Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.     

森林火灾后生态变化遥感监测评价模型的构建方法研究

通过攀西林区云南松林的一系列旧火烧迹地的更新恢复和生态变化的遥感调查，对各生态因子的空间分布特征及生态变化的影响规律进行分析，确定了评价因子（变量）及其评价标准，利用遥感信息，以及地形、土壤、林分和林木受害程度等要素的８个因子的模糊综合评判结果和火烧年限等为变量，通过多组数据的多元统计分析，建成森林火灾后生态变化遥感监测评价模型，经野外调查结果验证分析，达到了预期的攻关目标。为使该模型能适应森林生态遥感监测运行系统的需要，对各监测因子数据的获取、植被指数的提取等方面进行了深入的方法探索。     

An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery

Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.     

Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation-substrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R² of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R² = 0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R² = 0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.     

Assessing the sensitivity of site index models developed using bi-temporal airborne laser scanning data to different top height estimates and grid cell sizes

Site productivity and forest growth are critical inputs into projecting wood volume and biomass accumulation over time. Site productivity, which is determined most commonly using site index models is also the primary criterion to consider many forest management decisions. Most of the previous research utilizing the remote sensing data for assessment of site index with forest height are based on the existing site index models developed with traditional dendrometric methods. However, these traditional methods are both time-consuming and expensive. This study demonstrates how bi-temporal airborne laser scanning (ALS) data collected within the 8-year period can be used for the development of site index models for Scots pine. The accuracy of ALS-derived models was assessed by comparison to the reference site index model developed based on data from stem analysis of 174 felled Scots pine trees. We evaluated the effect of different height metrics and grid cell size on the trajectory of site index models developed from ALS-derived measurements. Four methods of estimating top height from ALS point clouds were evaluated: 95th, 99th and 100th percentiles of point clouds and an individual tree detection approach (ITD). The models were created for a range of grid cell sizes: 10 × 10 m, 30 × 30 m, and 50 × 50 m. The results indicate that bitemporal ALS data could substitute traditional methods that have been applied to date for stand growth modelling. It was found that top height increment can be estimated by using both ITD approach and the 100th percentile of point cloud giving an appropriate top height (TH) increment estimation. Observed growth curves of reference trees agreed best with the trajectories that were obtained based on TH calculated using ITD method (R² = 0.892) and 100th percentile (R² = 0.797). In case of TH obtained from 99th and 95th percentiles only weak correlation was found: R² = 0.358 and R² = 0.213, accordingly. The height growth models developed with 95th and 99th percentiles of point cloud were not compatible with the reference model. We also found that grid cell size did not affect the model height growth trajectories. Irrespective of the grid cell size, the obtained model trajectories for the given method of TH estimation are nearly identical for cells 10 × 10, 30 × 30 and 50 × 50 m.     

Assessing the spatio-temporal variability of vegetation productivity in Africa: quantifying the relative roles of climate variability and human activities

ABSTRACT

Quantitative attribution at the individual pixel level of the relative contributions of climate variability and human activities to vegetation productivity dynamics across Africa is generally lacking. This is because of the difficulty in establishing a baseline or potential vegetation against which the relative impacts of these factors can be assessed. This study addresses these gaps. First, annual potential net primary productivity (NPP_P) for 2000–2014 was estimated for Africa using a model constructed from samples of NPP and environmental covariates from protected areas. Second, trends in NPP_P, actual NPP (NPP_A), and human-appropriated NPP (NPP_H?=?NPP_P ? NPP_A) were estimated and used in quantifying the relative contributions of climate and human activities to NPP dynamics. Over 2000–2014, NPP improvement was largely concentrated in equatorial and northern Africa, while subequatorial Africa exhibited the most NPP decline. Parts of Mali, Burkina Faso, and the central Africa region are associated with the greatest influence of climate-driven NPP improvement. Areas where humans dominated NPP decline include parts of Ethiopia and South Africa. Climate had a stronger role in driving NPP decline in subequatorial Africa. Nonetheless, further work is required to validate the results of this study with high-resolution imagery and field information.     

Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed

An extensive land cover change was triggered by a series of typhoons, especially Typhoon Morakot in 2009 in Taiwan. The normalized difference vegetation index (NDVI) series from multiple satellite images were applied to monitor the change processes of land cover. This study applied spatiotemporal analysis tools, including empirical orthogonal functions (EOF), and multiple variograms in analyzing space–time NDVI data, and detected the effects of large chronological disturbances in the characteristics of land cover changes. Spatiotemporal analysis delineated the temporal patterns and spatial variability of NDVI caused by these large typhoons. Results showed that mean of NDVI decreased but spatial variablity of NDVI increased after typhoons in the study area. The EOF can clarify the major component of NDVI variations and identify the core area of the NDVI changes. Various approaches showed consistent results that Typhoon Morakot significantly lowered the NDVI in land cover change process. Furthermore, the spatiotemporal analysis is an effective monitoring tool, which advocates the use of the index for the quantification of land cover change and resilience.     

Assessing the spatial distribution of crop areas using a cross-entropy method

While crop production statistics are reported on a geopolitical – often national – basis, we often need to know, for example, the status of production or productivity within specific sub-regions, watersheds, or agro-ecological zones. Such re-aggregations are typically made using expert judgments or simple area-weighting rules. We describe a new, entropy-based approach to the plausible estimates of the spatial distribution of crop areas. Using this approach tabular crop production statistics are blended judiciously with an array of other secondary data to assess the areas of specific crops within individual ‘pixels’—typically 25–100 km² in size. The information utilized includes crop production statistics, farming system characterization, satellite-based interpretation of land cover, biophysical crop suitability assessments, and population density. An application is presented in which Brazilian state level production statistics are used to generate pixel level crop area data for eight crops. To validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of municipality level areas in Brazil, and compared those estimates with actual municipality statistics. The approach produced extremely promising results. We then examined the robustness of these results compared to simplified approaches to spatializing crop production statistics and showed that, while computationally intensive, the cross-entropy method does provide more reliable spatial allocations.     

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVI_v) and bare soil (NDVI_s), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVI_v and NDVI_s at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVI_s and NDVI_v can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVI_v and NDVI_s. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVI_v and NDVI_s maps for FVC estimation on regional and global scales.     

Mapping and characterizing the vegetation types of the Democratic Republic of Congo using SPOT VEGETATION time series

The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarse-resolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from coarse-resolution (GLC2000) and high-resolution imagery (Africover).     

Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory

Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods. The reason to limit the gap probability theory applied in the product algorithm is the availability and accuracy of leaf area index (LAI) and clumping index (CI) products. With the improvement of the LAI and CI products, it is necessary to assess whether the algorithm based on gap probability theory using the present products can improve the accuracy of FVC products. In this study, we generated the FVC estimates based on the gap probability theory (FVCgap) with a resolution of 500 m every 8 days for Europe. FVCgap estimates were validated with field FVC measurements of ImagineS from 2013 to 2015 for crop types. Two existing FVC products, Geoland2 Version1 (GEOV1) and Multisource data Synergized Quantitative remote sensing production system (MuSyQ), were used to inter-compare with the FVCgap estimates. FVCgap estimates showed a better agreement with field FVC measurements, with lowest root mean square error (RMSE) (0.1211) and bias (0.0224), than GEOV1 and MuSyQ FVC products. The inter-annual and seasonal variations of FVCgap estimates were also showed the most consistent with field measurements.     

Quantifying local fire regimes using the Landsat data-archive: a conceptual framework to derive detailed fire pattern metrics from pixel-level information

Free and open access to the Landsat archive has enabled the detection and delineation of an unprecedented number of fire events across the globe. Despite the availability and potential of these data, few studies have analysed residual vegetation patterns and/or partial mortality of fire across the Canadian boreal forest, and those available, are either incomplete or inaccurate. Further, they all differ in the methods and spatial language, which makes it difficult for managers to interpret fire patterns over large areas. There is an urgent need for methods to help unify fire pattern observations across the Canadian boreal forest. This study explores the capacity of the Landsat data archive when coupled with a recently developed fire mapping approach and a robust spatial language to characterize and compare tree mortality patterns across the boreal plains ecozone, Canada. With 507 fires 2.5?Mha mapped, this study represents the most comprehensive analysis of mortality patterns for study area. Summaries from this demonstration generated an accurate characterization of the fire patterns the various ecoregions based on seven key fire metrics. The comparison between ecoregions revealed differences in the amount of residual vegetation, which in turn suggested various climate, topography and/or vegetation ecosystem drivers.     

Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R² > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.     

Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm

The objective of this paper is to demonstrate a new method to map the distributions of C₃ and C₄ grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C₃ and C₄ grasses in our study area such as elevation, soil pH, soil organic matter and soil texture.     

Assessment of the three factors affecting Myanmar's forest cover change using Landsat and MODIS vegetation continuous fields data

Long-term observation of the earth is essential for studying the factors affecting global environmental changes. Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information. In this study, we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm. Our results show that Myanmar's forests have declined 0.68% annually over this six-year period. We validated our derived change results and found the overall accuracy to be greater than 88%. We also assessed forest loss from protected areas, areas close to roads, and areas subject to fire, which were most likely to lose forested area. The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion, fire, and the construction of highways. This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.     

Estimation of leaf area index using PROSAIL based LUT inversion,MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation,North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R² = 0.9, RMSE = 0.14), peak (R² = 0.87, RMSE = 0.21) and senescence (R² = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.     

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.     

基于多角度遥感的植被指数与叶面积指数的线性关系研究

以辐射传输方程PROSAIL为基础,模拟不同观测天顶角和不同叶面积指数(LAI)下的植被冠层光谱。利用模拟的冠层光谱构建3种常用的植被指数,并分析不同观测天顶角下叶面积指数变化对3种植被指数的影响。结果表明,MSR能较好解决由于LAI变化而引起的饱和现象。观测天顶角为-30°时,3种植被指数与叶面积指数的线性关系较30°和0°时好。     

Assessing factors impacting the spatial discrepancy of remote sensing based cropland products: A case study in Africa

Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate and timely information on the spatial distribution of cropland is critical for the effective management of crop production and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remote sensing data are available for public use. However, discrepancies exist among these cropland products, and the level of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identify and assess the driving factors contributing to the spatial discrepancies among four cropland products derived from remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these cropland products and assess the impact of environmental factors such as elevation dispersion, field size, land-cover richness and frequency of cloud cover on these spatial differences. Results from this study show that the overall accuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasets covering Sahel zone and along the West African coasts. This study has identified land-cover richness as the driving factor with the largest contribution to the spatial disagreement among cropland products over Africa, followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. To improve the accuracy of future cropland products for African regions, the data producers are encouraged to take a multi-classification approach and incorporate multi-sensors into their cropland mapping processes.