Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil

Since 2000, the southwestern Brazilian Amazon has undergone a rapid transformation from natural vegetation and pastures to row-crop agricultural with the potential to affect regional biogeochemistry. The goals of this research are to assess wavelet algorithms applied to MODIS time series to determine expansion of row-crops and intensification of the number of crops grown. MODIS provides data from February 2000 to present, a period of agricultural expansion and intensification in the southwestern Brazilian Amazon. We have selected a study area near Comodoro, Mato Grosso because of the rapid growth of row-crop agriculture and availability of ground truth data of agricultural land-use history. We used a 90% power wavelet transform to create a wavelet-smoothed time series for five years of MODIS EVI data. From this wavelet-smoothed time series we determine characteristic phenology of single and double crops. We estimate that over 3200 km² were converted from native vegetation and pasture to row-crop agriculture from 2000 to 2005 in our study area encompassing 40,000 km². We observe an increase of 2000 km² of agricultural intensification, where areas of single crops were converted to double crops during the study period.     

Agricultural land-use change in Brazilian Amazônia between 1980 and 1995: Evidence from integrated satellite and census data

As cropland and pasture have replaced forest and cerrado in Brazilian Amazônia, concern has mounted over the effects of changing the biogeochemical and hydrological properties of one of the world's great storehouses of biomass and biodiversity. Although much recent effort has focused on the location, effects, and causes of deforestation and cerrado conversion, much less is known about the basin-wide spatial distribution and density of the land use following conversion for crops or pasture.In this paper, we use census and satellite records to develop maps of the distribution and abundance of major agricultural land uses across 4.5×10⁸ ha of Brazilian Amazônia in 1980 and 1995. Results indicate an overall expansion of 7.0×10⁶ ha in total agricultural area in Brazilian Amazônia between 1980 and 1995. The net change during this period is estimated for three different land-use types: croplands (an increase of 0.8×10⁶ ha), natural pastures (a decrease of 8.4×10⁶ ha), and planted pastures (an increase of 14.7×10⁶ ha). These estimates, the first spatially explicit quantifications of agricultural land-use activities in 1980 and 1995 across Brazilian Amazônia, are shown to be consistent with the results of applying a land use change and secondary regrowth model to published deforestation rates for the period.The resulting time slices, presented for each land-use category at 5-min (∼9 km) spatial resolution, allow for the quantification of land-use changes in this region for biogeochemical, demographic and economic models. Several foci of agricultural change existed within Brazilian Amazônia during this period: in the state of Pará, cropland was lost and planted pasture increased markedly; in Mato Grosso, both cropland and planted pasture increased; in Rondônia, planted pasture replacing forest was the primary route to agricultural expansion.     

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

Reliable estimation of evapotranspiration on agricultural fields predicted by the Priestley–Taylor model using soil moisture data from ground and remote sensing observations compared with the Common Land Model

Evapotranspiration (ET) is a crucial factor in understanding the hydrological cycle and is essential to many applications in hydrology, ecology and water resources management. However, reliable ET measurements and predictions for a range of temporal and spatial scales are difficult. This study focused on the comparison of ET estimates using a relatively simple model, the Priestley–Taylor (P-T) approach, and the physically based Common Land Model (CLM) using ground and remotely sensed soil moisture data as input. The results from both models were compared directly with hourly eddy covariance measurements at two agricultural field sites during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in the corn soybean production region in the Upper Midwest, USA. The P-T model showed a significant overestimation of the potential ET compared to the measurements, with a root mean square error (RMSE) between 115 and 130 W m^–2. Actual ET was better predicted by the CLM, with the RMSE ranging between 50 and 75 W m^–2. However, actual ET from the P-T model constrained with a soil moisture dependency parameterization showed improved results when compared to the measurements, with a significantly reduced bias and RMSE values between 60 and 65 W m^–2. This study suggests that even with a simple semi-empirical ET model, similar performance in estimating actual ET for agricultural crops compared to more complex land surface–atmosphere models (i.e. the CLM) can be achieved when constrained with the soil moisture function. This suggests that remote sensing soil moisture estimates from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and others such as the Soil Moisture and Ocean Salinity (SMOS) mission may be effective alternatives under certain environmental conditions for estimating actual ET of agricultural crops using a fairly simple algorithm.     

