Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data

Bromus tectorum (cheatgrass) is an annual Eurasian grass that has invaded rangelands of the western USA. Being both a fire follower and a fire promoter, it can rapidly exclude native vegetation and is among the greatest threats to conservation in the region. Key to land management is a strong understanding of B. tectorum distribution and density. Percentage ground cover of B. tectorum was estimated and mapped as a continuous variable over 13.3?million?ha in Nevada, USA. Estimation involved a statistical model derived from 262 training plots, two dates of six scenes from Landsat 7 ETM+ imagery collected in 2001, and elevation. Absence of B. tectorum in many plots led to a dataset that was left‐censored at zero for the response variable, B. tectorum ground cover. Tobit regression, a method for modelling censored data, was found to produce a better model from these data than ordinary least squares regression. The two dates of the imagery were used to derive a variable representing phenology of the landscape. The derived phenology (in quadratic form), elevation, and the late‐season green band were statistically significant in the model development. Additionally, a brightness index was used to limit estimates in bright and dark portions of the imagery such as playas and lakes. Final map accuracy determined from an additional 75 independent assessment plots showed good correspondence between sampled and estimated B. tectorum ground cover (r?=?0.71) and the root‐mean‐square error for estimated ground cover is 9.1%.     

MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data

The tasselled cap concept is extended to Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF‐Adjusted Reflectance (NBAR, MOD43) data. The transformation is based on a rigid rotation of principal component axes (PCAs) derived from a global sample spanning one full year of NBAR 16‐day composites. To provide a standard for MODIS tasselled cap axes, we recommend an orientation in MODIS spectral band space as similar as possible to the orientation of the Landsat Thematic Mapper (TM) tasselled cap axes. To achieve this we first transformed our global sample of MODIS NBAR reflectance values to TM tasselled cap values using the existing TM transformation, then used an existing algorithm (Procrustes) to compute the transformation that minimizes the mean square difference between the TM transformed NBAR values and NBAR PCA values. This transformation can then be used as a standard to rotate the MODIS NBAR PCA axes into a new MODIS Kauth–Thomas (KT) orientation. Global land cover patterns in tasselled cap space are demonstrated graphically by linking the global sample with several other products, including the MODIS Land Cover product (MOD12) and the MODIS Vegetation Continuous Fields product (MOD44). Patterns seen at this global scale agree with previous explorations of TM tasselled cap space, but are shown here in greater detail with a globally representative sample. Temporal trends of eight smaller‐scale BigFoot Project (www.fsl.orst.edu/larse/bigfoot) sites were also examined, confirming the spectral shifts in tasselled cap space related to phenology.     

Differentiation of Acacia koa forest stands across an elevation gradient in Hawai‘i using fine-resolution remotely sensed imagery

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been applied at the landscape level because of small-scale spatial variability. The goal of this study is to compare the differentiation of koa forest types across an elevation/temperature gradient ranging from 1200 to 2050 m asl (17–13°C mean annual temperature (MAT)) through the analysis of field measurements of forest structure and fine-resolution remotely sensed imagery. Several vegetation indices (VIs) (atmospherically resistant vegetation index (ARVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), simple ratio (SR) and modified simple ratio (MSR)) are calculated from IKONOS satellite imagery of these stands and analysed using supervised classification techniques. This procedure allows a clear differentiation of koa stands from areas dominated by grasses, shrubs and bare lava. Across the elevation gradient, VIs allow differentiation of three koa forest stand classes at upper, intermediate and lower elevations. In agreement with the image classification, analysis of variance (ANOVA) of tree height and leaf phosphorus (P) suggests that there are also three significantly different groups of koa stands at those elevations. A landscape-scale map of land cover and koa stand classes demonstrates both the general trend with elevation and the small-scale heterogeneity that exists across the elevation gradient. Application of these classification techniques with fine spatial resolution imagery can improve the characterization of different koa stand types across the islands of Hawai‘i, which should aid both the conservation and utilization of this ecologically important species.     

Development of an approach for generation of temporally complete daily nadir MODIS reflectance time series

