Employing spatial information technologies to monitor biological control of saltcedar in West Texas

The saltcedar leaf beetle (Diorhadha spp.) has shown promise as a biocontrol agent for saltcedar (Tamarix spp.) invasions in the USA. In Texas, natural resource managers need assistance in monitoring biological control of invasive saltcedars. This study describes application of a medium-format, digital camera acquiring natural colour imagery and global positioning system (GPS) and geographic information system (GIS) technologies to check biological control of saltcedar in west Texas. On 8 July and 8 September 2011, natural colour airborne digital imagery was collected along a 155.8?km transect covering portions of Presidio and Brewster counties of Texas. The camera was tethered to a GPS receiver that geotagged each image and saved the coordinates to a key-hole marked up language file that was viewable on Google Earth. Saltcedar trees exhibiting severe feeding damage and those that were totally defoliated were easily identified in the imagery. The former appeared in orange to brown colour tones; the latter exhibited grey colour tones. Point distribution maps showing locations of saltcedar trees exhibiting feeding damage were developed from GPS information in the GIS. Coordinate points on the map were linked to the corresponding image, permitting the user to have quick access to view imagery. The results of this study show a practical method for monitoring biological control of saltcedar.     

Airborne MSS for land cover classification II

A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per‐field sampling and the addition of low‐pass filtering, image texture, prior probabilities and two dates of imagery.

For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per‐field sampling accounted for the largest proportion of this increase.     

AN EXPERIMENT IN AUTOMATED INTERPRETATION OF REMOTE SENSING IMAGERY

Algorithms, designed for digital image processing in standard mainframe computers and representing sequential stages in a land-use classification procedure, are used to produce maps of agricultural crop types from multispectral satellite imagery. Pixel reflectance values are first grouped according to an unsupervised “rapid classification algorithm,” or data compression procedure. Mean reflectance values of the resulting classes then go into a supervised “sequential clustering algorithm” where classes are refined according to training value and other parameter inputs. The objective is to increase the accessibility of automated image interpretation while balancing classification accuracy and processing time. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1984, No. 4, pp. 63-69.     

Landcover Classification in the Taieri River Catchment,New Zealand: A Focus on the Riparian Zone

Abstract

Riparian vegetation has a fundamental influence on the biological, chemical and physical nature of rivers. The quantification of riparian landcover is now recognised as being essential to the holistic study of the ecosystem characteristics of rivers. Medium resolution satellite imagery is now commonly used as an efficient and cost effective method for mapping vegetation cover; however such data often lack the resolution to provide accurate information about vegetation cover within riparian corridors. To assess this, we measure the accuracy of SPOT multispectral satellite imagery for classification of riparian vegetation along the Taieri River in New Zealand. In this paper, we discuss different sampling strategies for the classification of riparian zones. We conclude that SPOT multispectral imagery requires considerable interpretative analysis before being adequate to produce sufficiently detailed maps of riparian vegetation required for use in stream ecological research.     

Applying broadband spectra to assess biological control of saltcedar in west Texas

Broadband field spectra were assessed to discriminate invasive saltcedar (Tamarix spp.) trees exhibiting feeding damage caused by the saltcedar leaf beetle (Diorhadba spp.) from other land cover types. Data were collected at two study sites near Presidio, Texas in 2010 and 2011. Spectral bands evaluated were coastal blue (400–450?nm), blue (450–510?nm), green (510–580?nm), yellow (585–625?nm), red (630–690?nm), red-edge (705–745?nm), and near-infrared (770–895, 860–1040?nm). Data were evaluated with analysis of variance and Scheffe’s multiple comparison test (α?=?0.05). The red band generally separated severely damaged saltcedar trees from other land cover features. Near-infrared bands separated defoliated saltcedar trees. Broadband spectra has potential for distinguishing saltcedar trees exhibiting feeding damage caused by the saltcedar leaf beetle from other associated features, thus supporting future explorations of airborne and satellite-borne multispectral systems to monitor biological control of saltcedar within complex landscapes.     

