Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

Characterizing vegetation cover in global savannas with an annual foliage clumping index derived from the MODIS BRDF product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.     

A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies

Leaf area index (LAI) is an important variable needed by various land surface process models. It has been produced operationally from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a look-up table (LUT) method, but the inversion accuracy still needs significant improvements. We propose an alternative method in this study that integrates both the radiative transfer (RT) simulation and nonparametric regression methods. Two nonparametric regression methods (i.e., the neural network [NN] and the projection pursuit regression [PPR]) were examined. An integrated database was constructed from radiative transfer simulations tuned for two broad biome categories (broadleaf and needleleaf vegetations). A new soil reflectance index (SRI) and analytically simulated leaf optical properties were used in the parameterization process. This algorithm was tested in two sites, one at Maryland, USA, a middle latitude temperate agricultural area, and the other at Canada, a boreal forest site, and LAI was accurately estimated. The derived LAI maps were also compared with those from MODIS science team and ETM+ data. The MODIS standard LAI products were found consistent with our results for broadleaf crops, needleleaf forest, and other cover types, but overestimated broadleaf forest by 2.0-3.0 due to the complex biome types.     

Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products

This paper evaluates the performances of a neural network approach to estimate LAI from CYCLOPES and MODIS nadir normalized reflectance and LAI products. A data base was generated from these products over the BELMANIP sites during the 2001-2003 period. Data were aggregated at 3 km × 3 km, resampled at 1/16 days temporal frequency and filtered to reject outliers. VEGETATION and MODIS reflectances show very consistent values in the red, near infrared and short wave infrared bands. Neural networks were trained over part of this data base for each of the 6 MODIS biome classes to retrieve both MODIS and CYCLOPES LAI products.Results show very good performances of neural networks to estimate the original LAI products with an overall root mean square error (RMSE) around 0.5 for MODIS LAI from both MODIS and CYCLOPES normalized reflectances and a RMSE ranging between 0.12 (CYCLOPES reflectances) and 0.29 (MODIS reflectances) for CYCLOPES LAI. A drop of 15% of performance was found by training MODIS biome dependant algorithm by a single network over all the classes at the same time. More detailed analyses show that CYCLOPES and MODIS LAI values are very consistent for grasses and crops. Conversely, other biomes including shrubs, savanna, needleleaf and broadleaf forests show significant discrepancies, mainly due to differences between LAI definitions used between CYCLOPES (closer to effective LAI) and MODIS (closer to true LAI). However, products derived from the original CYCLOPES LAI products show a better agreement with both effective and true LAI ground measurements values. MODIS LAI products show more instability, partly because of the slightly shorter temporal resolution as compared to CYCLOPES.These results confirm the interest and versatility of neural networks for operational algorithms. This approach could be extended to other products or sensors, and may constitute a step forward for the fusion of data from several sensors, hence contributing to develop ‘virtual constellations’.     

Global land cover classification at 1 km spatial resolution using a classification tree approach

This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.     

Land cover mapping in support of LAI and FPAR retrievals from EOS-MODIS and MISR: Classification methods and sensitivities to errors

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from Advanced Very High Resolution Radiometer (AVHRR) data with 1 km spatial resolution using a six-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat Thematic Mapper (TM) and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FPAR maps. Further, the main source of disagreement in LAI and FPAR maps can be attributed to specific biome classes that are characterized by a continuum of fractional cover and canopy structure.     

Land cover assessment with MODIS imagery in southern African Miombo ecosystems

Global land use and land cover products in highly dynamic tropical ecosystems lack the detail needed for natural resource management and monitoring at the national and provincial level. The MODIS sensor provides improved opportunities to combine multispectral and multitemporal data for land use and land cover mapping. In this paper we compare the MODIS Global Land Cover Classification Product with recent land use and land cover maps at the national level over a characteristic location of Miombo woodlands in the province of Zambezia, Mozambique. The performances of three land cover-mapping approaches were assessed: single-date supervised classification, principal component analysis of band-pair difference images, and multitemporal NDVI analysis. Extensive recent field data were used for the definition of the test sites and accuracy assessment. Encouraging results were achieved with the three approaches. The classification results were refined with the help of a digital elevation model. The most consistent results were achieved using principal component analysis of band-pair difference images. This method provided the most accurate classifications for agriculture, wetlands, grasslands, thicket and open forest. The overall classification accuracy reached 90%. The multitemporal NDVI provided a more accurate classification for the dense forest cover class. The selection of the right image dates proved to be critical for all the cases evaluated. The flexibility of these alternatives makes them promising options for rapid and inexpensive land cover mapping in regions of high environmental variability such as tropical developing countries.     

Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America

A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time-steps of 10 days, using data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms.     

缅甸土地覆被遥感制图和空间格局分析

在“一带一路”倡议框架下,中缅经济走廊逐步从概念转入实质规划建设阶段,了解和掌握缅甸土地覆被的空间格局和分布特征对于合理开发利用资源、制定务实的经济廊道建设规划具有重要的战略意义。利用Landsat-8 OLI遥感影像数据,基于多分类器集成的面向对象迭代分类方法（OIC-MCE）,生产了缅甸2015年30 m分辨率土地覆被产品（MyanmarLC-2015）。采用Google Earth高分辨率影像获取验证样本用于产品精度验证,验证结果表明：MyanmarLC-2015产品的总体分类精度为89.05%,Kappa系数为0.87,各类别的用户精度和制图精度均超过72%,能够准确地反映缅甸土地覆被类型的空间格局。根据产品统计,林地是缅甸面积最大的土地覆被类型,占国土面积56.15%,以常绿阔叶林为主,占林地面积83.57%。耕地面积次之,占国土面积27.01%。地形因子对缅甸土地覆被类型空间分布格局有显著的影响,随着海拔升高,呈现出按如下顺序的垂直地带性特征：森林湿地、水田、旱地、落叶灌木林、落叶阔叶林、常绿灌木林、常绿阔叶林、常绿针叶林。从植被生产力的角度来看,缅甸东部、东北部和东南部植...     

Assessment of MODIS LAI retrievals over soybean crop in Southern Brazil

In the present paper we have looked into the excessive occurrence of 255 standard fill value retrievals in Collection 4 MODIS LAI product over soybean areas from crop year 2001/2002 to 2004/2005, in Southern Brazil. The 255 standard fill value indicates that no leaf area index (LAI) retrieval was possible for the considered pixel. Time series of eight‐day composite LAI images (MOD15A2) and 16‐day composite NDVI images (MOD13Q1) were both compared with a soybean reference map derived from multitemporal Landsat images. The Land Cover Type 3 product (MOD12Q1) was also analysed to verify if the occurrence of those retrievals was related to misclassification of the broadleaf crops biome. Results indicated that the 255 standard fill value retrievals in Collection 4 LAI product were mainly related to soybean areas during peak growing season and occurred in every crop year we have studied. Eventual misclassification in the biome map was not the cause of those retrievals in the Collection 4 MODIS LAI product.     

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere-Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.     

A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin

In this paper we demonstrate a new approach that uses regional/continental MODIS (MODerate Resolution Imaging Spectroradiometer) derived forest cover products to calibrate Landsat data for exhaustive high spatial resolution mapping of forest cover and clearing in the Congo River Basin. The approach employs multi-temporal Landsat acquisitions to account for cloud cover, a primary limiting factor in humid tropical forest mapping. A Basin-wide MODIS 250 m Vegetation Continuous Field (VCF) percent tree cover product is used as a regionally consistent reference data set to train Landsat imagery. The approach is automated and greatly shortens mapping time. Results for approximately one third of the Congo Basin are shown. Derived high spatial resolution forest change estimates indicate that less than 1% of the forests were cleared from 1990 to 2000. However, forest clearing is spatially pervasive and fragmented in the landscapes studied to date, with implications for sustaining the region's biodiversity. The forest cover and change data are being used by the Central African Regional Program for the Environment (CARPE) program to study deforestation and biodiversity loss in the Congo Basin forest zone. Data from this study are available at http://carpe.umd.edu.     

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

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

Comparison of four global FAPAR datasets over Northern Eurasia for the year 2000

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has been identified as one of several key satellite-derived biophysical datasets. With multiple global FAPAR datasets now available and a lack of in-situ measurements and comparison studies in the far north, this study attempts to provide the reader with an indication of the performance of four global FAPAR datasets (MODIS, CYCLOPES, JRC and GLOBCARBON) over Northern Eurasia in the year 2000 via comparison. Within the year 2000 growing season, both the MODIS and CYCLOPES datasets recorded on average similar but substantially higher values than the JRC and GLOBCARBON datasets. Among three of the four datasets, a high level of agreement in deciduous broadleaf forests and croplands was observed. Largest disagreement occurred among needleleaf forests and grassland/shrubland. Potential reasons for discrepancies among the datasets include different retrieval methods, use of LAI and land cover, snow effects and others. Findings from this study and other published results suggest that overall, JRC best captures FAPAR over northern Eurasia in the year 2000. However, when considering individual landcover types, any one or more of the four products may be suitable. There exists a real need for more in-situ measurements in this region — the lack of such measurements makes evaluation extremely difficult. It appears that areas north of 60° urgently require further investigation.     

