Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images

Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R² = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R² = 0.87; in addition, this index is dependent on the soil and residue lines considered.     

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (<30 m) data are used to assess their accuracy with little regard to the accuracy of the higher spatial resolution reference data. In this study we aimed to investigate whether Landsat Enhanced Thematic Mapper (ETM+)‐derived reference imagery can be more accurately produced using such spectrally informed methods. The efficacy of several spectral index methods to discriminate between burned and unburned surfaces over a series of spatial scales (ground, IKONOS, Landsat ETM+ and data from the MOderate Resolution Imaging Spectrometer, MODIS) were evaluated. The optimal Landsat ETM+ reference image of burned area was achieved using a charcoal fraction map derived by linear spectral unmixing (k = 1.00, a = 99.5%), where pixels were defined as burnt if the charcoal fraction per pixel exceeded 50%. Comparison of coincident Landsat ETM+ and IKONOS burned area maps of a neighbouring region in Mongu (Zambia) indicated that the charcoal fraction map method overestimated the area burned by 1.6%. This method was, however, unstable, with the optimal fixed threshold occurring at >65% at the MODIS scale, presumably because of the decrease in signal‐to‐noise ratio as compared to the Landsat scale. At the MODIS scale the Mid‐Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r ² = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km² during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

Fire patterns in north-eastern Argentina: influences of climate and land use/cover

Fires are one of the most frequent disturbances on terrestrial ecosystems, thus making their understanding and prediction crucial for their management. While the fuels condition are linked to well-known environmental factors, the ignition triggering agents are less understood and may display a more stochastic behaviour. We studied the regional relationship between the fire density and frequency, climate and land use/cover in the north-east region of Argentina using Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots data, daily weather reports and land use/cover data at national level. The pre-fire water availability index showed a significantly negative relationship with hotspot densities (r ²?=?0.51, p < 0.01). The uncultivated land coverage, mainly represented by natural grasslands, was positively associated with the fire density (for Entre Rios, r ²?=?0.74, p < 0.0001 and for Corrientes, r ²?=?0.67, p < 0.0001). The results highlight the potential of remote sensing for fire monitoring in this region and its value in urgently needed policies for fire control and management.     

Regional scale impacts of Tamarix leaf beetles (Diorhabda carinulata) on the water availability of western U.S. rivers as determined by multi-scale remote sensing methods

Tamarix leaf beetles (Diorhabda carinulata) have been widely released on western U.S. rivers to control introduced shrubs in the genus Tamarix. Part of the motivation to control Tamarix is to salvage water for human use. Information is needed on the impact of beetles on Tamarix seasonal leaf production and subsequent water use over wide areas and multiple cycles of annual defoliation. Here we combine ground data with high resolution phenocam imagery and moderate resolution (Landsat) and coarser resolution (MODIS) satellite imagery to test the effects of beetles on Tamarix evapotranspiration (ET) and leaf phenology at sites on six western rivers. Satellite imagery covered the period 2000 to 2010 which encompassed years before and after beetle release at each study site. Phenocam images showed that beetles reduced green leaf cover of individual canopies by about 30% during a 6–8 week period in summer, but plants produced new leaves after beetles became dormant in August, and over three years no net reduction in peak summer leaf production was noted. ET was estimated by vegetation index methods, and both Landsat and MODIS analyses showed that beetles reduced ET markedly in the first year of defoliation, but ET recovered in subsequent years. Over all six sites, ET decreased by 14% to 15% by Landsat and MODIS estimates, respectively. However, results were variable among sites, ranging from no apparent effect on ET to substantial reduction in ET. Baseline ET rates before defoliation were low, 394 mm yr^? 1 by Landsat and 314 mm yr^? 1 by MODIS estimates (20–25% of potential ET), further constraining the amount of water that could be salvaged. Beetle–Tamarix interactions are in their early stage of development on this continent and it is too soon to predict the eventual extent to which Tamarix populations will be reduced. The utility of remote sensing methods for monitoring defoliation was constrained by the small area covered by each phenocam image, the low temporal resolution of Landsat, and the low spatial resolution of MODIS imagery. Even combined image sets did not adequately reveal the details of the defoliation process, and remote sensing data should be combined with ground observations to develop operational monitoring protocols.     

