Combination of the Normalized Difference Vegetation Index and surface temperature for regional scale European Forest cover mapping using AVHRR data

Forest and non-forest samples selected from an existing European forest map were classified using 8 months of cloud-screened European AVHRR data divided into 82 ecological/climatic strata. Consistently higher mean monthly forest/non-forest classification accuracies were found when the samples were classified using Normalized Difference Vegetation Index (NDVI) and surface temperature ( T ) data rather than using NDVI or T data alone.     

High-resolution multispectral techniques for mapping former Little Ice Age terrestrial ice cover in the Canadian High Arctic

Multispectral classification approaches were applied to high-resolution ASTER (15 m) and ETM+ (30 m) imagery for the purpose of developing new techniques for mapping recently deglaciated LIA perennial ice cover in the Canadian Arctic. Four areas in the Queen Elizabeth Islands, with dissimilar surficial geology and diverse topographic complexity, were selected to test the efficacy of both sensors for mapping these subtle landscape features. Automated classification (band calculation) methods were found to be most effective on quartzitic sandstone and siliceous crystalline bedrock, whereas, semi-automated (supervised classification) techniques were most successful on substrates comprised primarily of carbonate lithologies. ASTER's superior spatial resolution yielded higher accuracies in topographically complex areas; however, ETM+ was more effective over a wider variety of substrate lithologies and topographic settings, with a mean overall accuracy of 91% (mean κ statistic = 0.71), compared to 87% (mean κ statistic = 0.60) for ASTER.     

NOAA-AVHRR data processing for the mapping of vegetation cover

The NOAA-AVHRR images have been widely used for global studies due to their low cost, suitable wavebands and high temporal resolution. Data from the AVHRR sensor (Bands 1 and 2) transformed to the Normalized Difference Vegetation Index (NDVI) are the most common product used in global land cover studies. The purpose of this Letter is to present the vegetation, soil, and shade fraction images derived from AVHRR, in addition to NDVI, to monitor land cover. Six AVHRR images from the period of 21 to 26 June 1993 were composed and used to obtain the above mentioned products over Sa o Paulo State, in the south-east of Brazil. Vegetation fraction component values were strongly correlated with NDVI values ( r 0.95; n 60). Also, the fraction image presented a good agreement with the available global vegetation map of Sao Paulo State derived from Landsat TM images.     

Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index

Pairs of Landsat Multispectral Scanner System images from seven contrasting areas were analysed using scale variance analysis to determine the spatial frequencies present. Specifically images of the Normalized Difference Vegetation Index (NDVI) were analysed, which is sensitive to vegetation activity. Analyses were performed on images for each of the two dates and change images derived by subtracting the NDVI values of the first images from those of the second date. As expected the change images were characterized by higher spatial frequencies than the images of individual dates, but this was only marked for four of the seven areas. Contrary to initial expectations, knowledge of the spatial frequency content of the images from the two dates could not be used to infer the spatial frequencies present in the change images and hence the spatial resolutions needed for detecting change in the NDVI.     

An algorithm for snow and ice detection using AVHRR data An extension to the APOLLO software package

Abstract

The AVHRR (Advanced Very High Resolution Radiometer) Processing scheme Over Land, cLbud and Ocean (APOLLO) is used to extract surface and cloud parameters from satellite data. Before these parameters can be computed, it is necessary to distinguish between land and ocean surfaces and to apply algorithms for the detection of partially cloudy and cloud-filled pixels. In regions with seasonal or permanent snow and ice coverage the separation of clouds becomes much more difficult or often impossible. For this reason, and to find cloud-free and partly cloudy snow and ice pixels,- a day-time algorithm has been developed which uses all five AVHRR channels as follows: The threshold testing of the reflected part of channel-3 radiance leads to a definite distinction between snow/ice and water clouds due, to the clouds much higher reflectivity at 3.7 μm. The detection; of sea ice is based on threshold tests of visible reflectances and, in particular, of the temperature difference between channels-4 and -5. Snow is identified if a high visible reflectance is combined with a low reflectance in channel-3 and with a ratio of channel-2 to channel-1 reflectances similar to that of a cloud. The latter criterion is also mostly suitable to distinguish between snow-covered and snow-free ice areas. Some tests of this algorithm applied to AVHRR data from the 1987 Baltic Sea ice season have shown reasonable classification results with the exception of a few areas with ice clouds or with ice topped water clouds.     

