Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

基于小波变换的MODIS与ETM数据融合研究

遥感影像融合方法多样，但针对空间分辨率相差十倍、甚至十几倍的不同数据源影像进行融合的研究很有限。有效算法也较少。MODIS影像高光谱数据具有36个相互配准的光谱波段信息，然而其0．25km～1km的低空间分辨率，却限制了其应用潜力。本文基于小波变换的算法思想提出了一种MODIS与Landsat ETM(空间分辨率30m)数据融合的方法，能够有效的将MODIS的光谱信息和ETM的空间几何信息结合起来，并在此基础上分析了地形阴影对融合的影响，为MODIS数据用于制作较大比例尺的土地利用现状图等提供了可能。     

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.     

Regional mapping of human settlements in southeastern China with multisensor remotely sensed data

Mapping human settlements from remotely sensed data at regional and global scales has attracted increasingly attention but remains a challenge. The thresholding technique is a common approach for settlement mapping based on the DMSP-OLS data. However, this approach often omits the areas with small proportional settlements such as towns and villages and overestimates urban extents, resulting in information loss of spatial patterns. This paper explored an integrated approach based on a combined use of multiple remotely sensed data to map settlements in southeastern China. Human settlements for selected sites were mapped from Landsat ETM+ images with a hybrid approach and they were used as reference data. The DMSP-OLS and Terra MODIS NDVI data were combined to develop a settlement index image. This index image was used to map a pixel-based settlement image with expert rules. A regression model was established to estimate fractional settlements at the regional scale, which the DMSP-OLS and MODIS NDVI data were used as independent variables and the settlement data derived from ETM+ images were used as a dependent variable. This research indicated that a combination of DMSP-OLS and NDVI variables provided a better estimation performance than single DMSP-OLS or NDVI variable, and the integrated approach for settlement mapping at the regional scale was promising. Compared to the results from the traditional thresholding technique, the estimated fractional settlement image in this paper greatly improved the spatial patterns of settlement distribution and accuracy of settlement areas. This paper provided a rapid and accurate approach to estimate fractional settlements from coarse spatial resolution images at the regional scale by combining a limited number of medium spatial resolution images. This research is especially valuable for timely updating settlement databases at regional and global scales with limited time, labor, and cost.     

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km². AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.     

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.     

Vectorial boundary-based sub-pixel mapping method for remote-sensing imagery

This article presents a vectorial boundary-based sub-pixel mapping (VBSPM) method to obtain the land-cover distribution with finer spatial resolution in mixed pixels. With inheritance from the geometric SPM (GSPM), VBSPM first geometrically partitions a mixed pixel using polygons, and then utilizes a vectorial boundary extraction model (VBEM), rather than the rasterization method in GSPM, to determine the location and length of each edge in the polygon, while these edges are located at the boundary of and within the interior of the mixed pixel. Furthermore, VBSPM uses a decay function to manage the mixed pixels along the image boundary region due to the missing parts of their neighbours. Finally, a ray-crossing algorithm is employed to determine the land-cover class of each sub-pixel in terms of vectorial boundaries. The experiments with artificial and remotely sensed images have demonstrated that VBSPM can reduce the inconsistency between the boundaries of different land-cover classes, approximately calculating errors with an odd zoom factor, and achieve more accurate sub-pixel mapping results than the hard classification methods and GSPM.     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

Use of MODIS NDVI data to improve forest-area estimation

The objective of this article is to develop and test a methodology capable of using medium spatial resolution satellite imagery to improve forest-area statistics derived from ground sampling. The methodology builds on the evidence that multitemporal Normalized Difference Vegetation Index (NDVI) images bring significant information on the spatial distribution of forest surfaces. Consequently, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images are potentially useful to improve forest-area assessment based on ground data. This expectation is verified in Tuscany (central Italy) using forest-area references extracted from the Coordination of Information on Environment (CORINE) land-cover map. The accuracy of forest-area statistics obtained at province level by different reference samplings is first assessed. Next, locally calibrated regression analyses are applied to multitemporal MODIS NDVI images in order to obtain per-pixel forest-area estimates. Two statistical methods (the direct expansion and the regression estimator) are finally used to combine these estimates with the ground data and produce corrected per-province statistics. The experimental results confirm that MODIS NDVI data contain relevant information on forest distribution, which can be efficiently extended over the land surface by locally calibrated regressions. The obtained estimates can be combined with the ground data for enhancing forest-area assessment at province level. To this aim, the regression estimator gives the best performance for all sampling densities of the reference data.     

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.     

Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery

Conversion of native forests to agriculture and urban land leads to fragmentation of forested landscapes with significant consequences for habitat conservation and forest productivity. When quantifying land-cover patterns from airborne or spaceborne sensors, the interconnectedness of fragmented landscapes may vary depending on the spatial resolution of the sensor and the extent at which the landscape is being observed. This scale dependence can significantly affect calculation of remote sensing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and its subsequent use to predict biophysical parameters such as the fraction of photosynthetically active radiation intercepted by forest canopies (fPAR). This means that simulated above-ground net primary productivity (NPP_A) using canopy radiation interception models such as 3-PG (Physiological Principles for Predicting Growth), coupled with remote sensing observations, can yield different results in fragmented landscapes depending on the spatial resolution of the remotely sensed data.We compared the amount of forest fragmentation in 1?km SPOT-4 VEGETATION pixels using a simultaneously acquired 20?m SPOT-4 multispectral (XS) image. We then predicted NPP_A for New Zealand native forest ecosystems using the 3-PG model with satellite-derived estimates of the fPAR obtained from the SPOT-4 VEGETATION sensor, using NDVI values with and without correction for fragmentation. We examined three methods to correct for sub-pixel fragmentation effects on NPP_A. These included: (1) a simple conversion between the broad 1?km scale NDVI values and the XS NDVI values; (2) utilization of contextural information from XS NDVI pixels to derive a single coefficient to adjust the 1?km NDVI values; and (3) calculation of the degree of fragmentation within each VEGETATION 1?km pixel and reduce NDVI by an empirically derived amount based on the proportional areal coverage of forest in each pixel. Our results indicate that predicted NPP_A derived from uncorrected 1?km VEGETATION pixels was significantly higher than estimates using adjusted NDVI values; all three methods reduced the predicted NPP_A. In areas of the landscape with a large degree of forest fragmentation (such as forest boundaries) predictions of NPP_A indicate that the fragmentation effect has implications for spatially extensive estimates of carbon uptake by forests.     

Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data

Recent advances in instrument design have led to considerable improvements in wildfire mapping at regional and global scales. Global and regional active fire and burned area products are currently available from various satellite sensors. While only global products can provide consistent assessments of fire activity at the global, hemispherical or continental scales, the efficiency of their performance differs in various ecosystems. The available regional products are hard-coded to the specifics of a given ecosystem (e.g. boreal forest) and their mapping accuracy drops dramatically outside the intended area. We present a regionally adaptable semi-automated approach to mapping burned area using Moderate Resolution Imaging Spectroradiometer (MODIS) data. This is a flexible remote sensing/GIS-based algorithm which allows for easy modification of algorithm parameterization to adapt it to the regional specifics of fire occurrence in the biome or region of interest. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and therefore retains the variability of spectral response of the area affected by fire and has the potential to be used beyond binary burned/unburned mapping for the first-order characterization of fire impacts from remotely sensed data. The algorithm inputs the MODIS Surface Reflectance 8-Day Composite product (MOD09A1) and the MODIS Active Fire product (MOD14) and outputs yearly maps of burned area with dNBR values and beginning and ending dates of mapping as the attributive information. Comparison of this product with high resolution burn scar information from Landsat ETM+ imagery and fire perimeter data shows high levels of accuracy in reporting burned area across different ecosystems. We evaluated algorithm performance in boreal forests of Central Siberia, Mediterranean-type ecosystems of California, and sagebrush steppe of the Great Basin region of the US. In each ecosystem the MODIS burned area estimates were within 15% of the estimates produced by the high resolution base with the R² between 0.87 and 0.99. In addition, the spatial accuracy of large burn scars in the boreal forests of Central Siberia was also high with Kappa values ranging between 0.76 and 0.79.     

Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas

Wildfires are a major cause of land degradation in the Mediterranean region due to their frequent recurrence in the same areas. The evaluation of fire risk is therefore of high practical importance, particularly during the summer arid season, when fires are most frequent and harmful. Recent studies have demonstrated that the evaluation of dynamic fire risk can be carried out by the use of remotely sensed images, and specifically of NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. This use relies on the sensitivity of the index to vegetation dryness, which is a major predisposing factor for fire occurrence. Several problems, however, remain linked to the spatial variability of the risk in environmentally heterogeneous areas, which requires the application of suitable processing techniques to the low-resolution imagery.The current work reports on the development and testing of different methodologies for estimating dynamic fire risk by the use of NOAA-AVHRR data. The investigation was conducted in Tuscany (Central Italy) using a large archive of fires that occurred in the region and NOAA-AVHRR NDVI data of 16 years (1985-2000). Relying on previous methodological achievements of our group and other research groups, several procedures were tested to extract information related to fire risk from the remotely sensed images. These trials led to define an optimum method which is based on the identification of pixels where the accordance between interyear variations in fire probabilities and NDVI values is maximum. The accuracy of the risk estimates from this optimum method was finally evaluated by a leave-one-out cross-validation strategy. In this way, the performance of the methodology was assessed, together with its potential for operational fire risk monitoring and forecasting in Mediterranean areas.     