Comparison of MODIS and Landsat TM5 images for mapping tempo–spatial dynamics of Secchi disk depths in Poyang Lake National Nature Reserve,China

Landsat has successfully been applied to map Secchi disk depth of inland water bodies. Operational use for monitoring a dynamic variable like Secchi disk depth is however limited by the 16‐day overpass cycle of the Landsat system and cloud cover. Low spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) image captured twice a day could potentially overcome these problems. However, its potential for mapping Secchi disk depth of inland water bodies has so far rarely been explored. This study compared two image sources, MODIS and Landsat Thematic Mapper (TM), for mapping the tempo–spatial dynamics of Secchi disk depth in Poyang Lake National Nature Reserve, China. Secchi disk depths recorded at weekly intervals from April to October in 2004 and 2005 were related to 5 Landsat TM and 22 MODIS images respectively. Two multiple regression models including the blue and red bands of Landsat TM and MODIS respectively explained 83% and 88% of the variance of the natural logarithm of Secchi disk depth. The standard errors of the predictions were 0.20 and 0.37 m for Landsat TM and MODIS‐based models. A high correlation (r = 0.94) between the predicted Secchi disk depth derived from the two models was observed. A discussion of advantages and disadvantages of both sensors leads to the conclusion that MODIS offers the possibility to monitor water transparency more regularly and cheaply in relatively big and frequently cloud covered lakes as is with Poyang Lake.     

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.     

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.     

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km² study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

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.     

Lake-area mapping in the Tibetan Plateau: an evaluation of data and methods

Lake area derived from remote-sensing data is a primary data source, because changes in lake number and area are sensitive indicators of climate change. These indicators are especially useful when the climate change is not convoluted with a signal from direct anthropogenic activities. The data used for lake-area mapping is important, to avoid introducing unnecessary uncertainty into long-term trends of lake-area estimates. The methods for identifying waterbodies from satellite data are closely linked to the quality and efficiency of surface-water differentiation. However, few studies have comprehensively considered the factors affecting the selection of data and methods for mapping lake area in the Tibetan Plateau (TP), nor of evaluating their consequences. This study tests the dominant data sets (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data) and the methods for automated waterbody mapping on 14 large lakes (>500 km²) distributed across different climate zones of the TP. Seasonal changes in lake area and data availability from Landsat imagery are evaluated. Data obtained in October is optimal because in this month the lake area is relatively stable. The data window can be extended to September and November if insufficient data is available in October. Grouping data into three-year bins decreases the effects of year-to-year seasonal variability and provides a long-term trend that is suitable for time series analysis. The Landsat data (Multispectral Scanner, MSS; Thematic Mapper, TM; Enhanced Thematic Mapper Plus, ETM+; and Operational Land Imager, OLI) and MODIS data (MOD09A1) showed good performance for lake-area mapping. The Otsu method is used to determine the optimal threshold for distinguishing water from non-water features. Several water extraction indices, namely NDWI_McFeeters, NDWI_Xu, and AWEI_non-shadow, yielded high overall classification accuracy (92%), kappa coefficient (0.83), and user’s accuracy (~90%) for lake-water classification using Landsat data. The MODIS data using NDWI_McFeeters and NDWI_Xu showed consistent lake area (r² = 0.99) compared with Landsat data on the corresponding date with root mean square error (RMSE) values of 86.87 and 103.33 km² and mean absolute error (MAE) values of 25.7 and 29.04 km², respectively. The MODIS data is suitable for great lake mapping, which is the case for the large lakes in the TP. Although automated water extraction indices exhibited high accuracy in separating water from non-water, visual examination and manual editing are still necessary. Combined with recent Chinese high-resolution satellites, these remotely sensed imageries will provide a wealth of data for studies of lake dynamics and long-term lake evolution in the TP.     

Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil

The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pará State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1 km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them.     

Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images

Using a combination of moso bamboo forest thematic maps derived from Landsat Thematic Mapper (TM) images, field inventory data, and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images, moso bamboo forest was extracted using the matched filtering (MF) technique and its aboveground carbon storage (AGC) was then estimated. This study presents a feasible method for extracting large-scale moso bamboo forests and for estimating moso bamboo forest AGC based on low-spatial resolution MODIS images. The results showed that moso bamboo forests in the majority of counties can be accurately estimated between actual area and estimates, with an R ² of 0.8453. The fitted accuracy of the AGC model was high (R ² = 0.491). The prediction accuracy of the AGC model was also evaluated using validation samples collected from Lin'an City, with an R ² and root mean square error prediction of 0.4778 and 3.06 Mg C ha^?1, respectively. The AGC in the majority of counties or cities in Zhejiang Province was between 0 and 15 Mg C ha^?1, and to a certain extent the predicted AGC estimates were close to observed ground truth data and representative of the study area.     

Relating spatial patterns of fractional land cover to savanna vegetation morphology using multi-scale remote sensing in the Central Kalahari

Understanding, monitoring, and managing savanna ecosystems requires characterizing both functional and structural properties of vegetation. From a functional perspective, in savannas, quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV), and bare soil (f_BS) is important as it relates to carbon dynamics and ecosystem function. On the other hand, vegetation morphology classes describe the structural properties of the ecosystem. Due to high functional diversity and structural heterogeneity in savannas, accurately characterizing both these properties using remote sensing is methodologically challenging. While mapping both fractional cover and vegetation morphology classes are important research themes within savanna remote sensing, very few studies have considered systematic investigation of their spatial association across different spatial resolutions. Focusing on the semi-arid savanna ecosystem in the Central Kalahari, this study utilized f_PV, f_NPV, and f_BS derived in situ and estimated from spectral unmixing of high- (GeoEye-1), medium- (Landsat TM), and coarse- (MODIS) spatial resolution imagery to investigate: (i) the impact of reducing spatial resolution on both magnitude and accuracy of fractional cover; and (ii) how fractional-cover magnitude and accuracy are spatially associated with savanna vegetation morphology classes. Endmembers for Landsat TM and GeoEye-1 were derived from the image based on purity measures; for MODIS (MCD43A4), the challenge of finding spectral endmembers was addressed following an empirical multi-scale hierarchical approach. GeoEye-1-derived fractional estimates showed comparatively closest agreement with in situ measurements and were used to evaluate Landsat TM and MODIS. Overall results indicate that increasing pixel size caused consistent increases in variance of and error in fractional-cover estimates. Even at coarse spatial resolution, f_PV was estimated with higher accuracy compared with f_NPV and f_BS. Assessment considering vegetation morphology of samples revealed both morphology- and cover-specific differences in accuracy. At larger pixel sizes, in areas with dominant woody vegetation, f_PV was overestimated at the cost of mainly underestimating f_BS; in contrast, in areas with dominant herbaceous vegetation, f_NPV was overestimated with a corresponding underestimation of both f_PV and f_BS. These results underscore that structural and functional heterogeneity in semi-arid savanna both impact retrieval of fractional cover, suggesting that comprehensive remote sensing of savannas needs to take both structure and cover into account.     

Assessing woody biomass in African tropical savannahs by multiscale remote sensing

Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R² = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.     

青藏高原MODIS积雪面积比例产品的精度验证与去云研究

MODIS积雪产品的精度验证和去云处理是积雪监测研究的基础。首先利用青藏高原典型地区的ETM+数据作为“真值”影像,对MODIS积雪面积比例(FSC)产品在无云条件下的精度进行验证,发展了一个基于三次样条函数插值的去云算法,并采用基于“云假设”的检验和地面站积雪覆盖日数(SCD)检验两种方法对去云算法的精度进行了分析评价。结果表明:MODIS FSC产品在青藏高原地区具有较高的精度,与FSC“真值”相比,其平均绝对误差、均方根误差以及相关系数分别为0.098、0.156和0.916;去云算法能够有效地获取云遮蔽像元的FSC信息,平均绝对误差为0.092,用新生成的无云MODIS FSC产品计算得到的SCD与地面观测值具有较高的一致性(87.03%),平均绝对误差为3.82 d。     

Using short-term MODIS time-series to quantify tree cover in a highly heterogeneous African savanna

