Probability sampling protocol of classification maps from spaceborne/airborne image

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperT (SIAMT) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMT by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMT pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMT by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMT software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines.

A composite DOP approach to excluding bottom reflectance in mapping water parameters of shallow coastal zones from TM imagery

In this paper we proposed a composite depth of penetration (DOP) approach to excluding bottom reflectance in mapping water quality parameters from Landsat thematic mapper (TM) data in the shallow coastal zone of Moreton Bay, Queensland, Australia. Three DOPs were calculated from TM1, TM2 and TM3, in conjunction with bathymetric data, at an accuracy ranging from +/-5% to +/-23%. These depths were used to segment the image into four DOP zones. Sixteen in situ water samples were collected concurrently with the recording of the satellite image. These samples were used to establish regression models for total suspended sediment (TSS) concentration and Secchi depth with respect to a particular DOP zone. Containing identical bands and their transformations for both parameters, the models are linear for TSS concentration, logarithmic for Secchi depth. Based on these models, TSS concentration and Secchi depth were mapped from the satellite image in respective DOP zones. Their mapped patterns are consistent with the in situ observed ones. Spatially, overestimation and underestimation of the parameters are restricted to localised areas but related to the absolute value of the parameters. The mapping was accomplished more accurately using multiple DOP zones than using a single zone in shallower areas. The composite DOP approach enables the mapping to be extended to areas as shallow as <3 m. (C) 2004 Elsevier Inc. All rights reserved.

Mapping water quality and substrate cover in optically complex coastal and reef waters: an integrated approach

Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs. (c) 2004 Elsevier Ltd. All rights reserved.

Linking riparian vegetation spatial structure in Australian tropical savannas to ecosystem health indicators: semi-variogram analysis of high spatial resolution satellite imagery

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone health. The objective of this work was to determine if the structural attributes of savanna riparian zones in northern Australia can be detected from commercially available remotely sensed image data. Two QuickBird images and coincident field data covering sections of the Daly River and the South Alligator River - Barramundie Creek in the Northern Territory were used. Semi-variograms were calculated to determine the characteristic spatial scales of riparian zone features, both vegetative and landform. Interpretation of semi-variograms showed that structural dimensions of riparian environments could be detected and estimated from the QuickBird image data. The results also show that selecting the correct spatial resolution and spectral bands is essential to maximize the accuracy of mapping spatial characteristics of savanna riparian features. The distribution of foliage projective cover of riparian vegetation affected spectral reflectance variations in individual spectral bands differently. Pan-sharpened image data enabled small-scale information extraction (< 6 m) on riparian zone structural parameters. The semi-variogram analysis results provide the basis for an inversion approach using high spatial resolution satellite image data to map indicators of savanna riparian zone health.

Remote sensing applied to slope stability in mountainous roads in Iran

The Alborz Mountain range separates the northern part of Iran from the southern part. It also isolates a narrow coastal strip to the south of the Caspian Sea from the Central Iran plateau. Communication between the south and north until the 1950's was via two roads and one rail link. In 1963 work was completed on a major access road via the Haraz Valley (the most physically hostile area in the region). From the beginning the road was plagued by accidents resulting from unstable slopes on either side of the valley. Heavy casualties persuaded the government to undertake major engineering works to eliminate ''black spots" and make the road safe. However, despite substantial and prolonged expenditure the problems were not solved and casualties increased steadily due to the increase in traffic using the road. Another road was built to bypass the Haraz road and opened to traffic in 1983. But closure of the Haraz road was still impossible because of the growth of settlements along the route and the need for access to other installations such as the Lar Dam. The aim of this research was to explore the possibility of applying Landsat MSS imagery to locating black spots along the road and the instability problems. Landsat data had not previously been applied to highway engineering problems in the study area. Aerial photographs are better in general than satellite images for detailed mapping, but Landsat images are superior for reconnaissance and adequate for mapping at the 1 :250,000 scale. The broad overview and lack of distortion in the Landsat imagery make the images ideal for structural interpretation. The results of Landsat digital image analysis showed that certain rock types and structural features can be delineated and mapped. The most unstable areas comprising steep slopes, free of vegetation cover can be identified using image processing techniques. Structural lineaments revealed from the image analysis led to improved results (delineation of unstable features). Damavand Quaternary volcanics were found to be the dominant rock type along a 40 km stretch of the road. These rock types are inherently unstable and partly responsible for the difficulties along the road. For more detailed geological and morphological interpretation a sample of small subscenes was selected and analysed. A special developed image analysis package was designed at Aston for use on a non specialized computing system. Using this package a new and unique method for image classification was developed, allowing accurate delineation of the critical features of the study area.