基于时序MODIS反射率产品统计检验方法的土地覆盖更新

土地覆盖产品的生产是遥感领域的研究热点。全球范围土地覆盖产品因其在空间范围上的巨大尺度,生产周期较长,因此产品的时间跨度也很大,需要具有与之相对应的快速生成与更新技术,以提高产品的时间分辨率。使用MODIS历史土地覆盖产品和反射率产品,采用平均值显著性统计检验的思想,实现土地覆盖产品的快速更新。在MODIS条带号为h26v05的结果中选取宁夏自治区作为检验样区,通过目视检验和精度评价的方法对土地覆盖更新结果进行检验,总体精度为90.0290%。对水、混交林、草原、城市和建成区、裸地或低植被覆盖地变化的趋势和变化原因进行分析论证,表明采用平均值显著性统计检验的思想使用时序反射率产品对土地覆盖产品进行自动化快速更新是可行的。     

中国西南山区GEOV１、GLASS和MODISLAI产品的对比分析

叶面积指数(LeafAreaIndex,LAI)是表征植被生物物理变化和冠层结构特征的关键参数,目前存在多个全球范围、长时间序列LAI产品,对其进行验证是LAI产品应用的重要前提,然而目前山区的验证工作尤其少见.在我国西南山区选取６个典型样区,考虑山区复杂地形特征,从产品时空完整性以及对山区植被时空特征表征能力等方面对GEOV１、GLASS和MODISLAI产品进行对比分析.研究结果表明:①相比于地形平坦地区,在山区随海拔和地形起伏度的增加,LAI产品时空完整性呈递减的趋势,其中,GEOV１LAI表现最差,MODISLAI次之,GLASSLAI表现最好;②GLASSLAI和GEOV１LAI的空间分布合理且具有较好的一致性,MODISLAI的空间分布和二者存在差异,３种LAI产品均难以准确反映山区植被垂直带谱的变化特征;③草地类型LAI产品间差值较小,林地和农作物GLASSLAI和GEOV１LAI产品一致性较好,MODISLAI产品和二者存在较大的差异;④GLASSLAI时间序列曲线平滑且连续,GEOV１LAI存在时间不连续现象,MODISLAI季相变化中的波动现象比较严重;各产品不仅难以准确反映冬季的常绿针叶林LAI,而且难以准确表征样区内农田作物轮作的物候信息.对比分析有助于发现LAI产品在山区存在的问题,并为今后LAI产品的算法改进提供帮助和参考.     

Spatial agreement between two land‐cover data sets stratified by agricultural eco‐regions

Two of the most widely used land‐cover data sets for the United States are the National Land‐Cover Data (NLCD) at 30‐m resolution and the Global Land‐Cover Characteristics (GLCC) at 1‐km nominal resolution. Both data sets were produced around 1992 and expected to provide similar land‐cover information. This study investigated the spatial distribution of NLCD within major GLCC classes at 1‐km unit over a total of 11 agricultural‐related eco‐regions across the continental United States. Our results exhibited that data agreement or relationship between the GLCC and NLCD was higher for the eco‐regions located in the corn belt plains with homogeneous or less complicated land‐cover distributions. The GLCC cropland primarily corresponded to NLCD row crops, pasture/hay and small grains, and was occasionally related to NLCD forest, grassland and shrubland in the remaining eco‐regions due to high land‐cover diversity. The unique GLCC classes of woody savanna and savanna were mainly related to the NLCD orchard and grassland, respectively, in the eco‐region located in the Central Valley of California. The GLCC urban/built‐up among vegetated areas strongly agreed to the NLCD urban for the eco‐regions in the corn belt plains. A set of sub‐class land‐cover information provided through this study is valuable to understand the degrees of spatial similarity for the major global vegetated classes. The sub‐class information from this study provides reference for substituting less‐detailed global data sets for detailed NLCD to support national environment studies.