Analysis of the relationship between spatial pattern and spectral detectability of areas burned in southern Africa using satellite data

Fires in Africa affect atmospheric emissions and carbon sequestration, landscape patterns, and regional and global climatic conditions. Studies of these effects require accurate estimation of the extent of measurable fire events. The goal of this study was to assess the influence of burned area spatial patterns on the spectral detectability of burned areas. Six Landsat‐7 ETM+ images from the southern Africa were used for burned area mapping and spatial pattern analysis, while contemporaneous MODIS 500 m spatial resolution images were used to measure the spectral detectability of burned areas. Using a 15 by 15 km sample quadrats analysis, we showed that above a burned area proportion threshold of approximately 0.5 the spectral detectability of burned areas increase due to the decrease in the number of mixed pixels. This was spatially related to the coalescence of burned patches and the decrease in the total burned area perimeter. Simple burned area shapes were found at the Botswana site, where the absence of tree cover and the presence of bright surfaces (soil and dry grass) enhanced the spectral contrast of the burned surfaces, thus enabling better estimates of burned area extent. At the Zambia and Congo sites, landscape fragmentation due to human activity and the presence of a tree vegetation layer, respectively, contribute to the presence of small burned area patches, which may remain undetectable using moderate spatial resolution satellite imagery, leading to less accurate burned area extent estimates.     

Verification of the analysis method for extracting the spatial continuity of the vegetation distribution on a regional scale

Vegetation plays a key role in not only improving urban environments, but also conserving ecosystems. The spatial continuity of vegetation distributions can be expected to make green corridors for landscape management, wind paths against heat island phenomena. In this paper, we develop a spatial analysis method of vegetation distributions using remotely sensed data on a regional scale. The method consists of a spatial autocorrelation analysis, an overlay analysis, and a hydrological analysis with the Normalized Difference Vegetation Index (NDVI) adopted as the proxy of vegetation abundance. Application of the method leads to the extraction of the lines between the core areas and sparse areas of vegetation. The purpose of this study is to verify our method through applying a vegetation map digitized from aerial photographs. The map contained three vegetation types of land cover: grasslands, agricultural fields, and tree-covered areas. We use remotely sensed data collected at four different time periods at the regional scale, along with information on the seasonal fluctuations of the vegetation. As a result, the exclusion of seasonal land-cover changes, as in the reaping of agricultural fields, in the process of applying the proposed method produces an effect. The analysis reveals steady areas unaffected by the seasonal fluctuation of vegetation along the lines extracted by applying the proposed method.     

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.     

Towards the development of a regional version of MOD17 for the determination of gross and net primary productivity of oil palm trees

Conducting quantitative studies on the carbon balance or productivity of oil palm is important for understanding the role of this ecosystem in global climate change. The MOD17 algorithm is used for processing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to generate the values of gross primary productivity (GPP) and net primary productivity for input to global carbon cycle modelling. In view of the increasing importance of data on carbon sequestration at regional and national levels, we have studied one important factor affecting the accuracy of the implementation of MOD17 at the sub-global level, namely the database of MODIS land cover (MOD12Q1) used by MOD17. By using a study area of approximately 7 km × 7 km (49 MODIS pixels) in semi-rural Johor in Peninsular Malaysia and using Google Earth 0.75 m resolution images as ground data, we found that the land-cover type for only 16 of these 49 MODIS pixels was correctly identified by MOD12Q1 using its 1 km resolution land-cover database. This leads to errors of 24% to 50% in the maximum light use efficiency, leading to corresponding errors of 24% to 50% in the GPP. We show that by using the Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC) land-cover database developed by Gong et al., this particular error can be essentially eliminated, but at the cost of using extra computing resources.     

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.     

Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery

The fraction of photosynthetically active radiation (FPAR) absorbed by a vegetation canopy is an important variable for global vegetation modelling and is operationally available from data of the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor starting from the year 2000. Product validation is ongoing and important for constant product improvement, but few studies have investigated the specific accuracy of MODIS FPAR using in situ measurements and none have focused on agricultural areas. This study therefore presents a validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in western Uzbekistan. High-resolution FPAR maps were compiled via linear regression between in situ FPAR measurements and the RapidEye normalized difference vegetation index (NDVI) for the 2009 season. The data were aggregated to the MODIS scale for comparison. Data on the percentage cover of agricultural crops per MODIS pixel allowed investigation of the impact of spatial heterogeneity on MODIS FPAR accuracy. Overall, the collection 5 MODIS FPAR overestimated RapidEye FPAR between approximately 6% and 15%. MODIS quality flags, the underlying biome classification and spatial heterogeneity were investigated as potential sources of error. MODIS data quality was very good in all cases. A comparison of the MODIS land-cover product with high-resolution land-use classification revealed a significant misclassification by MODIS. Yet, we found that the overestimation of MODIS FPAR is independent of classification accuracy. The results indicate that the amount of background information, present even in the most homogeneous pixels (～70% crop cover), is most likely the reason for the overestimation. The behaviour of pure pixels could not be investigated due to a lack of appropriate pixels.