SPOT HRV Data Research Articles

Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias.

Applying vegetation indices to detect high water table zones in humid warm-temperate regions using satellite remote sensing

This study developed a geobotanical remote sensing method for detecting high water table zones using differences in the conditions of forest trees induced by groundwater supply in a humid warm-temperate region. A new vegetation index (VI) termed added green band NDVI (AgbNDVI) was proposed to discriminate the differences. The AgbNDVI proved to be more sensitive to water stress on green vegetation than existing VIs, such as SAVI and EVI2, and possessed a strong linear correlation with the vegetation fraction. To validate a proposed vegetation index method, a 23km2 study area was selected in the Tono region of Gifu prefecture, central Japan. The AgbNDVI values were calculated from atmospheric corrected SPOT HRV data. To correctly extract high VI points, the influence factors on forest tree growth were identified using the AgbNDVI values, DEM and forest type data; the study area was then divided into 555 domains chosen from a combination of the influence factors and forest types. Thresholds for extracting high VI points were defined for each domain based on histograms of AgbNDVI values. By superimposing the high VI points on topographic and geologic maps, most high VI points are clearly located on either concave or convex slopes, and are found to be proximal to geologic boundaries—particularly the boundary between the Pliocene gravel layer and the Cretaceous granite, which should act as a groundwater flow path. In addition, field investigations support the correctness of the high VI points, because they are located around groundwater seeps and in high water table zones where the growth increments and biomass of trees are greater than at low VI points.

Monitoring Gross Primary Production at the Surface of Lake Shinji Using Multi-Date SPOT HRV Data

Monitoring Gross Primary Production at the Surface of Lake Shinji Using Multi-Date SPOT HRV Data

Error propagation in atmospheric correction due to azimuthal angle simplification of lookup tables

Though atmospheric correction model version 2 (ATCOR2) and version 3 (ATCOR3), developed by Richter (International Journal of Remote Sensing, 18, 1099–1111, 2007), have been successfully applied to various applications, these models simplify the azimuthal dependence of atmospheric functions in the atmospheric correction (AC) algorithm by using lookup tables (LUT) only at relative azimuthal angles of 30° and 150° for SPOT HRV data. Thus, they may generate errors as indicated by Liu (Terrestrial, Atmospheric and Oceanic Sciences, 15, 89–96, 2004). Since the error of retrieved surface reflectance was not considered as the one propagated from the error of the retrieved aerosol optical depth, it may not truly account for the error in AC. In this paper, errors in the AC processing chain are considered. The results show that the errors are more significant than those in the study by Liu. The errors can be significant along not only the principal plane but also the cross‐principal plane, especially for urban aerosol with higher absorption than continental aerosol. Hence, the accuracy of AC functions considered by ATCOR2 and ATCOR3 for SPOT sensors needs to be improved. A simple and effective method to reduce the errors is also discussed.

Simulation Approaches for Burn Severity Estimation Using Remotely Sensed Images

Traditional field-based methods for estimating burn severity are time-consuming, labour intensive and normally limited in spatial extent. Remotely sensed data provide a means to estimate severity levels across large areas, but it is critical to understand the causes of variability in spectral response with variations in burn severity. Since experimental measurements over a range of burn severities are difficult to obtain, the simulation tools provided by radiative transfer models (RTM) offer a promising alternative to better understand factors affecting burn severity reflectances. Two-layer RTM, such as the combined leaf (Prospect) and canopy (Kuusk) model can be used to simulate a wide range of burn severity conditions. Specifically, the effects of changes in soil background, leaf color and leaf area index as a result of different burn severities can be simulated with two-layer RTM models in the forward mode. This approach can provide a deeper understanding of the effects of each factor in satellite-sensed reflectance, as well as their relative importance. Additionally, RTMs can also be used in an inverse mode, and therefore burn severities can be retrieved from remotely sensed data by comparing measured and simulated reflectance. Examples of these two-way modes of RTMs are presented in this paper. Burn severity was measured using the Composite Burn Index (CBI). The Kuusk model was used to simulate scenarios of different combinations of changes in the substrate, upper vegetation, and lower vegetation strata. This paper shows some results of inverting the simulated reflectances to estimate CBI from calibrated reflectance derived from different satellite sensors. The case study is based on a large forest fire that affected central Spain in July, 2005. Landsat-TM, SPOT-HRV, IRS-AWIFS, Envisat-MERIS and Terra-MODIS data were used for this retrieval. Determination coefficients (r2) values range between 0.436 (MODIS) to 0.629 (Landsat-TM), with lower precision for the intermediate range of CBI values.

