Aerial Photograph Imagery Research Articles

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.

The Role of Farm Management Characteristics in Understanding the Spatial Distribution of Landscape Elements: A Case Study in the Netherlands

In Western Europe the fate of biodiversity is intimately linked to agricultural land use. A driving force behind biodiversity decline is the gradual conversion of Europe’s traditional integrated rural landscapes of nature and agriculture into monofunctional units of production. With these developments, semi-natural landscape elements have increasingly disappeared from agricultural landscapes. A growing body of research, however, underlines the importance of semi-natural habitats in agricultural landscapes for biodiversity conservation, habitat connectivity, and ecosystem services. On the local scale, considerable variation between the relative area of landscape elements on individual farms can be observed. Farm management decisions are presumed to be important determinants for the composition of agricultural landscapes and the services provided to society. By bringing together data from farmer interviews and aerial photographic imagery, this paper analyzes the predictive validity of farm management characteristics to understand the distribution of landscape elements on farmland parcels. The farm management parameters included in the study are relevant to current dominant trends in the Dutch agricultural sector; intensification, scale enlargement, diversification, and gradual termination of farming activities. Scale enlargement and migratory processes are found to be important predictors. The results of the Dutch case study provide insights in the threats and opportunities for the conservation of semi-natural habitat in agricultural landscapes. The findings present an empirical contribution to the debate on sustainable management of agriculture’s green infrastructure and, in broader perspective, the objective to reconcile agricultural production with the urging need of biodiversity conservation in Europe’s spatially contested countryside.

MONITORING OF LAND USE CHANGES USING AERIAL PHOTOGRAPH AND IKONOS IMAGE IN BEDUGUL, BALI

There was change of expending land use in Bedugul. It is necessary to monito the change of highland of Bali, catchments area of Beratan, Buyan and Tamblingan lakes. In order to control land use change and to anticipate degradation of hydrology function of this area. This study is to monitor the land use change by remote sensing and GIS technique. To evaluate land use and land cover, aerial photograph imagery and Ikonos imagery were used. Over 22 years of observation (1981-2003), there was land use changes in the catchments area of Beratan, Buyan and Tamblingan lakes at Bedugul area. The area of settlement increased by 62.6 ha, dry land vegetable crops and forest decreased by 116.5 ha and 32.5 ha, respectively. The surface area of Buyan Lake was also decreased, due to sedimentation caused by erosion in the vegetables dry land crops. Planning the land use study on erosion and soil-water conservation in this area necessary, in order to control land use change, erosion, and sedimentation in the lakes. Keywords: land use, monitoring, aerial photograph, Ikonos image.

Stratal patterns and lithofacies of an intact seismic-scale Carboniferous carbonate platform (Asturias, northwestern Spain): a virtual outcrop model

Abstract Among the more challenging questions in geology are those concerning the anatomy of sedimentary bodies and related stratal surfaces. Though significant progress has been made on the interpretation of depositional environments, little systematic data are available on their dimensions and geometry. With the recent advances in computer power, software development and accuracy of affordable positioning equipment, it has now become possible to extract high-resolution quantitative data on the anatomy of sedimentary bodies. In Asturias, northwestern Spain, aerial photography provides continuous 2-D cross-sections of a seismic-scale, rotated to vertical, carbonate platform margin of the Early Carboniferous. Digital elevation models, orthorectified aerial photographic imagery and ground verification of stratal surfaces generated the elements that are required to reconstruct the true dimensions, angular relationships of bedding planes and the spatial distribution of facies units in this platform margin. Along with biostratigraphy this provides sufficient constraints to estimate rates of progradation, aggradation, growth and removal of sediments in the slope environment. Here we present a methodology to create outcrop models and integrate complementary types of data that provide new insights in sedimentology that were previously unattainable.

リニアメントの断裂系としての特性とその評価

Recently, the relationship between lineaments and fracture systems has been discussed by some statistic approach. In this study, the criterias for the interpretation of lineaments using Landsat TM, Spot and aerial photograph imagery are proposed for the detection of fractured zones through lineaments. In order to clarify this criteria which are divided into 6 ranks, ranging from A to F, the six sites including USA are studied.Field evidence shows that many lineaments coincide with faults or joints and it's probability of the coincidence between lineaments and faults or joints is restricted by the length of lineaments. The lineaments length of the 100 percent probability to coincide lineaments with faults or joints agree with the linear part which is called fractal distribution in the relationship between lineaments length and frequency.In addition, the relationship between lineaments criteria and the width of the fractured zones have a good harmony. High ranking lineaments are very useful in the interpretation of geological structures.

Census planning and the recent improvements in aerial photographic imagery—the Nigerian example

AbstractAerial photo imagery has been suggested by recent writers on census cartography as the best source of new data depends on a number of technical factors. The factors include aerial camera lens and its focal length, film base, film emulsion and mounting, flight planning and operation, aerial survey platform and weather conditions. This paper discusses the parameters as they apply to the Nigerian situation.

Census planning and the recent improvements in aerial photographic imagery—the Nigerian example

Census planning and the recent improvements in aerial photographic imagery—the Nigerian example

多様な画像によるリニアメントひん度の解析 レーダ画像の屋久島地域への適用例について

The result of lineament analysis of the Yakushima Island, Southwest Japan, using Landsat, radar (SAR) and aerial photograph imagery was compared to study the difference caused by various image data. It differs owing to variation of resolution (R) and scale (S) of each imagery and indicates that lineament analysis can be performed most efficiently in case of R×S = 0.01 (cm) .The magnitude-frequency relation for length of fractures was examined by combining the results of lineament analysis of these various kinds of imagery. It is experimentally represented aslog N=k-m log Lwhere N is the number of lineaments whose length is ranging from L to L±1/2·ΔL and k, m are constants. The m value gives decreasing ratio with respect to the length of lineaments and is calculated as 2.30, which seems reasonable value for representing the magnitude-frequency relation of fractures compared with several reports thus far made.

Textural Features for Image Classification

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.