Integration of Satellite and Financial Data to Model Future Economic Impact of Citrus Crops (Final Project Report)

This study analyzed the health and overall landcover of citrus crops in Florida. The analysis was completed using Landsat satellite imagery available free of charge from the University of Maryland Global Landcover Change Facility. The project hypothesized that combining citrus production (economic) data with citrus area per county derived from spectral signatures would yield correlations between observable spectral reflectance throughout the year, and the fiscal impact of citrus on local economies. A positive correlation between these two data types would allow us to predict the economic impact of citrus using spectral data analysis to determine final crop harvests.

Inventory of thermo-erosional valleys and streams in three ice-rich permafrost lowlands adjacent to the Laptev Sea, with links to maps in ArcGIS format

This dataset provides an inventory of thermo-erosional landforms and streams in three lowland areas underlain by ice-rich permafrost of the Yedoma-type Ice Complex at the Siberian Laptev Sea coast. It consists of two shapefiles per study region: one shapefile for the digitized thermo-erosional landforms and streams, one for the study area extent. Thermo-erosional landforms were manually digitized from topographic maps and satellite data as line features and subsequently analyzed in a Geographic Information System (GIS) using ArcGIS 10.0. The mapping included in particular thermo-erosional gullies and valleys as well as streams and rivers, since development of all of these features potentially involved thermo-erosional processes. For the Cape Mamontov Klyk site, data from Grosse et al. [2006], which had been digitized from 1:100000 topographic map sheets, were clipped to the Ice Complex extent of Cape Mamontov Klyk, which excludes the hill range in the southwest with outcropping bedrock and rocky slope debris, coastal barrens, and a large sandy floodplain area in the southeast. The mapped features (streams, intermittent streams) were then visually compared with panchromatic Landsat-7 ETM+ satellite data (4 August 2000, 15 m spatial resolution) and panchromatic Hexagon data (14 July 1975, 10 m spatial resolution). Smaller valleys and gullies not captured in the maps were subsequently digitized from the satellite data. The criterion for the mapping of linear features as thermo-erosional valleys and gullies was their clear incision into the surface with visible slopes. Thermo-erosional features of the Lena Delta site were mapped on the basis of a Landsat-7 ETM+ image mosaic (2000 and 2001, 30 m ground resolution) [Schneider et al., 2009] and a Hexagon satellite image mosaic (1975, 10 m ground resolution) [G. Grosse, unpublished data] of the Lena River Delta within the extent of the Lena Delta Ice Complex [Morgenstern et al., 2011]. For the Buor Khaya Peninsula, data from Arcos [2012], which had been digitized based on RapidEye satellite data (8 August 2010, 6.5 m ground resolution), were completed for smaller thermo-erosional features using the same RapidEye scene as a mapping basis. The spatial resolution, acquisition date, time of the day, and viewing geometry of the satellite data used may have influenced the identification of thermo-erosional landforms in the images. For Cape Mamontov Klyk and the Lena Delta, thermo-erosional features were digitized using both Hexagon and Landsat data; Hexagon provided higher resolution and Landsat provided the modern extent of features. Allowance of up to decameters was made for the lateral expansion of features between Hexagon and Landsat acquisitions (between 1975 and 2000).