Ground and space spectral measurements for assessing the semi-arid ecosystem phenology related to CO 2 fluxes of biological soil crusts

This paper reports on ranges of carbon dioxide (CO 2) activity in biological soil crusts (BSC) correlated with different ranges of the BSC's spectral reflectance throughout the phenological cycle of the year. Methodology is based on surface CO 2 exchange measurements, ground spectral measurements, and satellite images interpretation. Thirty-nine field campaigns, each of duration of 3 days, were conducted over the course of 2 years at a sand dunes and a loess environment of the northwestern Negev desert in Israel, in order to relate the CO 2 fluxes and the spectral signals to the seasonal phenology. The Normalized Difference Vegetation Index (NDVI) was derived from ground measurements of the BSC's reflectance and correlated with their CO 2 exchange data. A linear mixture model, incorporating the different contributions of the sites' ground features, was calculated and compared with SPOT-HRV data. From the ground measurements, fairly good correlations were found between the NDVI and the CO 2 fluxes on a seasonal scale. Hence, the NDVI successfully indicates the potential magnitude and capacity of the BSC's assimilation activity. The linear mixture model successfully describes the phenological cycles of the BSC, annual, and perennial plants and corresponds well to the satellite data. Moreover, the model enables annual changes of the phenology cycle and the growing season length to be distinguished. Both the linear mixture model and the derived NDVI values recorded the recovery of the BSC at the beginning of the wet season before annuals had germinated. Finally, it is concluded that a combination of CO 2 exchange measurements, linear mixture model, and NDVI values is suitable for monitoring BSC's productivity in arid regions.

Estimation of methane emission from West Siberian wetland by scaling technique between NOAA AVHRR and SPOT HRV

Wetlands are one of the most important ecosystems in the world and at the same time they are presumed to be a source of methane gas, which is one of the most important greenhouse gases. The West Siberian wetlands is the largest in the world and remote sensing techniques can play an important role for monitoring the wetland. High spatial resolution satellite data are effective for monitoring land cover type changes, but can't cover a wide area because of a narrow swath width. On the other hand, global scale data are indispensable in covering a large area, but are too coarse to get the detailed information due to the low spatial resolution. It is necessary to devise a method for the fusion of the data with different spatial resolutions for monitoring the scale-differed phenomena. In this paper, firstly, a SPOT HRV image near Plotnikovo mire was used to map four wetland ecosystems (birch forest, conifer forest, forested bog and open bog) supplemented by field observation. Then, spectral mixture analysis was performed between NOAA AVHRR and SPOT HRV data acquired on the same day. Secondly, field observations were scaled up with these different spatial resolution satellite data. Each of the wetland ecosystem coverage fraction at the sub-pixel level was provided by spectral mixture analysis. Field observation shows that the mean rate of CH 4 emission from forested bog and open bog averaged 21.1 and 233.1 (mg CH 4/m 2/day), respectively. The methane emission from the area was estimated by multiplying these average methane emission rates and the fraction coverage in each AVHRR pixel. Finally, the total methane emission over AVHRR coverage was estimated to be 9.46 (10 9 g CH 4/day) and the mean methane emission over AVHRR coverage was calculated as 59.3 (mg CH 4/m 2/day). We could conclude that this mean value is within the probabilistic variability as compared with the airborne measurement results.

