Showing papers in "Journal of remote sensing in 2010"

Remote sensing of coastlines: detection, extraction and monitoring

Abstract: This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings.

Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects

Abstract: A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program DMSP Operational Linescan System OLS imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.

Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

Abstract: For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region ( 500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.

Automated masking of cloud and cloud shadow for forest change analysis using Landsat images

Abstract: Accurate masking of cloud and cloud shadow is a prerequisite for reliable mapping of land surface attributes. Cloud contamination is particularly a problem for land cover change analysis, because unflagged clouds may be mapped as false changes, and the level of such false changes can be comparable to or many times more than that of actual changes, even for images with small percentages of cloud cover. Here we develop an algorithm for automatically flagging clouds and their shadows in Landsat images. This algorithm uses clear view forest pixels as a reference to define cloud boundaries for separating cloud from clear view surfaces in a spectral-temperature space. Shadow locations are predicted according to cloud height estimates and sun illumination geometry, and actual shadow pixels are identified by searching the darkest pixels surrounding the predicted shadow locations. This algorithm produced omission errors of around 1% for the cloud class, although the errors were higher for an image that had very low cloud cover and one acquired in a semiarid environment. While higher values were reported for other error measures, most of the errors were found around the edges of detected clouds and shadows, and many were due to difficulties in flagging thin clouds and the shadow cast by them, both by the developed algorithm and by the image analyst in deriving the reference data. We concluded that this algorithm is especially suitable for forest change analysis, because the commission and omission errors of the derived masks are not likely to significantly bias change analysis results.

Applicability of the SIFT operator to geometric SAR image registration

Abstract: The SIFT operator's success for computer vision applications makes it an attractive alternative to the intricate feature based SAR image registration problem. The SIFT operator processing chain is capable of detecting and matching scale and affine invariant features. For SAR images, the operator is expected to detect stable features at lower scales where speckle influence diminishes. To adapt the operator performance to SAR images we analyse the impact of image filtering and of skipping features detected at the highest scales. We present our analysis based on multisensor, multitemporal and different viewpoint SAR images. The operator shows potential to become a robust alternative for point feature based registration of SAR images as subpixel registration consistency was achieved for most of the tested datasets. Our findings indicate that operator performance in terms of repeatability and matching capability is affected by an increase in acquisition differences within the imagery. We also show that the proposed adaptations result in a significant speed-up compared to the original SIFT operator.

Evaluation of four remote sensing based land cover products over China

Abstract: Precise global/regional land cover mapping is of fundamental importance in studies of land surface processes and modelling. Quantitative assessments of the map quality and classification accuracy for existing land cover maps will help to improve accuracy in future land cover mapping. We compare and evaluate four land cover datasets over China. The datasets include the Version 2 global land cover dataset of IGBP, MODIS land cover map 2001, a global land cover map produced by the University of Maryland, and the land cover map produced by the global land cover for the year 2000 (GLC 2000) project coordinated by the Global Vegetation Monitoring Unit of the European Commission Joint Research Centre. The four maps used different classification systems, which made the comparison difficult. So we first aggregated these maps by reclassifying them using a unified legend system. A large-scale, i.e. 1:100 000 land cover map of China was used as the reference data to validate the four maps. The results show that the GLC2000 land cover map represents the highest accuracy. However, it has obvious local labelling errors and a zero labelling accuracy for the wetland type. The MODIS land cover map ranks second for type area consistency and third for sub-fraction overall accuracy compared with reference data, which may be affected by the local labelling error. The IGBP land cover map has good labelling accuracy, although it has a local labelling error and third consistency for type area. The labelling accuracy and type area consistency for the reference data of UMd land cover map is low. We conclude that the accuracies of all the datasets cannot meet the requirements of land surface modelling. For the reference data, i.e. the 1:100 000 land cover map, the classification system needs to be transferred to a well recognized one that has been used commonly in land surface modelling. In addition, we propose an information fusion strategy to produce a more accurate land cover map of China whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests

Abstract: Measuring individual trees can provide valuable information about forests, and airborne light detection and ranging (LiDAR) sensors have been used recently to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A crucial issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the three-dimensional (3D) 'cloud' of LiDAR points and adapts to irregular canopies sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy better than 0.8 and 1.5 m, respectively.

