Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data

Estimates of forest area are among the most common and useful information provided by national forest inventories. The estimates are used for local and national purposes and for reporting to international agreements such as the Montréal Process, the Ministerial Conference on the Protection of Forests in Europe, and the Kyoto Protocol. The estimates are usually based on sample plot data and are calculated using probability-based estimators. These estimators are familiar, generally unbiased, and entail only limited computational complexity, but they do not produce the maps that users are increasingly requesting, and they generally do not produce sufficiently precise estimates for small areas. Model-based estimators overcome these disadvantages, but they may be biased and estimation of variances may be computationally intensive. The study objective was to compare probability- and model-based estimators of mean proportion forest using maps based on a logistic regression model, forest inventory data, and Landsat imagery. For model-based estimators, methods for evaluating bias and reducing the computational intensity were also investigated. Four conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the data; all the estimators produced comparable estimates for mean proportion forest, except for small areas; probability-based inferences enhanced using maps produced increased precision; and the computational intensity associated with estimating variances for model-based estimators can be greatly reduced with no detrimental effects.     

Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Airborne scanning LiDAR is a spatial technology increasingly used for forestry and environmental applications. However, the accuracy and coverage of LiDAR observations is highly dependent on both the extrinsic specifications of the LiDAR survey as well as the intrinsic effects such as the underlying forest structure. Extrinsic parameters which are set as part of the LiDAR survey include platform altitude, scan angle (half max. angle off nadir), and beam cross sectional diameter at the reflecting surface (referred to as footprint size). In this paper we investigate the effect of a number of these extrinsic parameters, including three different platform altitudes (1000, 2000, and 3000 m), two scan angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes (0.2, 0.4, and 0.6 m). The comparison was undertaken in eucalypt forests at three sites, varying in vegetation structure and topography within the Wedding Bells State Forest, Coffs Harbour, Australia. Results at the plot scale (40 × 90 m areas) indicate that tree heights computed from the 1000 m LiDAR data set (10° half max. angle off nadir) are well correlated with maximum plot heights (difference < 3 m) and field measured canopy volume (r² > 0.75, p < 0.001). Using normalised canopy height profiles (CHP) derived for sites, from data recorded at each altitude, we observed no significant difference between the relative distribution of LiDAR returns, indicating that platform altitude and footprint size have not had a major influence on CHP estimation. Interestingly, comparisons of first and last returns for individual pulses at increasing altitudes identified progressively fewer discrete first/last pulse combinations with more than 70% of pulses recorded as a single return at the highest altitude (3000 m). A possible hypothesis is that greater platform altitude and footprint size reduces the intensity of laser beam incident on a given surface area thus decreasing the probability of recording a last return above the noise threshold. Furthermore, tree scale analysis found a positive relationship between platform altitude and the underestimation of crown area and crown volume. The implications of this work for forest management are: (i) platform altitudes as high as 3000 m can be used to quantify the vertical distribution of phyto-elements, (ii) higher platform altitudes record a lower proportion of first/last return combinations that will further reduce the number of points available for forest structural assessment and development of digital elevation models, and (iii) for discrete LiDAR data, increasing platform altitude will record a lower frequency of returns per crown, resulting in larger underestimates of individual tree crown area and volume if standard algorithms are applied.     

Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks

The relation between satellite measurements of the normalized difference vegetation index (NDVI), cumulated over the growing season, and inventory estimates of forest woody biomass carbon is estimated statistically with data from 167 provinces and states in six countries (Canada, Finland, Norway, Russia and the USA for a single time period and Sweden for two periods). Statistical tests indicate that the regression model can be used to represent the relation between forest biomass and NDVI across spatial, temporal and ecological scales for relatively long time scales. For the 1.42 billion ha of boreal and temperate forests in the Northern Hemisphere, the woody biomass carbon pools and sinks are estimated at a relatively high spatial resolution (8×8 km). We estimate the carbon pool to be 61±20 gigatons (10⁹) carbon (Gt C) during the late 1990s and the biomass sink to be 0.68±0.34 Gt C/year between the 1982 and 1999. The geographic detail of carbon sinks provided here can contribute to a potential monitoring program for greenhouse gas emission reduction commitments under the Kyoto Protocol.     

