Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non‐supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km² of land surface were burnt in Acre State. Of this, 3700 km² corresponded to the previously deforested areas and 2800 km² corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.     

Mapping cotton fields using data mining and MODIS time-series

ABSTRACT

Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R²) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.     

Analysis of the applicability of multi-temporal full polarimetric airborne L-band SAR scattering to paddy rice field mapping

ABSTRACT

A Synthetic Aperture Radar (SAR) is an all-weather imaging system that is often used for mapping paddy rice fields and estimating the area. Fully polarimetric SAR is used to detect the microwave scattering property. In this study, a simple threshold analysis of fully polarimetric L-band SAR data was conducted to distinguish paddy rice fields from soybean and other fields. We analysed a set of ten airborne SAR L-band 2 (Pi-SAR-L2) images obtained during the paddy rice growing season (in June, August, and September) from 2012 to 2014 using polarimetric decomposition. Vector data for agricultural land use areas were overlaid on the analysed images and the mean value for each agricultural parcel computed. By quantitatively comparing our data with a reference dataset generated from optical sensor images, effective polarimetric parameters and the ideal observation season were revealed. Double bounce scattering and surface scattering component ratios, derived using a four-component decomposition algorithm, were key to extracting paddy rice fields when the plant stems are vertical with respect to the ground. The alpha angle was also an effective factor for extracting rice fields from an agricultural area. The data obtained during August show maximum agreement with the reference dataset of estimated paddy rice field areas.     

Winter wheat mapping using temporal signatures of MODIS vegetation index data

Because most land-cover types have distinct seasonal changes and corresponding reflectance characteristics in remotely sensed images, the signatures in time-series data are useful for discriminating different land covers. Although temporal signatures have been used to classify different land-cover types, they have not been fully exploited to classify specific crops, and the influence of low resolution should be evaluated. The aims of this study were to seek an effective method to classify specific crops using the temporal signatures in coarse time-series data and to examine the applicability of the data for crop classification as well. A winter wheat-producing region in China was selected for this case study. Moderate-Resolution Imaging Spectroradiometer (MODIS) 8-day composite land surface reflectance product (MOD09Q1) data with a 250 m spatial resolution were used to calculate the vegetation index data, which was applied to detect the properties of live green plants. The noise in the time series was filtered to minimize the classification uncertainties. The curve shape in the time-series vegetation index profile was used as the major metric to classify winter wheat, and other phenological metrics extracted from the data were used conjunctly as auxiliary functions to improve the separability. The metrics for winter wheat classification were quantified in the large fields with relatively pure pixels. Winter wheat was successfully extracted from the MODIS vegetation index data, and the MODIS-derived result was validated with a fine-resolution (19.5 m) thematic map derived from images collected by the charge-coupled device sensor on board the China–Brazil Earth Resources Satellite (CBERS). It showed that the MODIS-derived result had inevitable low-resolution bias, and the errors of commission and omission were 32.3 and 33.8%, respectively. The overall classification effect of the MODIS-derived result relied upon the distribution of pixel purity in the study area.     

Cropland distributions from temporal unmixing of MODIS data

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R² ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km². The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.     

Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km² the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.     

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.     

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.     

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.     

MODIS土地覆盖数据产品精度分析——以黄河源区为例

MODIS土地覆盖数据产品覆盖广、时间分辨率高,是区域土地覆盖变化监测的重要数据源。本文以中国土地资源分类系统为依据,重新归类黄河源区MODIS土地覆盖数据。利用2000年和2006年黄河源区Land-sat解译数据为参考数据,对相应的MODIS土地覆盖数据,从数量精度和形状一致性两个方面进行精度分析和适用性评价。结果表明:在形状上,加入权重的总体形状一致性皆在69%以上,其中主要地类草地的一致性达到88%以上;在数量上,加入权重的总体面积相对误差在26%以内,误差主要产生在未利用土地等地类。MODIS土地覆盖数据产品在大尺度的土地覆盖监测中仍然有重要的应用价值。     

Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea

Estimating the evapotranspiration (ET) is a requirement for water resource management and agricultural productions to understand the interaction between the land surface and the atmosphere. Most remote-sensing-based ET is estimated from polar orbiting satellites having low frequencies of observation. However, observing the continuous spatio-temporal variation of ET from a geostationary satellite to determine water management usage is essential. In this study, we utilized the revised remote-sensing-based Penman–Monteith (revised RS-PM) model to estimate ET in three different timescales (instantaneous, daily, and monthly). The data from a polar orbiting satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS), and a geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), were collected from April to December 2011 to force the revised RS-PM model. The estimated ET from COMS and MODIS was compared with measured ET obtained from two different flux tower sites having different land surface characteristics in Korea, i.e. Sulma (SMC) with mixed forest and Cheongmi (CFC) with rice paddy as dominant vegetation. Compared with flux tower measurements, the estimated ET on instantaneous and daily timescales from both satellites was highly overestimated at SMC when compared with the flux tower ET (Bias of 41.19–145.10 W m^?2 and RMSE of 69.61–188.78 W m^?2), while estimated ET results were slightly better at the CFC site (Bias of –27.28–13.24 W m^?2 and RMSE of 45.19–71.82 W m^?2, respectively). These errors in results were primarily caused due to the overestimated leaf area index that was obtained from satellite products. Nevertheless, the satellite-based ET indicated reasonable agreement with flux tower ET. Monthly average ET from both satellites showed nearly similar patterns during the entire study periods, except for the summer season. The difference between COMS and MODIS estimations during the summer season was mainly propagated due to the difference in the number of acquired satellite images. This study showed that the higher frequency of COMS than MODIS observations makes it more ideal to continuously monitor ET as a geostationary satellite with high spatio-temporal coverage of a geostationary satellite.     