SWIR-based spectral indices for assessing nitrogen content in potato fields

Abstract: Nitrogen N is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. Spectral indices derived from handheld, airborne and spaceborne spectrometers are used for assessing N content. The majority of these indices are based on indirect indicators, mostly chlorophyll content, which is proven to be physiologically linked to N content. The current research aimed to explore the performance of new N spectral indices dependent upon the shortwave infrared SWIR region 1200–2500 nm, and particularly the 1510 nm band because it is related directly to N content. Traditional nitrogen indices NIs and four proposed new SWIR-based indices were tested with canopy-level spectral data obtained during two growing seasons in potato experimental plots in the northwest Negev, Israel. Above-ground biomass samples were collected at the same location of the spectral sampling to provide in-situ N content data. The performance of all indices was evaluated by three methods: 1 correlations between the existing and proposed indices and N as well as correlations among the indices themselves; 2 the root mean square error prediction RMSEP of the N content; and 3 the indices relative sensitivity S r to the N content. The results reveal a firm advantage for the proposed SWIR-based indices in their ability to predict, and in their sensitivity to, N content. The best index is one that combines information from the 1510 and 660 nm bands but no significant differences were found among the new SWIR-based indices.

The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution

Abstract: Satellite imagery of night-time lights provided by the US Air Force Defense Meteorological Satellite Program (DMSP), using the Operational Linescan System (OLS), has been used to estimate the spatial distribution of electricity consumption throughout Australia. For the period 1997 to 2002, there was very high correlation between state electricity consumption and night-time lights with an R2 value of 0.9346 at the state and territory spatial resolution. To increase the accuracy at which electricity consumption can be estimated at greater spatial resolution, an Overglow Removal Model (ORM) was developed to overcome the overglow effect caused by the dispersion of light into surrounding areas. The ORM makes use of the relationship between light source strength and the overglow/dispersion distance from the light source. As electricity consumption statistics at a greater spatial resolution than the state or territory level are not publically available in Australia, population statistics at the statistical local area (SLA) were used to demonstrate the increased accuracy of the ORM at returning the overglow light to its source, and, in turn, the accuracy of measuring electricity consumption. The ORM enabled an estimation of the electricity consumption of SLAs, greater than 10 km2, with an R2 value of 0.8732, which is a 25.4% increase in accuracy over untreated data before applying the ORM. The increase in accuracy of the location of the origin of night-time lights can enable better georeferencing of satellite imagery of night-time lights and greater accuracy in locating population centres and centres of economic development, and assist with electricity infrastructure planning in regions of the world where statistics are not readily available. The result of the ORM is a map of Australian electricity consumption, and an estimation of the regional electricity consumption for all SLAs greater than 10 km2 in size is included.

Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record

Abstract: A new set of reflectance calibration coefficients has been derived for channel 1 (0.63 μm) and channel 2 (0.86 μm) of the Advanced Very High Resolution Radiometer (AVHRR) flown on the National Oceanic and Atmospheric Administration (NOAA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar orbiting meteorological satellites. This paper uses several approaches that are radiometrically tied to the observations from National Aeronautics and Space Administration's (NASA's) Moderate Resolution Imaging Spectroradiometer (MODIS) imager to make the first consistent set of AVHRR reflectance calibration coefficients for every AVHRR that has ever flown. Our results indicate that the calibration coefficients presented here provide an accuracy of approximately 2% for channel 1 and 3% for channel 2 relative to that from the MODIS sensor.

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Abstract: Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI-and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy ( 90% pixel-level thematic agreement) and classified crop areas between the series of EVI-and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.

Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa

Abstract: Achieving food security in particular in Africa continues to pose a major challenge to humankind. It is clear that the future agricultural potential of Africa plays a critical role in meeting this challenge. Although crop yield can be estimated with a degree of reliability using a limited sample of ground observations, the exact crop acreage and the spatial distribution are rarely available. Even though remote sensing offers the ability to produce a rapid and up-to-date land use and land cover database for agricultural monitoring, there are only a few countries in Africa where higher resolution satellite data such as Landsat have been used for land cover map production at the national level. However a number of global products have been produced which contain information on cropland extent. This paper will outline a comparison of four sources of land cover data to determine which product is the most suitable for agricultural monitoring and for the subsequent development of a crop mask. The land cover products used are the Global Land Cover Map (GLC-2000), the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MOD12V1), the SAGE (Center for Sustainability and the Global Environment) and the AFRICOVER dataset from the Food and Agriculture Organisation (FAO). Both the GLC-2000 and MODIS land cover products are at a resolution of 1 km2 while AFRICOVER, based on visual interpretation of 30-m resolution Landsat images, is available at a much finer resolution. The four land cover products are first aggregated to the same resolution so that they can be compared. The legend categories of the four land cover products in this study are reconciled using the method developed in Fritz and See [Fritz, S. and See, L., 2005, Comparison of land cover maps using fuzzy agreement. International Journal of Geographic Information Science, 19, pp. 787-807.] and See and Fritz [See, L.M. and Fritz, S., 2005, A user-defined fuzzy logic approach to comparing global land cover products. In 14th European Colloquium on Theoretical and Quantitative Geography, 9-12 September 2005, Lisbon, Portugal.] that allows overlap between legend definitions to be taken into account. Once the legend definitions between the different land cover products are reconciled, the maps are then compared with national and sub-national statistics. Analysis is undertaken at both continental and national scales as well as sub-national for Sudan and Eritrea. The study generally concludes that MODIS has the tendency to underestimate cropland cover when compared with FAO statistics or AFRICOVER data, whereas GLC-2000 tends to overestimate cropland cover in those countries that are located at the northern transition zone of subtropical shrubland and semi-desert areas. In this area MODIS and SAGE show a relatively similar cropland distribution. Even though the SAGE database has been calibrated with national statistics, it does not perform better than the other two datasets overall, and has highlighted the fact that the SAGE data show regional weaknesses and should be replaced in certain regions by more recent datasets such as GLC-2000 and MODIS, or ideally by a hybrid product that combines the best of the three products, depending upon the region and country.

A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

Abstract: This review paper evaluates the potential of hyperspectral remote sensing for assessing species diversity in homogeneous (non-tropical) and heterogeneous (tropical) forest, an increasingly urgent task. Existing studies of species distribution patterns using hyperspectral remote sensing have used different techniques to discriminate different species, in which the wavelet transforms, derivative analysis and red edge positions are the most important of them. The wavelet transform is used based on its effectiveness and determined as the most powerful technique to identify species. Furthermore, estimations of relationships between spectral values and species distributions using chemical composition of foliage, tree phenology, selection of signature training sites based on field measured canopy composition, selection of the best wavelet coefficient and waveband regions may be useful to identify different plant species. This paper presents a summary on the feasibility, operational applications and possible strategies of hyperspectral remote sensing in forestry, especially in assessing its biodiversity. The paper also reviews the processing and analysis of techniques for hyperspectral data in discriminating different forest tree species.

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Abstract: Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network HNN model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.

Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data

Abstract: Eutrophication is a serious environmental problem in Qiantang River, the largest river in the Zhejiang Province of southeast China. Increased phosphorus concentration is thought to be the major cause of water eutrophication. The objective of this study was to develop an empirical remote sensing model using Landsat Thematic Mapper (TM) data to estimate phosphorus concentration and characterize the spatial variability of the phosphorus concentration in the mainstream of Qiantang River. Field water quality data were collected across a spatial gradient along the river and geospatially overlaid with Landsat satellite images. Various statistical regression models were tested to correlate phosphorus concentration with a combination of other water quality indicators and remotely sensed spectral reflectance, including Secchi depth (SD) and chlorophyll-a (Chl-a) concentration. The optimal regression model was subsequently used to map and characterize the spatial variability of the total phosphorus (TP) concentration in the mainstream of Qiantang River. The results suggest that spectral reflectance from the Landsat satellite is spatially and implicitly correlated with phosphorus concentration (R2 = 0.77). The approach proved to be effective and has the potential to be applied over large areas for water quality monitoring.

Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression

Abstract: Lead (Pb) poisoning from anthropogenic sources continues to threaten the health of urban children. Mapping Pb distribution on a large scale is imperative to identify hotspots and reduce Pb poisoning. To assess the feasibility of using reflectance spectroscopy to map soil Pb and other heavy metal abundance, the relationship between surface soil metal concentrations and hyperspectral reflectance measurements was examined via partial least-squares regression (PLSR) modelling. Soil samples were taken from four study sites. Metal concentrations were determined by inductively coupled plasma-atomic-emission spectrometry (ICP-AES) analysis, and reflectance was measured with an ASD (Analytical Spectral Devices) field spectrometer covering the spectral region of 350-2500 nm. Pb displayed an exponential decrease as a function of distance from the roadway, demonstrating the depositional patterns from leaded gas combustion which remain on the landscape 20 years after the phase-out of leaded gasoline. Calibration samples were used to derive the PLSR algorithm, and validation samples assessed the model's predictive ability. The correlation coefficients between the lab-determined abundance and the abundance predicted from PLSR calibration for all metals except copper were at or above 0.970, with the correlation coefficient for Pb the highest of all metals (0.992). Manganese, zinc and Pb had significant coefficients of determination (0.808, 0.760 and 0.746, respectively) for the validation samples. These results suggest that Pb and other heavy metal concentrations can be retrieved from spectral reflectance at high accuracy. Reflectance spectroscopy thus has potential to map the spatial distribution of Pb abundance with the aim of improving children's health in an urban environment.

Estimation of tropical forest structure from SPOT-5 satellite images

Abstract: Predictions of tropical forest structure at the landscape level still present relatively high levels of uncertainty. In this study we explore the capabilities of high-resolution Satellite Pour l'Observation de la Terre (SPOT)-5 XS images to estimate basal area, tree volume and tree biomass of a tropical rainforest region in Chiapas, Mexico. SPOT-5 satellite images and forest inventory data from 87 sites were used to establish a multiple linear regression model. The 87 0.1-ha plots covered a wide range of forest structures, including mature forest, with values from 74.7 to 607.1 t ha-1. Spectral bands, image transformations and texture variables were explored as independent variables of a multiple linear regression model. The R2s of the final models were 0.58 for basal area, 0.70 for canopy height, 0.73 for bole volume, and 0.71 for biomass. A leave-one-out cross-validation produced a root mean square. error (RMSE) of 5.02 m2 ha-1 (relative RMSE of 22.8%) for basal area; 3.22 m (16.1%) for canopy height; 69.08 m3 ha-1 (30.7%) for timber volume, and 59.3 t ha-1 (21.2%) for biomass. In particular, the texture variable 'variance of near-infrared' turned out to be an excellent predictor for forest structure variables.

An automated waveband selection technique for optimized hyperspectral mixture analysis

Abstract: Linear spectral mixture analysis SMA has been used extensively in remote sensing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing SZU, is presented and evaluated. Stable spectral features i.e. least sensitive to spectral variability are automatically selected for use in the mixture analysis based on a minimum InStability Index ISI criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R2—the coefficient of determination of the modelled versus the observed sub-pixel cover fractions—of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

Abstract: Sugarcane is a semi-perennial grass whose cultivation is characterized by an extended harvest season lasting several months leading to very high spatio-temporal variability of the crop development and radiometry. The objective of this paper is to understand this variability in order to propose appropriate spectral indicators for yield forecast. To do this, we used ground observations and Satellite Pour l‘Observation de la Terre SPOT4 and SPOT5 time series acquired monthly over a 2-year period over Reunion Island and Guadeloupe French West Indies. We showed that variations in the Normalized Difference Vegetation Index NDVI of sugarcane at the field scale are the result of the interaction between the sugarcane crop calendar and plant phenology in a given climatic environment. We linked these variations to crop variables measured in the field leaf area index and leaf colour, and derived simple, appropriate NDVI-based indicators of sugarcane yield components at the field scale cane yield and sugar content. For biomass forecast, the best correlation R2 = 0.78 was obtained with images acquired about 2 months before the harvest season, when all the fields are fully developed but before the maturation stage. For sugar content, a polynomial relationship R2 = 0.75 was observed between the field NDVI acquired during the maturation stage and sugar content in the stalk.