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We spatially aggregated 30 m datasets to coarser spatial resolutions (90, 150, 210, 270, 510 and 990 m) and produced estimates of forest proportion for each spatial resolution using both model‐ and design‐based approaches. Average‐based aggregation had no effect on per‐image estimates of forest proportion; image variability decreased with increasing spatial resolution and local variability peaked between 210 and 270 m. Majority‐based aggregation resulted in overestimation of forest land in a heavily forested landscape and underestimation of forest land in a sparsely forested landscape, with both trends following a natural log distribution. Of the spatial resolutions tested, 30 m was superior for obtaining estimates using model‐based approaches. However, standard errors of design‐based inventory estimates of forest proportion were smallest when accompanying stratification maps which were aggregated to between 90 and 150 m spatial resolutions and strata thresholds were optimized by study area. These results suggest that spatially aggregating existing 30 m land cover datasets can provide NFIs with gains in precision of their estimates of forest land area, while reducing image storage size and processing times; land cover datasets derived from coarser spatial resolution sensors may provide similar benefits.     

Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems

This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set.

The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m³ ha^− 1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m³ ha^− 1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%.     

Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle.The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression.Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n = 31 circular nested sample plots of 154 m² for trees and shrubs and 1 m² for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs.Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions.The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE = 1.50 t/ha, SE% = 9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 t/ha with a root mean square error RMSE = 6.42 t/ha, RMSE_r = 44%) resulting from leave-one-out cross-validation. The k-NN-approach allows mapping and analysis of the spatial variability of AGC. The results show high spatial variability with AGC predictions ranging from 4.3 t/ha to 28.8 t/ha, reflecting the highly heterogeneous conditions in those permafrost-influenced landscapes. The means and totals of linear regression and k-NN predictions revealed only small differences but some regional distinctions were recognized in the maps.     

Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass

Regression has been widely applied in Light Detection And Ranging (LiDAR) remote sensing to spatially extend predictions of total aboveground biomass (TAGB) and other biophysical properties over large forested areas. Sample (field) plot size has long been considered a key sampling design parameter and focal point for optimization in forest surveys, because of its impact on sampling effort and the estimation accuracy of forest inventory attributes. In this study, we demonstrate how plot size and co-registration error interact to influence the estimation of LiDAR canopy height and density metrics, regression model coefficients, and the prediction accuracy of least-squares estimators of TAGB. We made use of simulated forest canopies and synthetic LiDAR point clouds, so that we could maintain strict control over the spatial scale and complexity of forest scenes, as well as the magnitude and type of planimetric error inherent in ground-reference and LiDAR datasets. Our results showed that predictions of TAGB improved markedly as plot size increased from 314 (10 m radius) to 1964 m² (25 m radius). The co-registration error (spatial overlap) between ground-reference and LiDAR samples negatively impacted the estimation of LiDAR metrics, regression model fit, and the prediction accuracy of TAGB. We found that larger plots maintained a higher degree of spatial overlap between ground-reference and LiDAR datasets for any given GPS error, and were therefore more resilient to the ill effects of co-registration error compared to small plots. The impact of co-registration error was more pronounced in tall, spatially heterogeneous stands than short, homogeneous stands. We identify and briefly discuss three possible ways that LiDAR data could be used to optimize plot size, sample selection, and the deployment of GPS resources in forest biomass surveys.     