A maximum entropy method to extract urban land by combining MODIS reflectance,MODIS NDVI,and DMSP-OLS data

Researchers often encounter difficulties in obtaining timely and detailed information on urban growth. Modern remote-sensing techniques can address such difficulties. With desirable spectral resolution and temporal resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) products have significant advantages in tackling land-use and land-cover change issues at regional and global scales. However, simply based on spectral information, traditional methods of remote-sensing image classification are barely satisfactory. For example, it is quite difficult to distinguish urban and bare lands. Moreover, training samples of all land-cover types are needed, which means that traditional classification methods are inefficient in one-class classification. Even support vector machine, a current state-of-the-art method, still has several drawbacks. To address the aforementioned problems, this study proposes extracting urban land by combining MODIS surface reflectance, MODIS normalized difference vegetation index (NDVI), and Defense Meteorological Satellite Program Operational Linescan System data based on the maximum entropy model (MAXENT). This model has been proved successful in solving one-class problems in many other fields. But the application of MAXENT in remote sensing remains rare. A combination of NDVI and Defense Meteorological Satellite Program Operational Linescan System data can provide more information to facilitate the one-class classification of MODIS images. A multi-temporal case study of China in 2000, 2005, and 2010 shows that this novel method performs effectively. Several validations demonstrate that the urban land extraction results are comparable to classified Landsat TM (Thematic Mapper) images. These results are also more reliable than those of MODIS land-cover type product (MCD12Q1). Thus, this study presents an innovative and practical method to extract urban land at large scale using multiple source data, which can be further applied to other periods and regions.     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Extraction of training samples from time-series MODIS imagery and its utility for land cover classification

A number of classification techniques to generate land cover maps from satellite imagery have been proposed but supervised classification with manual selection and delineation of training samples (TSs) continues to be the preferred technique. The current practices of field visits and manual delineation of TSs by visual recognition are highly demanding on both resources and time, with limited utility. With an increase in the number of Earth Observation Satellite (EOS) platforms and the enormous data that they generate, there is a need to process the data quickly and efficiently for creating global science products. Towards this goal, an attempt has been made in this article to develop a method for the automatic extraction of the TSs from the time series of Moderate Resolution Imaging Spectroradiometer (MODIS – 250 m) vegetation index (VI), which can then be used for supervised classification to create a land cover map with any classification technique on relevant remotely sensed data. The TSs contained 1.27%, 0.09% and 1.18% of the total pixels for the forest, crop and water classes of the study region. Validation with Advanced Wide Field Sensor (AWiFS – 56 m)-derived national land use/land cover (LULC) map of India shows a complete agreement with the location of what can be considered as pure class pixels. The article also demonstrates and compares the utility of these TSs with an expert choice of TSs on MODIS time-series data using k-nearest neighbour, and support vector machine (SVM) classifiers and on a single-scene Linear Imaging Self-Scanning Sensor-3 (LISS-3 – 24 m) imagery using maximum likelihood (ML) classifier.     

The importance of data type,laser spot density and modelling method for vegetation height mapping in continental China

Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.     

Reference spectra to classify Amazon water types

Reference spectra extracted from spectral libraries can distinguish different water types in images when associated with limnological information. In this study, we compiled available databases into a single spectral library, using field water reflectance spectra and limnological data collected by different researchers and campaigns in the Amazonian region. By using an iterative clustering procedure based on the combination of reflectance and optically active components (OACs), reference spectra representative of the major Amazonian water types were defined from this library. Differences between the resultant limnological classes were also evaluated by paired t-tests at significance level 0.05. Finally, reference spectra were tested for Spectral Angle Mapper (SAM) classification of waters in Hyperion/Earth Observing-One (EO-1) and Medium Resolution Imaging Spectrometer (MERIS)/Environment Satellite (Envisat) images acquired simultaneously as the field campaigns. Results showed highly variable concentrations of OACs due to the complexity of the Amazonian aquatic environments. Ten classes were defined to represent this complexity, broadly grouped into four limnological characteristics: clear waters with low concentrations of OACs (class 1); black waters rich in dissolved organic carbon (DOC) (class 2); waters with large concentrations of inorganic suspended solids (ISSs) (classes 3–7); and waters dominated by chlorophyll-a (chl-a) (classes 8–10). Using the ten reference spectra, SAM classification of the field water curves produced an overall accuracy of 86% with the highest values observed for classes 3, 4, 6 and 7 and the lowest accuracy for classes 1 and 2. The results of paired t-tests confirmed the class differences based on the concentrations of OACs. SAM classification of the Hyperion and MERIS images using ground truth information resulted in overall classification accuracies of 48% and 67%, respectively, with the highest errors associated with specific portions of the scenes that were not adequately represented in the spectral library.     