The multispectral separability of Costa Rican rainforest types with support vector machines and Random Forest decision trees

Abstract: Estimating the extent of tropical rainforest types is needed for biodiversity assessment and carbon accounting. In this study, we used statistical comparisons to determine the ability of Landsat Thematic Mapper (TM) bands and spectral vegetation indices to discriminate composition and structural types. A total of 144 old-growth forest plots established in northern Costa Rica were categorized via cluster analysis and ordination. Locations for palm swamps, forest regrowth and tree plantations were also acquired, making 11 forest types for separability analysis. Forest types classified using support vector machines (SVM), a theoretically superior method for solving complex classification problems, were compared with the random forest decision tree classifier (RF). Separability comparisons demonstrate that spectral data are sensitive to differences among forest types when tree species and structural similarity is low. SVM class accuracy was 66.6% for all forest types, minimally higher than the RF classifier (65.3%). TM bands and the Normalized Difference Vegetation Index (NDVI) combined with digital elevation data notably increased accuracies for SVM (84.3%) and RF (86.7%) classifiers. Rainforest types discriminated here are typically limited to one or two categories for remote sensing classifications. Our results indicate that TM bands and ancillary data combined via machine learning algorithms can yield accurate and ecologically meaningful rainforest classifications important to national and international forest monitoring protocols.

On the altitude dependence of the temperature scaling behaviour at the global troposphere

Abstract: Detrended fluctuation analysis is applied to the mean monthly temperature values, over different heights of the global troposphere, during 1980-2004, to search for self-similarity properties. The results show that the tropospheric temperature anomalies obey persistent long-range power-law correlations for time-scales longer than about four months and shorter than about six years. This suggests that the temperature fluctuations over small time periods (i.e. a few months) are related to those over longer time periods (i.e. a few years). In addition, the long-range power-law persistence in the global tropospheric temperature fluctuations becomes stronger as the altitude increases.

Object-oriented method for urban vegetation mapping using IKONOS imagery

Abstract: Urban vegetation plays an important role in quality of life. However, accurate urban vegetation maps cannot be easily acquired from multispectral remotely sensed data alone because the spectral bands are indistinct among different vegetation classes. This study aimed to detect urban vegetation categories from IKONOS imagery based on an object-oriented method that can integrate both spectral and spatial information of objects in the classification procedure and thus can improve classification capability. Considering the characteristics of urban vegetation in IKONOS imagery, a two-scale segmentation procedure was designed to obtain 'objects', and the feature set for vegetation objects was constructed. Redundant information among the features was then removed by using correlation analysis, the Jeffries-Matusita (J-M) distance and principal component transformation (PCT). Finally, the vegetation objects were identified by the classification and regression tree (CART) model. The results show that IKONOS imagery can be used to map vegetation types with a total accuracy of 87.71%. Segmentations involving both micro and macro scales could acquire better vegetation objects than using a single scale. The correlation analysis combined with the J-M distance and PCT was efficient in optimizing the feature set. The rule-based classification method is suitable for identifying urban vegetation types using the feature set with a complex structure.