Consistent, spatially and temporally complete reflectance time series are required for reliable terrestrial monitoring. The Moderate Resolution Imaging Spectroradiometer (MODIS), like other polar-orbiting wide field of view satellite sensors, can provide global observations on a nearly daily basis, but the sparseness of valid observations due to cloud, residual atmospheric effects, and sensor anomalies, may result in gaps in the derived reflectance time series. This paper presents an approach for the generation of temporally complete daily MODIS 500 m nadir view BRDF-adjusted reflectance (NBAR) time series. The research is illustrated and assessed quantitatively using two years of cloud and snow screened, daily MODIS Terra and Aqua reflectance data at four sites in Africa, and demonstrated for phenology monitoring using NBAR derived NDVI time series. The components of the approach include: 1) an outlier detection algorithm to remove residual anomalous daily observations undetected in the upstream processing, 2) the dynamic generation of NBAR time series on a daily basis when seven or more observations are available for a day under consideration over a 16-day period, and 3) the means to gap fill the NBAR time series where less than 7 observations are available. The MODIS Ross-Thick/Li-Sparse-Reciprocal BRDF model is used with a rolling approach whereby a 16-day BRDF inversion window is moved on a daily overlapping basis to provide more reliable outlier detection and daily NBAR. NBAR gap filling in periods of missing observations is investigated using static land cover specific archetype BRDF parameters and using BRDF parameters defined adaptively from the temporally closest 16-day periods with 7 or more observations. Scaling factor estimators using ordinary least squares (OLS) and median-based robust least squares regression are investigated, and the robust method is demonstrated to provide on average temporally more coherent gap filled NBAR values. For regions with persistent clouds, the utility of the adaptive NBAR gap filling method is demonstrated to be severely limited due to the decreased likelihood that the surface BRDF at each gap can be described reliably. The reliability of the NBAR gap filling methodology is evaluated statistically using a cross-validation approach. For the small number of study site considered, the adaptive method is shown to provide more accurate results than the archetype method when there are more than an average of ~ 4-5 observations per 16-day window, or when a gap day is on average less than about 30 days from a 16-day period with 7 or more observations. The resulting gap free daily NBAR time series and derived daily NBAR NDVI generated by the approach is shown to capture phenological variations in a coherent temporally consistent manner, suggesting that it is a fruitful avenue for future research and validation.     

Extracting structural characteristics of herbaceous floodplain vegetation under leaf‐off conditions using airborne laser scanner data

Hydrodynamic models of river flow need detailed and accurate friction values as input. Friction values of floodplain vegetation are based on vegetation height and density. To map spatial patterns of floodplain vegetation structure, airborne laser scanning is a promising tool. In a test for the lower Rhine floodplain, vegetation height and density of herbaceous vegetation were measured in the field at 42 georeferenced plots of 200 m² each. Simultaneously, three airborne laser scanning (ALS) surveys were carried out in the same area resulting in three high resolution, first pulse, small‐footprint datasets. The laser data surveys differed in flying height, gain setting and laser diode age. Point density of the laser data varied between 10 and 75 points m^?2. Point heights relative to the DTM derived from the ALS data were used in all analyses. Laser points were labelled as either vegetation or ground using three different methods: (1) a fixed threshold value; (2) a flexible threshold value based on the inflection point in the point height distribution; and (3) using a Gaussian distribution to separate noise in the ground surface points from vegetation. Twenty‐one statistics were computed for each of the resulting vegetation‐point distributions, which were subsequently compared with field observations of vegetation height. Additionally, the percentage index (PI) was computed to relate density of vegetation points to hydrodynamic vegetation density. The vegetation height was best predicted by using the inflection method for labelling and the 95 percentile as a regressor (R ² = 0.74–0.88). Vegetation density was best predicted using the threshold method for labelling and the PI as a predictor (R ² = 0.51). The results of vegetation height prediction were found to depend on the combined effect of flying height, gain setting or laser diode age. The quality of the estimation of vegetation height and density is also affected by point density, for densities lower than 15 points m^?2. We conclude that high resolution ALS data allows to estimate vegetation height and density of herbaceous vegetation in winter condition, but field reference data remains necessary for calibration.     

Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic

During the Global Rain Forest Mapping (GRFM) project, the JERS-1 SAR (Synthetic Aperture Radar) satellite acquired wall-to-wall image coverage of the humid tropical forests of the world. The rationale for the project was to demonstrate the application of spaceborne L-band radar in tropical forest studies. In particular, the use of orbital radar data for mapping land cover types, estimating the area of floodplains, and monitoring deforestation and forest regeneration were of primary importance. In this paper we examine the information content of the JERS-1 SAR data for mapping land cover types in the Amazon basin. More than 1500 high-resolution (12.5 m pixel spacing) images acquired during the low flood period of the Amazon river were resampled to 100 m resolution and mosaicked into a seamless image of about 8 million km², including the entire Amazon basin. This image was used in a classifier to generate a 1 km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first-order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages. First, a supervised maximum a posteriori Baysian approach classified the mean backscatter image into five general cover categories: terra firme forest (including secondary forest), savanna, inundated vegetation, open deforested areas and open water. A hierarchical decision rule based on texture measures was then applied to attempt further discrimination of known subcategories of vegetation types based on taxonomic information and woody biomass levels. True distributions of the general categories were identified from the RADAMBRASIL project vegetation maps and several field studies. Training and validation test sites were chosen from the JERS-1 image by consulting the RADAM vegetation maps. After several iterations and combining land cover types, 14 vegetation classes were successfully separated at the 1 km scale. The accuracy of the classification methodology was estimated to be 78% when using the validation sites. The results were also verified by comparison with the RADAM- and AVHRR-based 1 km resolution land cover maps.     