Assessing the information content of multiangle satellite data for mapping biomes: II. Theory

The insights gained from present land cover classification activities suggest integration of multiangle data into classification attempts for future progress. Land cover types that exhibit distinct signatures in the space of remote sensing data facilitate unambiguous identification of cover types. In this two-part series, we develop a theme for consistency among cover type definitions, uniqueness of their signatures, and physics of the remote sensing data. In the first part, Zhang et al.'s [Remote Sens. Environ., in press.] empirical arguments in support of the consistency principle were presented. This part provides a theoretical justification of the consistency requirements. Radiative transfer best explains the physics of the processes operative in the generation of the signal in the optical remote sensing data. Biome definitions given in terms of variables that this theory admits and the use of the transport equation to interpret biome signatures guarantee the consistency requirements. It is shown in this paper that three metrics of the biome angular signature in the spectral space—location, angular signature slope (ASSI), and length (ASLI) indices—are related to eigenvalues and eigenvectors of the transport equation. These variables allow a novel parameterization of canopy structure based on the partitioning of the incident radiation among canopy absorption, transmission, and reflection. Consistency between cover type definitions and uniqueness of their signatures with the physics of the remote sensing data is required not only to reduce ambiguity in land cover identification, but also to directly relate land cover type to biophysical and biogeochemical processes in vegetation canopies.     

A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data

Classification of remotely sensed data involves a set of generalization processes, i.e. reality is simplified to a set of a few classes that are relevant to the application under consideration. This article introduces an approach to image classification that uses a class hierarchy structure for mapping unit definition at different generalization levels. This structure is implemented as an operational relational database and allows querying of more detailed land cover/use information from a higher abstraction level, which is that viewed by the map user. Elementary mapping units are defined on the basis of an unsupervised classification process in order to determine the land cover/use classes registered in the remotely sensed data. Mapping unit composition at different generalization levels is defined on the basis of membership values of sampled pixels to land cover/use classes. Unlike fuzzy classifications, membership values are presented to the user at mapping unit level.     

A comparative study of the effects of data reduction on terrain cover mapping from LANDSAT

Abstract

Using LANDSAT-1 data for an area around Esfahan, central Iran, the effect which an hierarchical cascaded clustering algorithm has upon terrain cover classification is examined.     

Assessing the information content of multiangle satellite data for mapping biomes: I. Statistical analysis

The insights gained from present land cover classification activities suggest integration of multiangle data into classification attempts for future progress. Land cover types that exhibit distinct signatures in the space of remote sensing data facilitate unambiguous identification of cover types. In this first part, we develop a theme for consistency between cover type definitions, uniqueness of their signatures, and physics of the remote sensing data. The idea of angular signatures in spectral space is proposed to provide a cogent synthesis of information from spectral and angular domains. Three new metrics, angular signature slope (ASSI), length (ASLI), and intercept indices, are introduced to characterize biome signatures. The statistical analyses with these indices confirm the idea that incorporation of the directional variable should improve biome classification result. The consistency principle is tested with the Multiangle Imaging SpectroRadiometer (MISR) leaf area index (LAI) algorithm by examining retrievals when both unique and nonunique signatures are input together with a land cover map. It is shown that this requirement guarantees valid retrievals. Part II provides a theoretical basis for these concepts [Zhang et al., Remote Sens. Environ., in press.].     