Feature-based image registration using the shape context

Abstract: Image registration is a basic and important process for multi-sensor or multi-temporal remote sensing. In this article, a new feature-based method named shape context is proposed for airborne multi-sensor image matching. This method has been found to be robust in hand-written digit and object recognition, and it is now introduced into remote-sensing image matching after some adjustments. In the proposed method, control points (CPs) are extracted on the reference image, and edge features are extracted on the reference and the sensed image, respectively. The shape context exploits feature similarity between circular regions of the two images to find corresponding CPs on the sensed image. Finally, the sensed image is warped according to the CPs using thin-plate spline interpolation. This method is successfully applied to register airborne optical and multi-band synthetic aperture radar (SAR) images in two experiments, and the results demonstrate its robustness and accuracy.

Very early prediction of wine yield based on satellite data from VEGETATION

Abstract: A forecast model for estimating the annual variation in regional wine yield based on remote sensing was developed for the main wine regions of Portugal. Normalized Difference Vegetation Index (NDVI) time-series obtained by the VEGETATION sensor, on board the most recent Satellite Pour l'Observation de la Terre (SPOT) satellite, over the period 1998-2008 were used for four test sites located in the main wine regions of Portugal: Douro (two sites), Vinhos Verdes and Alentejo. The CORINE (Coordination of Information on the Environment) Land Cover maps from 2000 were initially used to select the suitable regional test sites. The NDVI values of the second decade of April of the previous season to harvest were significantly correlated to the wine yield for all studied regions. The relation between the NDVI and grapevine induction and differentiation of the inflorescence primordial or bud fruitfulness during the previous season is discussed. This NDVI measurement can be made about 17 months before harvest and allows us to obtain very early forecasts of potential regional wine yield. Appropriate statistical tests indicated that the wine yield forecast model explains 77-88% of the inter-annual variability in wine yield. The comparison of official wine yield and the adjusted prediction models, based on 36 annual data records for all regions, shows an average spread deviation between 2.9% and 7.1% for the different regions. The dataset provided by the VEGETATION sensor proved to be a valuable tool for vineyard monitoring, mainly for inter-annual comparisons on a regional scale due to their high data acquisition rates and wide availability. The accuracy, very early indication and low-cost of the developed forecast model justify its use by the winery and viticulture industry.

Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Abstract: Remote sensing is widely used in coastal management. Lyzenga's model has been traditionally used to explain the relationship between bottom surface reflectance and the radiance level measured by satellite. Due to its central assumption, this model lacks accuracy compared with the other radiative transfer models. Nonetheless, it enables, with a single and simple equation, representation of the multiple optical processes taking place in coastal areas. Mapping processes associated with this model may include radiometric correction, a technique previously pointed out as a major driver of mapping accuracy. Radiometric correction is generally based on a depth-invariant index, efficient for clear waters (Jerlov water type I to II) but largely unsuitable when transparency decreases (Jerlov water type II to III). In order to overcome this problem, we developed a new index for radiometric correction, which combines bathymetry data with attenuation coefficients. The improved efficiency of our model with regard to the traditional depth invariant index was demonstrated through two case studies: Funakoshi Bay (Japan; Jerlov water type II) and the Gabes Gulf part located off Mahares (Tunisia; Jerlov water type II to III).

A maximum entropy approach to one-class classification of remote sensing imagery

Abstract: In remote sensing classification there are situations when users are only interested in classifying one specific land type without considering other classes, which is referred to as one-class classification. Traditional supervised learning requires all classes that occur in the image to be exhaustively labelled and hence is inefficient for one-class classification. In this study we investigate a maximum entropy approach (MAXENT) to one-class classification of remote sensing imagery, i.e. classifying a single land class (e.g. urban areas, trees, grasses and soils) from an aerial photograph with 0.3 m spatial resolution. MAXENT estimates the Gibbs probability distribution that is proportional to the conditional probability of being positive. A threshold for generating binary predictions can be determined based on the omission rate of a validation set. The results indicate that MAXENT provides higher classification accuracy than the one-class support vector machine (OCSVM). MAXENT does not require other land classes for training. Its input is only a set of training samples of the specific land class of interest, as well as a set of known constraints on the distribution. Therefore, the effort of manually collecting training data for classification can be significantly reduced.