The spatial distribution of indigenous forest and its composition in the Wellington region, New Zealand, from ETM+ satellite imagery

In order to improve biodiversity management in the Wellington region of New Zealand, it is necessary to make an inventory of the indigenous forest—where is it, and what type is it? The single greatest impediment to making a spatially (i.e., 1:50,000 scale) and thematically detailed inventory from satellite imagery has been the topography of the three mountainous ranges in the Wellington region. The effective irradiance of incoming light varies with slope orientation, as does the proportion of light that is reflected towards the satellite (the bidirectional reflectance). In this paper, we show how satellite imagery may be processed to standardised spectral reflectance, which is a property of the vegetation alone, independent of sun position, slope, and view direction. Because of this, the use of automatic methods to map vegetation and provide spatially and thematically detailed maps is greatly simplified. Using this method, we produce a land-cover map of the Wellington region, with eight classes, to a classification accuracy of approximately 95%. We also show how the proportions of conifer, broad-leaved, and beech trees may be determined for indigenous forest to provide a framework for forest-type inventory.     

The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes

The k-nearest-neighbour (kNN) algorithm is widely applied for the estimation of forest attributes using remote sensing data. It requires a large amount of reference data to achieve satisfactory results. Usually, the number of available reference plots for the kNN-prediction is limited by the size of the area covered by a terrestrial reference inventory and remotely sensed imagery collected from one overflight. The applicability of kNN could be enhanced if adjacent images of different acquisition dates could be used in the same estimation procedure. Relative radiometric calibration is a prerequisite for this. This study focuses on two empirical calibration methods. They are tested on adjacent LANDSAT TM scenes in Austria. The first, quite conventional one is based on radiometric control points in the overlap area of two images and on the determination of transformation parameters by linear regression. The other, recently developed method exploits the kNN-cross-validation procedure. Performance and applicability of both methods as well as the impact of phenology are discussed.     

Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers

Researchers in lidar (Light Detection And Ranging) strive to search for the most appropriate laser-based metrics as predictors in regression models for estimating forest structural variables. Many previously developed models are scale-dependent that need to be fitted and then applied both at the same scale or pixel size. The objective of this paper is to develop methods for scale-invariant estimation of forest biomass using lidar data. We proposed two scale-invariant models for biomass: a linear functional model and an equivalent nonlinear model that use lidar-derived canopy height distributions (CHD) and canopy height quantile functions (CHQ) as predictors, respectively. The two models are called functional regression models because the predictors CHD and CHQ are themselves functions or functional data. The model formulation was justified mathematically under moderate assumptions. We also created a fine-resolution biomass map by mapping individual tree component biomass in a temperate forest of eastern Texas with a lidar tree-delineation approach. The map was used as reference data to synthesize training and test datasets at multiple scales for validating the two scale-invariant models. Results suggest that the models can accurately predict biomass and yield consistent predictive performances across a variety of scales with an R² ranging from 0.80 to 0.95 (RMSE: from 14. 3 Mg/ha to 33.7 Mg/ha) among all the fitted models. Results also show that a training data size of around 50 plots or less was enough to guarantee a good fitting of the linear functional model. Our findings demonstrate the effectiveness of CHD and CHQ as lidar metrics for estimating biomass as well as the capability of lidar for mapping biomass at a range of scales. The functional regression models of this study are useful for lidar-based forest inventory tasks where the analysis units vary in size and shape. They also hold promise for estimating other forest characteristics such as below-ground biomass, timber volume, crown fuel weight, and Leaf Area Index.     

Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories

According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3 ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m³ ha^− 1) and aboveground biomass (t ha^− 1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15 m × 15 m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250 m × 250 m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.     

Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data

Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m² located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height (h_L) and timber volume (V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for h_L and 10.7% for V were found when comparing data from different acquisitions.     

Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification

Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 × 3 pixels and 11 × 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy = 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellite image data, providing an additional tool to ongoing aerial photograph interpretation.     

GPS全球卫星定位技术的发展现状、动态及应用

本文分析了美国GPS现代化的背景，内容其影响；也分析了美国为其政治，经济利益，宣布停止执行GPS的SA政策，其次于GPS市场及中国的发展趋势进行了综述，最后对基于INTERNET的GPS－－网络GPS的应用前景进行了展望。     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

纳税人诚信识别系统设计的方法与模型研究

在纳税人税收不遵从行为的微观经济解释基础上,应用Logistic回归模型来识别纳税人诚信,给出纳税人诚信的测算方法。并应用实际数据作实证研究,对模型进行参数估计和预测分析,证实了Logistic回归模型是完善纳税信誉等级管理的强有力技术手段,为设计纳税人诚信识别系统提供了完善的技术解决方案。     

A-GPS定位技术在3G终端中的应用

A—GPS定位技术是目前移动运营商推出的移动位置服务中所采用的精度最高的定位方式，它结合了GPS定位技术及移动通信网络的优势于一身。文中主要介绍了A—GPS定位技术在3G中的应用。重点介绍了A—GPS定位技术的两个基本模型，及SUPL的原理和结构。     

The colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates

In this paper, uncertainties in the retrieval of satellite surface chlorophyll concentrations in the Mediterranean Sea have been evaluated using both regional and global ocean colour algorithms. The rationale for this effort was to define the most suitable ocean colour algorithm for the reprocessing of the entire SeaWiFS archive over the Mediterranean region where standard algorithms were demonstrated to be inappropriate. Using a large dataset of coincident in situ chlorophyll and optical measurements, covering most of the trophic regimes of the basin, we validated two existing regional algorithms [Bricaud, A., E. Bosc, and D. Antoine, 2002. Algal biomass and sea surface temperature in the Mediterranean Basin — Intercomparison of data from various satellite sensors, and implications for primary production estimates. Remote Sensing of Environment, 81(2-3), 163-178.; D'Ortenzio, F., S. Marullo, M. Ragni, M. R. d'Alcala and R. Santoleri, 2002. Validation of empirical SeaWiFS algorithms for chlorophyll-alpha retrieval in the Mediterranean Sea — A case study for oligotrophic seas. Remote Sensing of Environment, 82(1), 79-94.] and the global algorithm OC4v4 used for standard NASA SeaWiFS products. The results of our analysis confirmed that the OC4v4 performs worse than the two existing regional algorithms. Nonetheless, these two regional algorithms do show uncertainties dependent on chlorophyll values. Then, we introduced a better tuned algorithm, the MedOC4. Using an independent set of in situ chlorophyll data, we quantified the uncertainties in SeaWiFS chlorophyll estimates using the existing and new regional algorithms. The results confirmed that MedOC4 is the best algorithm matching the requirement of unbiased satellite chlorophyll estimates and improving the percentage of the satellite uncertainty, and that the NASA standard chlorophyll products are affected by an uncertainty of the order of 100%. Moreover, the analysis suggests that the poor quality of the SeaWiFS chlorophyll in the Mediterranean is not due to the atmospheric correction term but to peculiarities in the optical properties of the water column. Finally the observed discrepancy between the global and the regional bio-optical algorithms has been discussed analysing the differences between the two in situ datasets used for tuning the algorithms (SeaBASS versus ours). The main results are that methodological differences in the two datasets cannot play a major role and the inherent bio-optical properties of the basin can explain the observed discrepancy. In particular the oligotrophic water of the Mediterranean Sea is less blue (30%) and greener (15%) than the global ocean.     

基于GPS的区域识别在车辆智能考试系统中的应用

近年来随着驾考人数的增多,车辆考试方式也从传统的人工方式转变为计算机自动控制方式,从而提高了考试效率.全球定位系统从军用转为民用后,在国民经济建设中发挥了重要作用,利用GPS进行车辆定位也恰恰是考试系统的关键.文章在矢量计算基本性质和算法的基础上提出了区域识别技术,介绍了该技术在基于GPS的车辆智能考试系统中的应用.实验结果表明,上述识别方法能够满足车辆智能考试系统的需求,效果较为理想.     

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R² (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.