Boosted decision tree classifications of land cover over Turkey integrating MODIS,climate and topographic data

This study investigates the impact of using different combinations of Moderate Resolution Imaging Spectroradiometer (MODIS) and ancillary datasets on overall and per-class classification accuracies for nine land cover types modified from the classification system of the International Geosphere Biosphere Programme (IGBP). Twelve land cover maps were generated for Turkey using boosted decision trees (BDTs) based on the stepwise addition of 14 explanatory variables derived from a time series of 16-day MODIS composites between 2000 and 2006 (Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and four spectral bands) and ancillary climate and topographic data (minimum and maximum air temperature, precipitation, potential evapotranspiration, aspect, elevation, distance to sea and slope) at 500-m resolution. Evaluation of the 12 BDTs indicated that the BDT built as a function of all the MODIS and climate variables, aspect and elevation produced the highest degree of overall classification accuracy (79.8%) and kappa statistic (0.76) followed by the BDTs that additionally included distance to sea (DtS), and both DtS and slope. Based on an independent validation dataset derived from a pre-existing national forest map and Landsat images of Turkey, the highest overall accuracy (64.7%) and kappa coefficient (0.58) among the 12 land cover maps was achieved by using MODIS-derived NDVI time series only, followed by NDVI and EVI time series combined; NDVI, EVI and four MODIS spectral bands; and the combination of all MODIS and climate data, aspect, elevation and distance to sea, respectively. The largest improvements in producer's accuracies were observed for grasslands (+50%), barrenlands (+46%) and mixed forests (+39%) and in user's accuracies for grasslands (+53%), shrublands (+30%) and mixed forests (+28%), in relation to the lowest producer's accuracy. The results of this study indicate that BDTs can increase the accuracy of land cover classifications at the national scale.     

基于MODIS温度和植被指数产品的山东省土地覆盖变化研究

地表温度(LST)与归一化植被指数(NDVI)构成的NDVI-Ts特征空间具有丰富的地学和生态学内涵。MODIS数据因其优越的时间分辨率、波谱分辨率,已被广泛地运用于各个领域。在本研究中,运用遥感技术和GIS技术相结合的手段,利用NASA提供的MODIS温度产品和NDVI产品,以山东省土地利用图、山东省TM遥感影像图和基于3S技术的山东省森林资源调查项目的外业调查数据为参考和评价标准,以NDVI-Ts时间序列为指标,在进行土地覆盖分类的基础上,分析比较了山东省土地覆盖从2000年到2006年的变化情况。研究结果表明,利用MODIS产品将NDVI-Ts时间序列作为分类特征,在较大尺度范围的土地覆盖分类中具有较高的分类精度,有利于对土地覆盖变化进行动态监测。     

Agricultural burning in the Southeastern United States detected by MODIS

The southeastern United States, including the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia, had a high occurrence of fire activity as detected by the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA Active Fire Product (MOD 14). The analysis of the satellite data from 2001 to 2004 showed that agricultural burning in the southeastern United States accounted for an average of 16% of annual fire activity. The southeastern region contributed an average of 33% of all agricultural burning detected in the contiguous United States. Crop residues that burned in the southeast included rice, winter wheat, sugarcane, soybean and cotton. Much of the agricultural burning occurred in June and from October to January and was related to the harvest of winter wheat and rice in the spring and the harvest of sugarcane, soybean and cotton in the fall and winter. The results showed that cropland burning was spatially dependent on crop type and temporally dependent on management practices (planting/harvesting). Arkansas, Florida, and Louisiana contributed more than 75% of all agricultural burning in the southeast. A 250 m MODIS land cover map cover for 2004 was developed for these three states using a decision tree classification and validation from a field campaign in the fall of 2004. Compared to the standard MODIS 1 km Land Cover Dataset (MOD 12) product ([Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C. (2002), Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of the Environment, 83, 287-302.]), the 250 m classified images contained on average 50% more cropland area and improved the estimation of cropland area based on validation from ground control sites of croplands. Results from the decision tree classification for each state revealed that in 2004 agricultural burning contributed 73%, 54%, and 33% of total fires for Arkansas, Florida, and Louisiana, respectively.