Using high resolution QuickBird imagery for crop identification and area estimation

Imagery from recently launched high spatial resolution satellite sensors offers new opportunities for crop assessment and monitoring. A 2.8-m multispectral QuickBird image covering an intensively cropped area in south Texas was evaluated for crop identification and area estimation. Three reduced-resolution images with pixel sizes of 11.2 m, 19.6 m, and 30.8 m were also generated from the original image to simulate coarser resolution imagery from other satellite systems. Supervised classification techniques were used to classify the original image and the three aggregated images into five crop classes (grain sorghum, cotton, citrus, sugarcane, and melons) and five non-crop cover types (mixed herbaceous species, mixed brush, water bodies, wet areas, and dry soil/roads). The five non-crop classes in the 10-category classification maps were then merged as one class. The classification maps were filtered to remove the small inclusions of other classes within the dominant class. For accuracy assessment of the classification maps, crop fields were ground verified and field boundaries were digitized from the original image to determine reference field areas for the five crops. Overall accuracy for the unfiltered 2.8-m, 11.2-m, 19.6-m, and 30.8-m classification maps were 71.4, 76.9, 77.1, and 78.0%, respectively, while overall accuracy for the respective filtered classification maps were 83.6, 82.3, 79.8, and 78.5%. Although increase in pixel size improved overall accuracy for the unfiltered classification maps, the filtered 2.8-m classification map provided the best overall accuracy. Percentage area estimates based on the filtered 2.8-m classification map (34.3, 16.4, 2.3, 2.2, 8.0, and 36.8% for grain sorghum, cotton, citrus, sugarcane, melons, and non-crop, respectively) agreed well with estimates from the digitized polygon map (35.0, 17.9, 2.4, 2.1, 8.0, and 34.6% for the respective categories). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas.     

Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps

Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists.     

Knowledge‐based digital mapping of vegetation types in Big Bend National park,Texas

A knowledge‐based strategy is utilized to develop a model for performing automated mapping of twenty vegetation cover types occurring within Big Bend National P ark, Texas. Many of the cover types found within this desert region cannot be reliably identified solely on a spectral basis, even on large‐scale, aircraft‐borne color imagery. Positive identification may be improved, however, by incorporating additional spatial information that may distinguish given cover types on a non‐spectral basis. In this study, digital soils and digital terrain data are utilized with spectral imagery from Landsat Thematic Mapper.

This knowledge‐based strategy is comprised of three primary elements: knowledge acquisition, rules development, and model structuring. Knowledge acquisition identifies the vegetation composition and non‐vegetative site characteristics associated with the occurrence of each cover type. Rules development compares and contrasts these characteristics among pairs of cover types and their subsets Model structuring places the presumed digital analogs of these characteristics within a multi‐layered classification.

After implementing the automated mapping model, its quality was evaluated with an accuracy assessment. Based upon the cover types field‐truthed at 142 sites within the park, the model performed at an 72% level of accuracy. For comparative purposes, a traditional supervised (spectral, statistical) classification yielded a 42% accuracy. The superiority of the model is attributed to its incorporation of knowledge‐based information; in essence, identification by considering only those cover types likely to occur over given spectral and physiographic conditions.     

Integrating spectral and non-spectral data to improve urban settlement mapping in a large Latin-American city

ABSTRACT

Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned and informal settlements. After carefully considering the landscape complexity there, we designed a data fusion method combining multispectral imagery and non-spectral data for urban and land mapping. Specifically, we acquired a cloud-free Landsat-8 image and two non-spectral datasets, i.e., digital elevation models and road networks. Then, we implemented a set of experiments with different inputs to evaluate their merits in thematic mapping through a supervised protocol. We found that the map generated with the multispectral data alone had an overall accuracy of 73.3% but combining multispectral imagery and non-spectral data yielded a land cover map with 90.7% overall accuracy. Interestingly, the thermal infrared information helped substantially improve both the overall and categorical accuracies, particularly for the two urban classes. The two types of non-spectral data were critical in resolving several spectrally confused categories, thus considerably increasing the mapping accuracy. However, the panchromatic band with higher spatial resolution and its derived textural measurement only generated a marginal accuracy improvement. The novelties of our work are with the successful separation between the two major types of urban settlements in a complex environment using a carefully designed data fusion approach and the insight into the relative merits of the thermal infrared information and non-spectral data in helping resolve the issue of class ambiguity. These findings should be valuable in deriving accurate urban settlement information which can further advance the research on socio-ecological dynamics and urban sustainability.     

Utilizing the Potential of World View −2 for Discriminating Urban and Vegetation Features Using Object Based Classification Techniques

With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.     

Investigating Issues in Map Accuracy When Using an Object-Based Approach to Map Benthic Habitats

In a recent study, benthic habitat maps were created of the Texas Gulf Coast from digital aerial imagery. The images were classified using an object-based image analysis (OBIA) approach and a classification and regression tree (CART) technique. The map was manually edited, changing 26% of the polygons' labels. Accuracy assessments of the unedited map and the edited map revealed the two were not significantly different. The research in this paper evaluates why these maps may have similar accuracies. Our analyses indicate that the small segmentation scale parameter used over-segmented the imagery, reducing the effectiveness of the CART technique and editing.     

机载三维成像仪的定位原理与误差分析

本文论述了“机载三维成像仪”的定位原理,并在系统定位原理的基础上,详细讨论了与“机载三维成像仪”的对地定位精度有关的传感器的误差对系统定位精度的影响,这一问题的研究不仅对研制针对不同目的的激光地形制图系统的设计具有指导意义,而且对激光扫描制图系统的数据平差具有重要意义。     

Monitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques

The objective of this study was to evaluate image-based procedures for monitoring cross-border foot trails in the US – Mexico border zone in eastern San Diego County using airborne remote sensing techniques. Specifically, digital multi-spectral and multi-temporal imagery from an airborne digital multi-spectral imaging system, digital image processing, and visual image analysis techniques were explored in the context of detecting and delineating new trail features and updating trail GIS layers. Three trail updating approaches: map-to-image (M-I) overlay, map and image-to-image (M/I-I) differencing, map and image-to-image (M/I-I) swiping and two types of spectral transform, PCA and NDVI, were tested and compared. The M-I overlay was found to be the most reliable trail updating approach. The optimal image enhancement method for the M-I overlay approach varied with vegetation structure. PCA imagery yielded better results than NDVI imagery in a highly disturbed area and NDVI imagery performed better in a densely vegetated area. The M/I-I swiping approach was useful for distinguishing misregistered extant trails from new trail features.     

MAPPING THE AVALANCHE HAZARD FOR MOUNTAINOUS AREAS OF AFGHANISTAN

The authors describe a procedure for the compilation of maps of the avalanche hazard in high-mountain regions of Afghanistan. The basic sources of information include space imagery (photographs and scanner imagery), weather station data, and other geographic information on relief, elevation, location of moisture sources, etc. The methodology features the compilation of series of increasingly more specific and informative maps and graphs regarding the avalanche hazard: terrain types, snow cover depth and seasonal extent, duration of snow cover and its elevational zonation, snow as a factor in avalanche formation, and summary map of avalanche hazard. Translated by Elliott B. Urdang, Providence, RI 02906 from: Materialy glyatsiologicheskikh issledovaniy, 1991, No. 71, pp. 86–93.     

A multi-resolution satellite imagery approach for large area mapping of ericaceous shrubs in Northern Quebec, Canada

Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps.     

Assessing ground cover at patch and hillslope scale in semi-arid woody vegetation and pasture using fused Quickbird data

The amount and distribution of vegetation and ground cover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslope scales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map ground cover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensity-hue-saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbird data; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating ground cover, and (3) map and assess spatial and temporal changes in ground cover at hillslope scale using the most appropriate method or combination of methods. Estimates of ground cover from the imagery were compared with a subset of observed ground cover estimates to determine map accuracy. The MIHS algorithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate ground cover maps. The patch size of the ground cover beneath canopies was similar to canopy size, and percent ground cover (mainly litter) increased with canopy size. Ground cover was mapped with relative accuracies of 84% in the woody vegetation and 86% in the pasture. From 2008 to 2009, ground cover increased from 55% to 65% in the woody vegetation and from 40% to 45% in the pasture. These ground cover maps can be used to explore the spatial ecohydrological interactions between areas of different ground cover at hillslope scale with application to management at broader scales.     