Land cover classification in SE Asia using near and short wave infrared bands

A land cover classification map is necessary for modelling interactions between the land surface and the atmosphere, monitoring the environment and estimating food production. In order to classify land cover in SE Asia in 2000, Normalized Difference Vegetation Index (NDVI), reflectance of near-infrared (NIR) band, and reflectance of short wave infrared (SWIR) band of Systeme pour l'Observation de la Terre (SPOT) VEGETATION data were used in this study. First, ground data were collected for training data. In addition, supervised classification was performed on twelve months of NDVI data. As a result, some deserts and peripheral sparse vegetative areas were classified into urban, compared with the world atlas. Secondly, the number of months when the reflectance of the SWIR band is higher than that of the NIR band was counted (SWIR>NIR month-count condition) in each pixel, and pixels with counts of 10 were classified as Sparse Herbaceous/Shrub and of 11 or 12 were classified as Bare Areas, respectively. Finally, land cover was classified based on the SWIR>NIR month-count condition combined with NDVI, and it was compared with the existing land cover map. It was found that the SWIR>NIR month-count condition gives a better result for areas of non- or sparsely vegetative classification than when using only NDVI.     

A land‐cover classification for landslide susceptibility mapping by using feature components

Classifying original bands and/or image components may cause unsatisfactory results in fields that have heterogeneous reflectance. In such cases, the demand for accurate land‐use, land‐cover, vegetation, and forestry information may require more specific components. The components should represent peculiar information collected from several inputs for target land covers. In this study, a new technique of land‐cover classification was explored to prepare an input which increases the success of landslide susceptibility mapping in a subtropical region, Asarsuyu Catchment Area (Duzce). Land‐cover mapping is a difficult issue in this area by only carrying out field studies and aerial‐photo interpretations. Moreover, applying different classifications of Landsat Thematic Mapper bands and/or their secondary products does not produce acceptable results. For this reason, vegetation indices, soil/surface moisture indices, topographic wetness index and drainage density were calculated to produce feature representative components for the land‐cover classification process. Results obtained from the proposed technique show that feature representative components significantly improve the conventional classification accuracy from 77% to 89% and the resultant land‐cover map is such a valuable input for landslide susceptibility mapping that it increases the success of the landslide susceptibility map from 63% to 88%.     

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.     

地形校正对叶面积指数遥感估算的影响

利用经过6S模型大气校正的地面反射率图像、数字地面高程数据以及改进的CIVCO地形校正模型,分别计算了褒河流域不同植被类型（阔叶林、针叶林和灌木林）的3类光谱植被指数（NDVI、SR和SAVI）,并建立了各个植被类型叶面积指数与同时相的各个植被指数的相关关系。结果表明,地形校正能有效地消除大部分的地形影响,显著地提高各植被指数与叶面积指数的相关关系;对于阴坡和阳坡来讲,阴坡较阳坡提高显著;对于不同的植被类型,针叶林和灌木较阔叶林提高较为显著;对于同一植被指数如SAVI,灌木提高较针叶林和阔叶林显著,说明地形校正对叶面积指数的遥感估算结果有很大的影响。因此在利用遥感数据定量估算叶面积指数时,尤其对于山区,不仅要进行地形校正,而且要针对不同的植被类型选择合适的植被指数进行估算。     

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.     

A new vegetation index derived from the pattern decomposition method applied to Landsat‐7/ETM+ images in Mongolia

The goal of this study was to estimate vegetation coverage and map the land‐cover in an experimental field (60×60 km) near Mandalgobi, Mongolia using Landsat‐7/ETM+ data for ground truthing in the Advanced Earth Observing Satellite II (ADEOS‐II) Mongolian Plateau Experiment (AMPEX). We measured soil moisture, vegetation coverage, and vegetation moisture in the field at 49 grid points around the time that the Aqua satellite passed over the area. We also surveyed the land‐cover in the field. Using ground‐based data and characteristics of spectral reflectance, we attempted to extract vegetation information from satellite data using the pattern decomposition method, which is a type of spectral mixture analysis. This method uses normalized spectral shapes as endmembers, which do not change between scenes. We defined an index using the pattern decomposition coefficients to analyse sparsely vegetated areas. The index showed a linear relationship with vegetation coverage. The vegetation coverage was estimated for the study site, and the average coverage at the site was 21.4%. Land‐cover types were classified using the index and the pattern decomposition coefficients; the kappa coefficient was 0.75. The index was useful for estimating vegetation coverage and land‐cover mapping for semiarid areas.     

Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling

Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30-0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.     

Measurements of land surface features using an airborne laser altimeter: the HAPEX-Sahel experiment

An airborne laser profiling altimeter was used to measure surface features and properties of the landscape during the HAPEX-Sahel Experiment in Niger, Africa in September 1992. The laser altimeter makes 4000 measurements per second with a vertical resolution of 5 cm. Airborne laser and detailed field measurements of vegetation heights had similar average heights and frequency distribution. Laser transects were used to estimate land surface topography, gully and channel morphology, and vegetation properties ( height, cover and distribution). Land surface changes related to soil erosion and channel development were measured. For 1 km laser transects over tiger bush communities, the maximum vegetation height was between 4-5 and 6-5 m, with an average height of 21 m. Distances between the centre of rows of tiger bush vegetation averaged 100 m. For two laser transects, ground cover for tiger bush was estimated to be 225 and 301 per cent for vegetation greater than 0-5m tall and 190 and 25-8 per cent for vegetation greater than 10m tall. These values are similar to published values for tiger bush. Vegetation cover for 14 and 18 km transects was estimated to be 4 per cent for vegetation greater than 0-5 m tall. These cover values agree within 1-2 per cent with published data for short transects (? 100 m) for the area. The laser altimeter provided quick and accurate measurements for evaluating changes in land surface features. Such information provides a basis for understanding land degradation and a basis for management plans to rehabilitate the landscape.     

Parameterizing the aerodynamic roughness length on a regional scale based on multi-source remote-sensing data

Aerodynamic roughness length (z₀) is one of those important biophysical parameters that influence energy exchange at the land–atmosphere interface, so it is significant to quantify the z₀ accurately. In this article, a scheme parameterizing land-surface z₀ at regional scale has been approached based on multi-resource remote-sensing data, including lidar and optical remote sensing. First, we retrieved the regional vegetation height from lidar data of Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud, and land Elevation Satellite (ICESat), and then the z₀ values of vegetated land surface were calculated using height data and canopy area index retrieved from remote-sensing data. Finally, the wall-to-wall map of z₀ in January and July 2008 were developed. The conclusions are as follows. (1) The vertical and horizontal structures of vegetation can be retrieved combining spaceborne lidar data and other optical remote-sensing data, so the vegetation characteristics and their intra-annual diversification of different land surfaces can be presented dynamically. The variation of z₀ with vegetation phenology can be quantified by modelling with vegetation height and multi-temporal leaf area index from multi-resource remote-sensing data. (2) The z₀ values of vegetated surface change significantly during leaf-on or leaf-off period in the year, but there are different features in the sparsely or densely vegetated surface. In the sparse vegetation areas, due to the relatively low leaf density in leaf-off season, the value of z₀ is also low. With the increase of leaf density in leaf-on season, the z₀ values will also increase. However, the relationship is complicated in the dense vegetation areas in leaf-on season; the z₀ values may or may not increase, but the zero-plane displacement heights will keep increasing continuously. This operational scheme to parameterize z₀ based on the vegetation height and canopy area index retrieved from multi-source remote-sensing data can be applied to quantify time serial z₀ at regional scale. Besides, it can also improve z₀ parameterization in land models or atmospheric models.     

Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics

Land surface vegetation phenology is an efficient bio-indicator for monitoring ecosystem variation in response to changes in climatic factors. The primary objective of the current article is to examine the utility of the daily MODIS 500 m reflectance anisotropy direct broadcast (DB) product for monitoring the evolution of vegetation phenological trends over selected crop, orchard, and forest regions. Although numerous model-fitted satellite data have been widely used to assess the spatio-temporal distribution of land surface phenological patterns to understand phenological process and phenomena, current efforts to investigate the details of phenological trends, especially for natural phenological variations that occur on short time scales, are less well served by remote sensing challenges and lack of anisotropy correction in satellite data sources. The daily MODIS 500 m reflectance anisotropy product is employed to retrieve daily vegetation indices (VI) of a 1 year period for an almond orchard in California and for a winter wheat field in northeast China, as well as a 2 year period for a deciduous forest region in New Hampshire, USA. Compared with the ground records from these regions, the VI trajectories derived from the cloud-free and atmospherically corrected MODIS Nadir BRDF (bidirectional reflectance distribution function) adjusted reflectance (NBAR) capture not only the detailed footprint and principal attributes of the phenological events (such as flowering and blooming) but also the substantial inter-annual variability. This study demonstrates the utility of the daily 500 m MODIS reflectance anisotropy DB product to provide daily VI for monitoring and detecting changes of the natural vegetation phenology as exemplified by study regions comprising winter wheat, almond trees, and deciduous forest.     

A predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km² (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km²; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D ², adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands.