in a clustering of debris flows at the break in slope where valley walls contact talus slopes. A variety of summer storm and antecedent moisture conditions initiate slope failures in the Glacier National Park, with no distinct meteorological threshold. With over two million visitors every year, and millions of dollars of park infrastructure at risk, identifying the hazard of debris flows is essential to future park management plans.

Extent and spatial patterns of grass bald land cover change (1948–2000), Oregon Coast Range, USA

Globally, temperate grasslands and meadows have sharply declined in spatial extent. Loss and fragmentation of grasslands and meadows may impact biodiversity, carbon storage, energy balance, and climate change. In the Pacific Northwest region of North America, grasslands and meadows have declined in extent over the past century. Largely undocumented in this regional decline are the grass balds of the Oregon Coast Range, isolated grasslands in a landscape dominated by coniferous forests. This study was conducted to quantify the spatial extent and patterns of grass bald change. Five balds in the Oregon Coast Range were evaluated using historical aerial photographs and recent digital orthophoto quadrangles (DOQ). Over the time period of study (1948/1953 to 1994/2000), bald area declined by 66%, primarily from forest encroachment. The number and average size of bald vegetation patches declined, while edge density increased. Tree encroachment into balds was inversely related to distance from nearest potential parent trees. Spatial patterns of bald loss may result from a forest to bald gradient of unfavorable environmental conditions for tree establishment and/or seed dispersal limitation. Species dependent on balds may be at risk from loss of bald area and increased fragmentation, although metrics of habitat fragmentation may not reflect species-specific habitat requirements. Tree encroachment patterns and increased bald edge densities suggest increasing rates of bald loss in the future. The remote sensing nature of this study cannot determine the fundamental causes of bald decline, although prior research suggests climate change, cessation of native burning, successional changes in response to prior wildfires, and cessation of livestock grazing all may have potential influence.

Analysis of Fire-related Vegetation Patterns in the Huachuca Mountains, Arizona, USA, and Sierra los Ajos, Sonora, Mexico

There is general interest among fire ecologists to integrate observed fire regimes into long term fire management. The United States-Mexico borderlands provide unique research opportunities to study effects of contrasting forest management activities on forest structure and pattern. To increase understanding of the range of forest stand conditions in borderland ecosystems, we compared tree crown patterns from two forests near the US-Mexico border that are managed under contrasting fire policies and have contrasting fire histories. Locations of individual tree crowns in geographically and ecologically matched forest patches were detected and plotted from Digital Orthophoto Quarter Quadrangles (DOQ) using a semi-automated crown detection procedure. Spatial patterns of tree crowns were analyzed with point pattern methods. Results describe disparate spatial patterns and levels of crown density in fire-managed forests of the US and unmanaged forests of northern Mexico.

Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison

Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO‐1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high‐quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI‐derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion‐derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low‐albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low‐albedo surfaces came mainly from additional bands in the mid‐infrared region.

Fluvial Geomorphologic and Hydrodynamic Assessment in the Tidal Portion of the Lower Rio Grande River, US-Mexico Borderland

With fast population growth and economic development in the US-Mexico Borderland, water diversion and usages has reduced the flow substantially in the Low Rio Grande River (LRGR). Tidal portion of the LRGR has posed an environmental drought issue since 1993 and the channel clogged with the invasion of non-native plants also causes environmental problems. As a result of the intensity of these natural and manmade factors, the Rio Grande stopped flowing to the sea in February 2001. The flows were reduced to a point that they were unable to push out the sand deposited at the mouth. Geomorphologic evolution in association with the fluvial process of the LRGR has received wide attention. The purpose of this study is to investigate the necessary flow to maintain the river mouth open to the Gulf of Mexico using integrated approach of remote sensing and numerical analysis. It also sheds lights on possible solutions in decision-making. The analysis starts with a geomorphologic analysis using satellite remote sensing imagery and historic flow rate assessment, followed by a two-dimensional, depth averaged, finite element numerical modeling analysis to simulate the hydrodynamics of the tidal portion of the LRGR. While Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery and Digital Orthophoto Quadrangles (DOQs) were used for the geomorphologic investigation, Research Management Associates (RMA-2) software and Surface Water Modeling System (SMS 8.0) were used for minimum stream flow rate analysis. Alternative geomorphic conditions were modeled and compared to the original case, where two simulation runs were established. The first one was designed in dealing with a more refined mesh; and the second was prepared for handling an increased discharge at the inflow boundary along with the investigation of shear stress. The study concludes that the peak shear stress increased with increasing discharge towards the mouth of the river and a 1.27 m3/s discharge is necessary to maintain the opening of the river mouth.