Estimating regional forest cover in East Texas using Advanced Very High Resolution Radiometer (AVHRR) data

This study tested the degree to which single date, near-nadir AVHRR image could provide forest cover estimates comparable to the phase I estimates obtained from the traditional photo-based techniques of the Forest Inventory and Analysis (FIA) program. FIA program is part of the United States Department of Agriculture-Forest Service (USFS). A six-county region in east Texas was selected for this study. Manual identification of ground control points (GCPs) was necessary for geo-referencing this image with higher precision. Through digital image classification techniques forest classes were separated from other non-forest classes in the study area. Classified AVHRR imagery was compared to two verification datasets: photo-center points and the USFS FIA plots. The overall accuracy values obtained were 67 and 71%, respectively. Analyses of the error matrices indicated that the AVHRR image correctly classified more forested areas than non-forested areas; however, most of the errors could be attributed to certain land cover and land use classes. Several pastures with tree cover, which were field-identified as non-forest, were misclassified as forest in the AVHRR image using the image classification system developed in this study. Recently harvested and young pine forests were misclassified as non-forest in the imagery. County-level forest cover estimates obtained from the AVHRR imagery were within the 95% confidence interval of the corresponding estimates from traditional photo-based methods. These results indicate that AVHRR imagery could be used to estimate county-level forest cover; however, the precision associated with these estimates was lower than that obtained through traditional photo-based techniques.     

INTERPRETATION OF THE AGRICULTURAL LANDSCAPE WITH SPACE IMAGERY

Heterochronous multispectral imagery from the “Fragment” scanner is used to identify and map a series of “natural-agricultural complexes,” or agricultural landscapes, of southern European Russia. Interpretation is based on imagery in the green, orange, red, and near-infrared zones of the spectrum (0.5-1.1 μm). Interpretation keys and other information designed to facilitate feature discrimination (optimal wavelengths, best seasons for imagining) are provided in many instances. Natural landscape and soil erosion maps are also compiled from the imagery, which supply information (an optimal crop rotation scheme and needed reclamation measures) used on the agricultural landscape map. Translated from: Vestnik Moskovs-kogo Unlversiteta, geografiya, 1985, No. 2, pp. 34-41.     

Using a Combination of Digital Aerial Photography and Satellite Remote Sensing to Assist Contemporary Restructuring in an Urban Area of South Africa

Abstract

The transition and restructuring process of urban South Africa are currently in the phase of identifying land development objectives. These objectives aim to integrate previously segregated areas through integrated development plans. This research aims firstly to identify and describe the historical development of the spatial form and structure of the secondary city and capital of the Northern Province, Pietersburg and its dispersed peripheral towns. Supervised classification of SPOT HRV multispectral imagery is used to support the theoretical explanation. Images from an airborne digital Kodak DCS 420 camera are used to provide training sites in the pre‐classification stages, and also provide field data to the process of post‐classification accuracy assessment. Secondly, SPOT HRV imagery is applied to identify the stark contrast in urban development between the city of Pietersburg and its surrounding former homeland towns. Both built and natural environmental aspects are investigated. In conclusion benefits and problems of assessing urban morphology and development in a developing country by means of a combination of satellite imagery and digital aerial photography are discussed.     

Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data

This study introduces a method for object-based land cover classification based solely on the analysis of LiDAR-derived information—i.e., without the use of conventional optical imagery such as aerial photography or multispectral imagery. The method focuses on the relative information content from height, intensity, and shape of features found in the scene. Eight object-based metrics were used to classify the terrain into land cover information: mean height, standard deviation (STDEV) of height, height homogeneity, height contrast, height entropy, height correlation, mean intensity, and compactness. Using machine-learning decision trees, these metrics yielded land cover classification accuracies > 90%. A sensitivity analysis found that mean intensity was the key metric for differentiating between the grass and road/parking lot classes. Mean height was also a contributing discriminator for distinguishing features with different height information, such as between the building and grass classes. The shape- or texture-based metrics did not significantly improve the land cover classifications. The most important three metrics (i.e., mean height, STDEV height, and mean intensity) were sufficient to achieve classification accuracies > 90%.