SPOT HRV data analysis for detecting earthquake-induced changes in Izmit, Turkey

SPOT HRV (XS and PAN) images acquired on 17 July 1999 and 20 August 1999 were used to detect earthquake-induced changes on 17 August 1999 in Izmit, Turkey. The pre-event and post-event images were geometrically and radiometrically calibrated to each other. In order to preserve the spatial and spectral information of the two data-sets the multispectral and panchromatic images were merged. The change areas were detected by subtracting the image brightness values for the near-infrared channel (Band 3) of the merged July image from that of the August image. The overall accuracy of the change image was found to be 83%. Upon finding the change areas, the emphasis was placed on the detection of the damaged building blocks within a selected urban area in the change image using automated analysis techniques. Our results show that the difference image of the merged pre- and post-event SPOT HRV data can successfully reveal the location of the earthquake induced changes. The procedure also demonstrates that the building blocks that contain collapsed buildings can be detected and categorized according to pre-defined damage conditions through the analysis of the merged SPOT HRV data.

Detection of a landslide movement as geometric misregistration in image matching of SPOT HRV data of two different dates

A landslide movement was detected as geometric misregistration in image matching between two SPOT HRV panchromatic image data of different acquisition dates. The test site is an active landslide in Itaya area, Yamagata Prefecture, northern Japan. The average rate of the landslide movement is approximately 1 cm per day based upon differential GPS measurements. Pixel level and subpixel level image matching was applied to an HRV image pair obtained on 1 November 1987 and 29 October 1993. As a result, the landslide movement was successfully detected as misregistration vectors between the two HRV scenes. The displacement is approximately 20 m to 30 m over the 6-year period between 1987 and 1993 and is consistent with the landslide movement inferred from the GPS measurements.

Monitoring the atmospheric aerosol optical depth with SPOT data in complex terrain

Two methods - the structure function method and the dispersion coefficient method, both based on the contrast reduction method - are utilized with accompanying sunphotometer observation data and shown to cause a significant reduction in the errors produced when retrieving the aerosol optical depth. When applying the structure function method on the multitemporal SPOT HRV data in estimating the atmospheric aerosol optical depth, the result is affected significantly by the observed geometry, landuse change, non-lambertian surface and terrain effect. Thus, an improved version is proposed in this paper to reduce these effects. Instead of using a single direction method, we expanded it into a multidirectional method that produced a more complete picture of the structure function. In addition, an 'optimal distance index' was further introduced to truncate the abnormal part of the structure functions in some cases. The result shows that the mean error can be reduced from 19% to 6.5% when compared with ground measurements. In addition, the dispersion coefficient method was also examined and improved with a linear regression to correct its bias. Results show than the mean error decreases from 85% to 24%.

Applying SPOT data to estimate the aerosol optical depth and air quality

The improvement in the structure function method for retrieving aerosol optical depth (AOD) with SPOT HRV data and its application in air quality monitoring are highlighted in this paper. Generally speaking, estimation of the aerosol optical depth will be affected by the temporal change of surface canopy, observation geometry and terrain effect when applying the contrast reduction method to the multi-temporal satellite image set. In order to reduce the errors induced by such effects, the single-directional structure function is replaced by the multi-directional mode, which can describe the real characteristics of the surface structure more completely. Comparison of the results with in-site observations show a significant improvement in the accuracy of the retrieved AOD. Furthermore, due to the linear relationship between aerosol optical depth and turbidity coefficient, satellite images can be employed for monitoring air quality. Application of the method is demonstrated with a case study situated around the northern Taiwan area.