Advanced Exploratory Data Analysis for Mapping Regional Canopy Cover

USGS Digital Orthophoto Quadrangles (DOQs) are a form of inexpensive, high spatial resolution (1 m ground resolution) imagery available for most regions of the United States. Typically, DOQs have been used in the construction of “basemaps” or as training datasets. In this paper we present a technical approach that uses DOQs as the primary data source to map regional forest canopy cover. This approach, a form of advanced exploratory data analysis (AEDA), can separate areas of crown, shadow, and non-crown vegetation using a single value threshold and a “value range” threshold obtained by analyzing the statistical plots built on a multifractal model. By applying AEDA, we can distinguish the crown, crown boundary zone, shadow areas, and non-crown areas within a DOQ mosaic by their distinctive multifractal properties. Over a period of two years, we mapped canopy cover of 20,000 km 2 of ponderosa pine-dominated forest across northern Arizona using this technique. We used two hundred ground plot measurements from an independent source to assess the accuracy of the canopy cover map across an 8,000 km 2 region.

STORM RUNOFF PREDICTION BASED ON A SPATIALLY DISTRIBUTED TRAVEL TIME METHOD UTILIZING REMOTE SENSING AND GIS1

ABSTRACT: In this study, remotely sensed data and geographic information system (GIS) tools were used to estimate storm runoff response for Simms Creek watershed in the Etonia basin in northeast Florida. Land cover information from digital orthophoto quarter quadrangles (DOQQ), and enhanced thematic mapper plus (ETM+) were analyzed for the years 1990, 1995, and 2000. The corresponding infiltration excess runoff response of the study area was estimated using the U.S. Department of Agriculture (USDA), Natural Resources Conservation Service Curve Number (NRCS‐CN) method. A digital elevation model (DEM)/GIS technique was developed to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. A comparison of predicted to observed stream response shows that the model predicts the total runoff volume with an efficiency of 0.98, the peak flow rate at an efficiency of 0.85, and the full direct runoff hydrograph with an average efficiency of 0.65. The DEM/GIS travel time model can be used to predict the runoff response of ungaged watersheds and is useful for predicting runoff hydrographs resulting from proposed large scale changes in the land use.

A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images

A hybrid method that incorporates the advantages of supervised and unsupervised approaches as well as hard and soft classifications was proposed for mapping the land use/cover of the Atlanta metropolitan area using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The unsupervised ISODATA clustering method was initially used to segment the image into a large number of clusters of pixels. With reference to ground data based on 1 : 40 000 colour infrared aerial photographs in the form of Digital Orthophoto Quarter Quadrangle (DOQQ), homogeneous clusters were labelled. Clusters that could not be labelled because of mixed pixels were clipped out and subjected to a supervised fuzzy classification. A final land use/cover map was obtained by a union overlay of the two partial land use/cover maps. This map was evaluated by comparing with maps produced using unsupervised ISODATA clustering, supervised fuzzy and supervised maximum likelihood classification methods. It was found that the hybrid approach was slightly better than the unsupervised ISODATA clustering in land use/cover classification accuracy, most probably because of the supervised fuzzy classification, which effectively dealt with the mixed pixel problem in the low-density urban use category of land use/cover. It was suggested that this hybrid approach can be economically implemented in a standard image processing software package to produce land use/cover maps with higher accuracy from satellite images of moderate spatial resolution in a complex urban environment, where both discrete and continuous land cover elements occur side by side.

Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case

The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies.Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required.

Estimating impervious surface distribution by spectral mixture analysis

Estimating the distribution of impervious surface, a major component of the vegetation–impervious surface–soil (V–I–S) model, is important in monitoring urban areas and understanding human activities. Besides its applications in physical geography, such as run-off models and urban change studies, maps showing impervious surface distribution are essential for estimating socio-economic factors, such as population density and social conditions. In this paper, impervious surface distribution, together with vegetation and soil cover, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, OH in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil were selected to model heterogeneous urban land cover. Impervious surface fraction was estimated by analyzing low and high albedo endmembers. The estimation accuracy for impervious surface was assessed using Digital Orthophoto Quarterquadrangle (DOQQ) images. The overall root mean square (RMS) error was 10.6%, which is comparable to the digitizing errors of DOQQ images. Results indicate that impervious surface distribution can be derived from remotely sensed imagery with promising accuracy.

Extraction of hydrographic regions from remote sensing images using an oscillator network with weight adaptation

The authors propose a framework for object extraction with accurate boundaries. A multilayer perceptron is used to identify seed points through examples, and regions are extracted and localized using a locally coupled network with weight adaptation. A functional system has been developed and applied to hydrographic region extraction from Digital Orthophoto Quarter-Quadrangle images.