Potensoft: MATLAB-based software for potential field data processing, modeling and mapping

An open-source software including an easy-to-use graphical user interface (GUI) has been developed for processing, modeling and mapping of gravity and magnetic data. The program, called Potensoft, is a set of functions written in MATLAB. The most common application of Potensoft is spatial and frequency domain filtering of gravity and magnetic data. The GUI helps the user easily change all the required parameters. One of the major advantages of the program is to display the input and processed maps in a preview window, thereby allowing the user to track the results during the ongoing process. Source codes can be modified depending on the users' goals. This paper discusses the main features of the program and its capabilities are demonstrated by means of illustrative examples. The main objective is to introduce and ensure usage of the developed package for academic, teaching and professional purposes.     

LAPT: A locality-aware page table for thread and data mapping

The performance and energy efficiency of current systems is influenced by accesses to the memory hierarchy. One important aspect of memory hierarchies is the introduction of different memory access times, depending on the core that requested the transaction, and which cache or main memory bank responded to it. In this context, the locality of the memory accesses plays a key role for the performance and energy efficiency of parallel applications. Accesses to remote caches and NUMA nodes are more expensive than accesses to local ones. With information about the memory access pattern, pages can be migrated to the NUMA nodes that access them (data mapping), and threads that communicate can be migrated to the same node (thread mapping).In this paper, we present LAPT, a hardware-based mechanism to store the memory access pattern of parallel applications in the page table. The operating system uses the detected memory access pattern to perform an optimized thread and data mapping during the execution of the parallel application. Experiments with a wide range of parallel applications (from the NAS and PARSEC Benchmark Suites) on a NUMA machine showed significant performance and energy efficiency improvements of up to 19.2% and 15.7%, respectively, (6.7% and 5.3% on average).     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data

There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km² up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 10³ and 140 × 10³ km², respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.     

Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data

Snow and glaciers in the mountain watersheds of the Tarim River basin in western China provide the primary water resources to cover the needs of downstream oases. Remote sensing provides a practical approach to monitoring the change in snow and glacier cover in those mountain watersheds. This study investigated the change in snow and glacier cover in one such mountain watershed using multisource remote-sensing data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat (Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+)), Corona, and Google Earth^TM imagery. With 10 years’ daily MODIS snow-cover data from 2002 to 2012, we used two de-cloud methods before calculating daily snow-cover percentage (SCP), annual snow-cover frequency (SCF), and annual minimum snow-cover percentage (AMSCP) for the watershed. Mann–Kendall analysis showed no significant trend in any of those snow-cover characterizations. With a total of 22 Landsat images from 1967 to 2011, we used band ratio and supervised classification methods for snow classification for Landsat TM/ETM+ images and MSS images, respectively. The Landsat snow-cover data were divided into two periods (1976–2002 and 2004–2011). Statistical tests indicated no significant difference in either the variance or mean of SCPs between the two periods. Three glaciers were identified from Landsat images of 1998 and 2011, and their total area increased by 12.6%. In addition, three rock glaciers were also identified on both the Corona image of 1968 and the Google high-resolution image of 2007, and their area increased by 2.5%. Overall, based on multisource remote-sensing data sets, our study found no evidence of significant changes in snow and glacier cover in the watershed.     

A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e

An inversion procedure is presented for estimating surface soil water content (as surface moisture availability, Mo ), fractional vegetation cover ( Fr ), and the instantaneous surface energy fluxes, using remote multispectral measurements made from an aircraft. The remotely derived values of these fluxes and the soil water content are compared with field measurements from two intensive field measurement programs FIFE and MONSOON '90. The measurements from the NS001 multispectral radiometer were reduced to fractional vegetation cover, surface soil water content (surface moisture availability), and turbulent energy fluxes, with the application of a soil vegetation atmosphere transfer (SVAT) model. A further step in the inversion process involved 'stretching' the SVAT results between pre-determined boundaries of the distribution of normalized difference vegetation index (NDVI) and surface radiant temperature ( To ). Comparisons with measurements at a number of sites from two field experiments show standard errors, between derived and measured fluxes, generally between 25 and 55Wm-2, or about 10-30 per cent of the magnitude of the fluxes and for surface moisture availability of 16 per cent.     