Intermountain Plant Community Classification Using Landsat TM and SPOT HRV Data

Rangeland plant communities of the Intermountain West differ in their ecology and management requirements. Successful management of extensive areas at plant community-level resolution first requires an efficient, cost-effective means of plant community classification and mapping. We evaluated the influence of image acquisition date and satellite imaging system on the accuracy of plant community maps created from multispectral satellite imagery of Reynolds Creek Experimental Watershed (RCEW) (234 km2) in southwestern Idaho. Maps delineating 6 native and 2 non-native Intermountain plant communities were created from Landsat 5 TM and SPOT 3 HRV data using a maximum likelihood classification procedure. Map accuracy was assessed using ground reference points. Maps created from satellite data acquired during dry-down (early August) had higher overall accuracy (average = 70.5%) than from data acquired during peak growth (early June) (average = 54.4%). Overall accuracy of maps generated by Landsat (average = 60.1%) and SPOT (average = 65.5%) were statistically similar. Given their broad spatial coverages (3,600 to 31,450 km2 scene(-1), respectively), moderate resolutions (20 to 30 m pixels, respectively), and potential to provide high classification accuracies, the SPOT 3 HRV and Landsat 5 TM satellite systems were well-suited for classifying plant communities in the Reynolds Creek Watershed and similar areas of the Intermountain West. Practical procedures for plant community classification and map accuracy assessment are presented for use by natural resource managers. DOI:10.2458/azu_jrm_v54i2_clark

Intermountain plant community classification using Landsat TM and SPOT HRV data

Rangeland plant communities of the Intermountain West differ in their ecology and management requirements. Successful management of extensive areas at plant community-level resolution first requires an efficient, cost-effective means of plant community classification and mapping. We evaluated the influence of image acquisition date and satellite imaging system on the accuracy of plant community maps created from multispectral satellite imagery of Reynolds Creek Experimental Watershed (RCEW) (234 km2) in southwestern Idaho. Maps delineating 6 native and 2 non-native Intermountain plant communities were created from Landsat 5 TM and SPOT 3 HRV data using a maximum likelihood classification procedure. Map accuracy was assessed using ground reference points. Maps created from satellite data acquired during dry-down (early August) had higher overall accuracy (average = 70.5%) than from data acquired during peak growth (early June) (average = 54.4%). Overall accuracy of maps generated by Landsat (average = 60.1%) and SPOT (average = 65.5%) were statistically similar. Given their broad spatial coverages (3,600 to 31,450 km2 scene(-1), respectively), moderate resolutions (20 to 30 m pixels, respectively), and potential to provide high classification accuracies, the SPOT 3 HRV and Landsat 5 TM satellite systems were well-suited for classifying plant communities in the Reynolds Creek Watershed and similar areas of the Intermountain West. Practical procedures for plant community classification and map accuracy assessment are presented for use by natural resource managers.

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.

Evaluation of SPOT HRV Data in Mapping Mangrove Forests in a Temperate Zone

Mangrove forests in the western Waitemata Harbour, Auckland, New Zealand were mapped as lush and stunted, in conjunction with eight other categories of cover, from winter and summer SPOT multispectral (XS) data individually, and jointly with a panchromatic (PAN) band. After the mangrove-containing coastal zone was geographically stratified, the sub‐scene data was classified using the maximum likelihood method. Lush mangroves are mapped 77.5 % and 82.7 % correctly from the XS data alone. The accuracy levels for stunted mangroves are lower at 67.5 % and 60% correct. These accuracy levels rise to 82.5 % for the winter image and 78.8 % for the summer image if the misclassifications between the two classes of mangroves are disregarded. Incorporation of the PAN band in the classification of the winter image increased the accuracy by 2.5 % for lush mangroves and by 12.5 % for stunted mangroves. Thus, SPOT HRV data can generate mangrove maps accurately in a temperate zone. The PAN band is effective in improving the mapping accuracy for stunted mangroves, but less so for dense mangroves due to the similarity of its spectral content to that of the first two XS bands.

自己組織化マップ（ＳＯＭ）による液状化領域の抽出

A supervised classification method using a self-organizing map (SOM) is proposed to classify remote sensing data. SOM has a characteristic that a probability density function of input data is represented as a feature map. The proposed method is realized by creating a category map from the feature map of SOM. The category map can visualize characteristics of SPOT HRV data and it is also employed as a supervised classification method. The proposed method extracts liquefied area in Kobe (Japan) damaged by the 1995 Hyogoken Nanbu earthquake using the SPOT HRV data and the category map. As an experimental result, it is shown that classification accuracies of the proposed method are higher than those of the maximum likelihood and the back-propagation methods.