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh

Despite research that has been conducted elsewhere, little is known, to-date, about land cover dynamics and their impacts on land surface temperature (LST) in fast growing mega cities of developing countries. Landsat satellite images of 1989, 1999, and 2009 of Dhaka Metropolitan (DMP) area were used for analysis. This study first identified patterns of land cover changes between the periods and investigated their impacts on LST; second, applied artificial neural network to simulate land cover changes for 2019 and 2029; and finally, estimated their impacts on LST in respective periods. Simulation results show that if the current trend continues, 56% and 87% of the DMP area will likely to experience temperatures in the range of greater than or equal to 30°C in 2019 and 2029, respectively. The findings possess a major challenge for urban planners working in similar contexts. However, the technique presented in this paper would help them to quantify the impacts of different scenarios (e.g., vegetation loss to accommodate urban growth) on LST and consequently to devise appropriate policy measures.

Eruption centres of the Hamilton area of the Newer Volcanics Province, Victoria, Australia: Pinpointing volcanoes from a multifaceted approach to landform mapping

Volcanic eruption centres of the mostly 4.5 Ma-5000 BP Newer Volcanics Province in the Hamilton area of southeastern Australia were examined in detail using a multifaceted approach, including ground truthing and analysis of ArcGIS Total Magnetic Intensity and seamless geology data, NASA Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation models and Google Earth satellite image interpretation. Sixteen eruption centres were recognised in the Hamilton area, including three previously unrecorded volcanoes-one of which, the Cas Maar, constitutes the northernmost maar-cone volcanic complex in the Western Plains subprovince. Seven previously allocated eruption centres were placed into question based on field and laboratory observations. Three phases of volcanic activity have been suggested by other authors and are interpreted to correlate with ages of >4 Ma, ca 2 Ma and <0.5 Ma, which may be further subdivided based on preservation of outcrop. Geochemical compositions of the dominantly basaltic products become increasingly alkaline and enriched in incompatible elements from Phases 1 to 2, with Phase 3 eruptions both covering the entire geochemical range and extending into increasingly enriched compositions. This research highlights the importance of a multifaceted approach to landform mapping and demonstrates that additional volcanic centres may yet be discovered in the Newer Volcanics Province

VegMachine - putting pastoralists in the picture

Australia’s rangelands are the extensive arid and semi-arid grazing lands that cover approximately 70% of the Australian continent. They are characterised by low and generally variable rainfall, low productivity and a sparse population. They support a number of industries including mining and tourism, but pastoralism is the primary land use. In some areas, the rangelands have a history of biological decline (Noble 1997), with erosion, loss of perennial native grasses and incursion of woody vegetation commonly reported in the scientific and lay literature. Despite our historic awareness of these trends, the establishment of systems to measure and monitor degradation, has presented numerous problems. The size and accessibility of Australia’s rangeland often mitigates development of extensive monitoring programs. So, too, securing on-going commitment from Government agencies to fund rangeland monitoring activities have led to either abandonment or a scaled-down approach in some instances (Graetz et al. 1986; Holm 1993). While a multiplicity of monitoring schemes have been developed for landholders at the property scale, and some have received promising initial uptake, relatively few have been maintained for more than a few years on any property without at least some agency support (Pickup et al. 1998). But, ironically, such property level monitoring tools can contribute significantly to local decisions about stock, infrastructure and sustainability. Research in recent decades has shown the value of satellites for monitoring change in rangelands (Wallace et al. 2004), especially in terms of tree and ground cover. While steadily improving, use of satellite data as a monitoring tool has been limited by the cost of the imagery, and the equipment and expertise needed to extract useful information from it. A project now under way in the northern rangelands of Australia is attempting to circumvent many of the problems through a monitoring system that allows property managers to use long-term satellite image sequences to quickly and inexpensively track changes in land cover on their properties

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.