Reliable mapping of tree cover and tree-cover change at regional, continental, and global scales is critical for understanding key aspects of ecosystem structure and function. In savannas, which are characterized by a variable mixture of trees and grasses, mapping tree cover can be especially challenging due to the highly heterogeneous nature of these ecosystems. Our objective in this article was to develop improved tools for large-scale classification of savanna tree cover in grass-dominated savanna ecosystems that vary substantially in woody cover over fine spatial scales. We used multispectral, low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to identify the bands and metrics that are best suited to quantify woody cover in an area of the Serengeti National Park, Tanzania. We first used 1-m resolution panchromatic IKONOS data to quantify tree cover for February 2010 in an area of highly variable tree cover. We then upscaled the classification to MODIS (250 m) resolution. We used a 2 year time series (IKONOS date ± 1 year) of MODIS 16 day composites to identify suitable metrics for quantifying tree cover at low resolution, and calculated and compared the explanatory power of three different variable classes for four MODIS bands using Lasso regression: longitudinal summary statistics for individual spectral bands (e.g. mean and standard deviation), Fourier harmonics, and normalized difference vegetation index (NDVI) green-up metrics. Longitudinal summary statistics showed better explanatory power (R ² = 73% for calibration data; R ² = 61% for validation data) than Fourier or green-up metrics. The mid-infrared, near-infrared, and NDVI bands were all important predictors of tree cover. Mean values for the time series were more important than other metrics, suggesting that multispectral data may be more valuable than within-band seasonal variation obtained from time series data for mapping tree cover. Our best model improved substantially over the MODIS Vegetation Continuous Fields product, often used for quantifying tree cover in savanna systems. Quantifying tree cover at coarse spatial resolution using remote-sensing approaches is challenging due to the low amount and high heterogeneity of tree cover in many savanna systems, and our results suggest that products that work well at global scales may be inadequate for low-tree-cover systems such as the Serengeti. We show here that, even in situations where tree cover is low (<10%) and varies considerably across space, satisfactory predictive power is possible when broad spectral data can be obtained even at coarse spatial resolution.     

Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data

Snow and glaciers in the mountain watersheds of the Tarim River basin in western China provide the primary water resources to cover the needs of downstream oases. Remote sensing provides a practical approach to monitoring the change in snow and glacier cover in those mountain watersheds. This study investigated the change in snow and glacier cover in one such mountain watershed using multisource remote-sensing data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat (Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+)), Corona, and Google Earth^TM imagery. With 10 years’ daily MODIS snow-cover data from 2002 to 2012, we used two de-cloud methods before calculating daily snow-cover percentage (SCP), annual snow-cover frequency (SCF), and annual minimum snow-cover percentage (AMSCP) for the watershed. Mann–Kendall analysis showed no significant trend in any of those snow-cover characterizations. With a total of 22 Landsat images from 1967 to 2011, we used band ratio and supervised classification methods for snow classification for Landsat TM/ETM+ images and MSS images, respectively. The Landsat snow-cover data were divided into two periods (1976–2002 and 2004–2011). Statistical tests indicated no significant difference in either the variance or mean of SCPs between the two periods. Three glaciers were identified from Landsat images of 1998 and 2011, and their total area increased by 12.6%. In addition, three rock glaciers were also identified on both the Corona image of 1968 and the Google high-resolution image of 2007, and their area increased by 2.5%. Overall, based on multisource remote-sensing data sets, our study found no evidence of significant changes in snow and glacier cover in the watershed.     

Satellite estimation of forest carbon using regression models

Periodic monitoring of forest carbon is important, since forest cover is changing rapidly in many parts of the world, and becomes a major source of terrestrial carbon emission that may be one of the main drivers of global climate change. Regression is often used to estimate forest variables (including carbon) using satellite sensor data though a low coefficient of determination (R ²) is apparent and this research was designed to investigate both traditional and alternate regression approaches to increase the magnitude of R ². The study area was located in southeastern Bangladesh. Data from Landsat Enhanced Thematic Mapper Plus (ETM+) and ground‐based forest survey were used. This research explored the use of dummy variables in regression models to increase R ², while the dummies were set from the optimal stratification of forestland. The finding will heighten the accuracy of forest attribute estimation and help to understand terrestrial carbon dynamics and global climate change.