A comparison of optimization heuristics for the data mapping problem

In the paper we compare the performance of six heuristics with suboptimal solutions for the data distribution of two dimensional meshes that are used for the numerical solution of partial differential equations (PDEs) on multicomputers. The data mapping heuristics are evaluated with respect to seven criteria covering load balancing, interprocessor communication, flexibility and ease of use for a class of single-phase iterative PDE solvers. Our evaluation suggests that the simple and fast block distribution heuristic can be as effective as the other five complex and computational expensive algorithms. © 1997 by John Wiley & Sons, Ltd.     

A tool for mapping and spatio-temporal analysis of hydrological data

There is a need in water sciences for computational tools to integrate large spatially distributed datasets to provide insight into the spatial and temporal domains of the data while allowing visualization, analysis in the spatial and temporal dimensions, data metrics, and pattern recognition in the same application. Spatial and temporal variability of hydrological processes as well as the associated phenomena transport is better represented in high spatio-temporal resolution datasets. A conceptual data model and analysis tool, SPELLmap, was developed at the USDA Agricultural Research Service, Grazinglands Research Laboratory using the Delphi programming language to rapidly process, manipulate, analyze, and visualize large geo-located datasets. SPELLmap integrates the spatial and temporal domains of hydrological data to perform analyses in space and time while providing data metrics. SPELLmap has the capacity to represent three or four dimensional problems using a layer data structure. Three examples to illustrate SPELLmap functionalities were provided for the raster and raster-to-network domains. SPELLmap can be used for data interpolation, visualization, gridding, pattern recognition, and data metrics in integrated environmental modeling problems.     

Narrowband-to-broadband albedo conversion for glacier ice and snow: equations based on modeling and ranges of validity of the equations

In this paper, we propose equations for narrowband-to-broadband (NTB) albedo conversion for glacier ice and snow for four types of satellite sensors: thematic mapper (TM), advanced very high resolution radiometer (AVHRR), moderate resolution imaging spectroradiometer (MODIS), and multi-angle imaging spectroradiometer (MISR). We do this on the basis of spectral albedos and incident spectral irradiances generated with radiative-transfer models of the (sub-)surface (a two-stream model) and the atmosphere, respectively. First, we establish equations for reference values of atmospheric components and the surface elevation. These equations describe measurements with root-mean-square differences of ∼0.016. We then show that the “reference equations” also perform well when total ozone and aerosol optical depth are changed with respect to the reference. The negative effect of humidity and elevation variations on the performance of the equations can be eliminated by adding a correction term. We argue that narrowband albedos are much less sensitive to variations in the incident spectral irradiance than broadband albedos. Hence, our conclusions about the effects of variations in atmospheric composition and elevation are also valid for equations for NTB conversion proposed in other papers.     

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52-95.4 and > 63-87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1-11.6%) and user's (+ 8.4-18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant (p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.     

Situvis: A sensor data analysis and abstraction tool for pervasive computing systems

Pervasive systems are large-scale systems consisting of many sensors capturing numerous types of information. As this data is highly voluminous and dimensional, data analysis tasks can be extremely cumbersome and time-consuming. Enabling computers to recognise real-world situations is an even more difficult problem, involving not only data analysis, but also consistency checking. Here we present Situvis, an interactive visualisation tool for representing sensor data and creating higher-level abstractions from the data. This paper builds on previous work, Clear et al. (2009) [8] through evolved tool functionality and an evaluation of Situvis. A user-trial consisting of 10 participants shows that Situvis can be used to complete the key tasks in the development process of situation specifications in over 50% less time than an improvised alternative toolset.