It's all about the format-unleashing the power of RAW aerial photography

Abstract: Current one-shot, handheld Digital Still Cameras (DSCs) generally offer different file formats to save the captured frames: Joint Photographic Experts Group (JPEG), RAW and/or Tag(ged) Image File Format (TIFF). Although the JPEG file format is the most commonly used file format worldwide, it is incapable of storing all original data, something that also occurs, to a certain extent, for large TIFF files. Therefore, most professional photographers prefer shooting RAW files, often described as the digital photography's equivalent of a film negative. As a RAW file contains the absolute maximum amount of information and original data generated by the sensor, it is the only scientifically justifiable file format. In addition, its tremendous flexibility in both processing and post-processing also makes it beneficial from a workflow and image quality point of view. On the other hand, large file sizes, the required software and proprietary file formats remain hurdles that are often too difficult to overcome for many photographers. Aerial photographers who shoot with handheld DSCs should be familiar with both RAW and other file formats, as their implications cannot be neglected. By outlining the complete process from photon capture to the generation of pixel values, additionally illustrated by real-world examples, the advantages and particularities of RAW aerial photography should become clear.

Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir

Abstract: This research estimates phytoplankton pigment concentrations (chlorophyll-a (chl-a) and phycocyanin (PC)) from hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery. AISA images were acquired for a meso-eutrophic reservoir in Central Indiana, USA. Concurrent with the airborne image acquisition, in situ water samples and reflectances were collected. The water samples were subsequently analysed for pigment concentrations, and in situ measured reflectance spectra were used for calibrating the AISA images. Spectral indices, derived from the AISA reflectance spectra, were regressed against the measured pigment concentrations to derive algorithms for estimating chl-a and PC. The relationship between the pigment concentrations and the spectral indices were analysed and evaluated. The results indicate that the highest correlation occurred between chl-a and a near-infrared to red ratio (coefficient of determination R2 = 0.78) and between PC and the reflectance trough at 628 nm (R2 = 0.80). The relationship between PC and the reflectance at 628 nm provides an approach to the estimation of cyanobacteria concentration from hyperspectral imagery, which facilitates water-quality authorities or management agencies in making well-informed management decisions.

Remote-sensing reflectance characteristics of highly turbid estuarine waters – a comparative experiment of the Yangtze River and the Yellow River

Abstract: An outdoor tank experiment is carried out to analyse the interrelationships between remote-sensing reflectance and sediment characteristics in the highly turbid waters of the Yangtze River and the Yellow River estuaries. The results show that the sensitivity of remote-sensing reflectance to water turbidity is inversely related to suspended sediment concentration (SSC). SSC estimation in the highly turbid waters (SSC > 0.15 g l-1) is best achieved by using ocean colour ratios, especially the ratio at 810 nm: 700 nm. The effect of particle size of suspended sediment matter (SSM) on the observed remote-sensing reflectance is significant and depends on wavelengths and a SSC range. The mineral composition of SSM has a weak effect on observed reflectance in comparison to that of particle size.

Suspended particulate matter in Lake Erie derived from MODIS aquatic colour imagery

Abstract: This paper explores the use of Moderate Resolution Imaging Spectroradiometer MODIS wavebands in the red/near-infra-red for estimating concentrations of suspended particulate matter SPM in the moderately turbid, optically complex waters of Lake Erie. Observations show that at wavelengths shorter than 550 nm, more than 50% of the absorption signal is accounted for by dissolved organic matter and phytoplankton, confirming that algorithms incorporating these wavelengths may not be appropriate for these waters. Single band and band ratios at wavelengths greater than 667 nm are tested for their suitability for monitoring SPM concentrations in these waters. A simplified regional semi-analytical model is utilized which is independent of variations in dissolved organic matter and chlorophyll absorption, enabling estimates of SPM concentrations from MODIS water-leaving radiance at 748 nm with an average root mean square RMS error of 40%. Knowledge of the vertical distribution of particles enables estimates of total water column suspended loads which are then related to wind re-suspension events. The method is applied to MODIS water-leaving radiance at 748 nm to produce a time series of surface and total water column suspended loads in Lake Erie for the period 2003–2007.