A new approach for tropical forest area monitoring using multiple spatial resolution satellite sensor imagery

Tropical forest deforestation is a major concern at global, regional, and local scales. Recent advances have demonstrated the feasibility of using coarse spatial resolution remote sensing imagery to assess tropical forest over large areas. To date, few methods have been developed for the estimation of change over large areas. A change detection method based on a combination of NOAAAVHRR, Landsat TM, and SPOT-HRV data is evaluated on a study site in Vietnam, Southeast Asia. Changes detected with fine spatial resolution imagery are related to AVHRR-derived forest class proportions and fragmentation patterns.

活断層センシング

In order to observe the status of earth's nature and human activity, we are able to utilize the sensor data onboard the orbiting satellite platform with high resolution. It is well known that remotely sensed data have quite unique character of quick, repeat, cheap and reliance, which reflect the features of earth's surface. This technique is often utilized for some of exploration stages such as petroleum and metal mining resources.JERS-1 SAR data were taken over disastered area before and after the Hyogoken Nanbu Earthquake, and generated interferrogram showing crustal deformation pattern. Also, we have derived lineament map from SPOT HRV data to compare with the pattern of interferrogram.This paper describing the effectiveness of new concept the usage of lineament pattern. Because, extracted plenty of lineaments from satellite image suppose to be more efficient to detect surface movement occurred by crustal deformation.

Effect of radiometric corrections on NDVI-determined from SPOT-HRV and Landsat-TM data

The normalized difference vegetation index (NDVI), which is generally considered as an index minimizing the radiometric errors on image data has to be corrected radiometrically when a quantitative analysis is performed. In this article, the main factors affecting NDVI are analyzed: proper characteristics (MTF) and absolute calibration of the satellite sensor, Sun zenith angle, Earth-Sun distance, and atmospheric condition. The effects of these factors are theoretically and practically analyzed on two SPOT-HRV and Landsat-TM images acquired the same day over the same area in southeast France. Some simplified correction methods are proposed. The results show that: i) The conversion of digital counts into apparent reflectance is the most important step for NDVI correction. Without this correction, a relatively constant error affects NDVI depending on the sensor considered (− 0.18 for SPOT-HRV and − 0.10 for Landsat TM). ii) The MTF correction does not affect the average NDVI value; its interest is to restore the radiometric level of individual pixels that have a large contrast with their surroundings. iii) The atmospheric effects are similar in the homologous spectral bands of SPOT-HRV and Landsat TM. Their correction increases the dynamic range of NDVI variation (around 24% in the example presented) and consequently the contrast between different targets. The effect of the noncoincidence of SPOT-HRV and Landsat-TM spectral bands is also studied. This effect can be considered either as a source of error or as a supplementary source of information. An example shows that the combination of the spectral information given by the two satellites can be used to improve the discrimination of some targets such as bare soil and soil with a low vegetation density.

Validation of the use of multiple linear regression as a tool for unmixing coarse spatial resolution images

When estimating agricultural production, we are faced with a problem of scale, be they scales of observation, modeling, or representation. In the context of the farming landscape of Europe, the relationships between scales and modeling take on special importance as the aim is to extract information on a smaller scale than the scale of observation, the latter necessarily being large relative to the average size of fields in order to obtain the required time resolution. This article presents a method for unmixing coarse resolution signals (of the NOAA-AVHRR type) through the use of multiple linear regression. This allows the signal for each mixed coarse resolution pixel to be broken down thanks to a knowledge of land use and a linear mixture model. It is then possible to calculate the individual radiometric contribution of each constituent of a mixed pixel. The work presented here is an assessment of the performance of this method using SPOT-HRV data, degraded to simulate the coarse resolution and as normally provided to produce the land use information. Simulation with SPOT-HRV data also enables various parameters to be calculated, from which it is possible to verify the interest and reliability of multiple linear regression as a method for spectral unmixing.