Crop classification modelling using remote sensing and environmental data in the Greater Platte River Basin,USA

With an ever expanding population, potential climate variability and an increasing demand for agriculture-based alternative fuels, accurate agricultural land-cover classification for specific crops and their spatial distributions are becoming critical to researchers, policymakers, land managers and farmers. It is important to ensure the sustainability of these and other land uses and to quantify the net impacts that certain management practices have on the environment. Although other quality crop classification products are often available, temporal and spatial coverage gaps can create complications for certain regional or time-specific applications. Our goal was to develop a model capable of classifying major crops in the Greater Platte River Basin (GPRB) for the post-2000 era to supplement existing crop classification products. This study identifies annual spatial distributions and area totals of corn, soybeans, wheat and other crops across the GPRB from 2000 to 2009. We developed a regression tree classification model based on 2.5 million training data points derived from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) in relation to a variety of other relevant input environmental variables. The primary input variables included the weekly 250 m US Geological Survey Earth Observing System Moderate Resolution Imaging Spectroradiometer normalized differential vegetation index, average long-term growing season temperature, average long-term growing season precipitation and yearly start of growing season. An overall model accuracy rating of 78% was achieved for a test sample of roughly 215 000 independent points that were withheld from model training. Ten 250 m resolution annual crop classification maps were produced and evaluated for the GPRB region, one for each year from 2000 to 2009. In addition to the model accuracy assessment, our validation focused on spatial distribution and county-level crop area totals in comparison with the NASS CDL and county statistics from the US Department of Agriculture (USDA) Census of Agriculture. The results showed that our model produced crop classification maps that closely resembled the spatial distribution trends observed in the NASS CDL and exhibited a close linear agreement with county-by-county crop area totals from USDA census data (R ² = 0.90).     

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.     

Evolutionary bi-objective optimization of a semi-arid vegetation dynamics model with NDVI and σ satellite data

Satellite radar backscattering coefficient σ⁰ data from ENVISAT-ASAR and Normalized Difference Vegetation Index (NDVI) data from SPOT-VEGETATION are assimilated in the STEP model of vegetation dynamics. The STEP model is coupled with a radiative transfer model of the radar backscattering and NDVI signatures of the soil and herbaceous vegetation. These models are driven by field data (rainfall time series, soil properties, etc.). While some model parameters have fixed values, some other parameters have target values to be optimized. The study focuses on a well documented 1 km² homogeneous area in a semi-arid region (Gourma, Mali).We here investigate whether departures between model predictions and the corresponding data result from field data errors, in situ data lack of representativeness or some model shortcomings. For this purpose we introduce an evolutionary strategy (ES) approach relying on a bi-objective function to be minimized in the data assimilation/inversion process. Several numerical experiments are conducted, in various mono-objective and bi-objective modes, and the performances of the model predictions compared in terms of NDVI, backscattering coefficient, leaf area index (LAI) and biomass.It is shown that the bi-objective ES leads to improved model predictions and also to a better readability of the results by exploring the Pareto front of optimal and admissible solutions. It is also shown that the information brought from the optical sensor and the radar is coherent; that the corresponding radiative transfer models are also coherent; that the representativeness of in situ data can be compared to satellite data through the modeling process. However some systematic biases on the biomass predictions (errors in the range 140 to 300 kg ha^− 1) are observed. Thanks to the bi-objective ES, we are able to identify some likely shortcoming in the vegetation dynamics model relating the LAI to the biomass variables.     

Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories

According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3 ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m³ ha^− 1) and aboveground biomass (t ha^− 1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15 m × 15 m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250 m × 250 m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.     

Confidence in soil carbon predictions undermined by the uncertainties in observations and model parameterisation

Soil carbon (C) responds quickly and feedbacks significantly to environmental changes such as climate warming and agricultural management. Soil C modelling is the only reasonable approach available for predicting soil C dynamics under future conditions of environmental changes, and soil C models are usually constrained by the average of observations. However, model constraining is sensitive to the observed data, and the consequence of using observed averages on C predictions has rarely been studied. Using long-term soil organic C datasets from an agricultural field experiment, we constrained a process-based model using the average of observations or by taking into account the variation in observations to predict soil C dynamics. We found that uncertainties in soil C predictions were masked if ignoring the uncertainties in observations (i.e., using the average of observations to constrain model), if uncertainties in model parameterisation were not explicitly quantified. However, if uncertainties in model parameterisation had been considered, further considering uncertainties in observations had negligible effect on uncertainties in SOC predictions. The results suggest that uncertainties induced by model parameterisation are larger than that induced by observations. Precise observations representing the real spatial pattern of SOC at the studied domain, and model structure improvement and constrained space of parameters will benefit reducing uncertainties in soil C predictions. The results also highlight some areas on which future C model development and software implementations should focus to reliably infer soil C dynamics.     

How a Pareto frontier complements scenario projections in land use change impact assessment

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m³ ethanol, with GHG emissions of 40 kg CO₂-eq/m³ ethanol. The Pareto frontier indicates an improvement potential of ∼50 $/m³ ethanol when keeping emissions fixed, or ∼250 kg CO₂-eq/m³ ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design.     

Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks

Sudden changes in weather, in particular extreme temperatures, can result in increased energy expenditures, depleted agricultural resources, and even loss of life. However, these ill effects can be reduced with accurate air temperature predictions that provide adequate advance warning. Support vector regression (SVR) was applied to meteorological data collected across the state of Georgia in order to produce short-term air temperature predictions. A method was proposed for reducing the number of training patterns of massively large data sets that does not require lengthy pre-processing of the data. This method was demonstrated on two large data sets: one containing 300,000 cold-weather training patterns collected during the winter months and one containing 1.25 million training patterns collected throughout the year. These patterns were used to produce predictions from 1 to 12 h ahead. The mean absolute error (MAE) for the evaluation set of winter-only patterns ranged from 0.514°C for the 1-h prediction horizon to 2.303°C for the 12-h prediction horizon. For the evaluation set of year-round patterns, the MAE ranged from 0.513°C for the 1-h prediction horizon to 1.922°C for the 12-h prediction horizon. These results were competitive with previously developed artificial neural network (ANN) models that were trained on the full data sets. For the winter-only evaluation data, the SVR models were slightly more accurate than the ANN models for all twelve of the prediction horizons. For the year-round evaluation data, the SVR models were slightly more accurate than the ANN models for three of the twelve prediction horizons.     

A model for European Biogenic Volatile Organic Compound emissions: Software development and first validation

A grid-oriented Biogenic Emission Model (BEM) has been developed to calculate Non-Methane Volatile Organic Compound (NMVOC) emissions from vegetation in high spatial and temporal resolutions. The model allows the emissions calculation for any modeling domain covering Europe on the basis of: 1) the U.S. Geological Survey 1-km resolution land-use database, 2) a land-use specific, monthly isoprene, monoterpene and Other Volatile Organic Compound (OVOC) emission potentials and foliar biomass densities database, 3) temperature and solar radiation data provided by the mesoscale meteorological model MM5. The model was applied for Europe in 30-km spatial resolution for the year 2003. The European total emissions for 2003 consist of 33.0% isoprene, 25.5% monoterpenes and 41.5% OVOC. BEM results are compared with those from the well-documented global Model of Emissions of Gases and Aerosols from Nature (MEGAN). The BEM total emissions compare well with the MEGAN ones. In July 2003, the results of both models agree within a factor of 1.2 for total isoprene emissions and within a factor of 2 for total monoterpene emissions. The comparison of the spatial distributions of the July 2003 isoprene and monoterpene emissions calculated with BEM and MEGAN shows that, in the greater part of the study area, the differences are below the current uncertainty limit for the estimation of spatially-resolved biogenic VOC emissions in Europe being equal to about ±600 kg km^?2 month^?1. Differences that are above this limit are found mainly in the eastern European countries for isoprene and in the Mediterranean countries for monoterpenes.     

Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle.The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression.Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n = 31 circular nested sample plots of 154 m² for trees and shrubs and 1 m² for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs.Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions.The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE = 1.50 t/ha, SE% = 9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 t/ha with a root mean square error RMSE = 6.42 t/ha, RMSE_r = 44%) resulting from leave-one-out cross-validation. The k-NN-approach allows mapping and analysis of the spatial variability of AGC. The results show high spatial variability with AGC predictions ranging from 4.3 t/ha to 28.8 t/ha, reflecting the highly heterogeneous conditions in those permafrost-influenced landscapes. The means and totals of linear regression and k-NN predictions revealed only small differences but some regional distinctions were recognized in the maps.     

Hybrid modeling of spatial continuity for application to numerical inverse problems

A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km² Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km² fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km² transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.     

Assessing the spatio-temporal rates and patterns of land-use and land-cover changes in the Cerrados of southeastern Mato Grosso,Brazil

The Cerrados of central Brazil have undergone profound landscape transformation in recent decades due to agricultural expansion, and this remains poorly assessed. The present research investigates the spatial-temporal rates and patterns of land-use and land-cover (LULC) changes in one of the main areas of agricultural production in Mato Grosso State (Brazil), the region of Primavera do Leste. To quantify the different aspects of LULC changes (e.g. rates, types, and spatial patterns) in this region, we applied a post-classification change detection method, complemented with landscape metrics, for three dates (1985, 1995, and 2005). LULC maps were obtained from an object-based classification approach, using the nearest neighbour (NN) classifier and a multi-source data set for image object classification (e.g. seasonal Thematic Mapper (TM) bands, digital elevation model (DEM), and a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived index), strategically chosen to increase class separability. The results provided an improved mapping of the Cerrados natural vegetation conversion into crops and pasture once auxiliary data were incorporated into the classification data set. Moreover, image segmentation was crucial for LULC map quality, in particular because of crop size and shape. The changes detected point towards increasing loss and fragmentation of natural vegetation and high rates of crop expansion. Between 1985 and 2005, approximately 42% (6491 km²) of Cerrados in the study area were converted to agricultural land uses. In addition, it was verified that cultivated areas are encroaching into fragile environments such as wetlands, which indicates the intense pressure of agricultural expansion on the environment.     

Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia

Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible–near infrared (vis–NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis–NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha⁻¹ y⁻¹ (95% confidence intervals of 1.78 and 1.93 t ha⁻¹ y⁻¹), equivalent to a total of 1242 × 10⁶ tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 10⁶ y⁻¹). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed.     

A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semiaquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km², or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements.     

An assessment of land-use change in the Cocoa Belt of south-west Nigeria

The Nigerian government is reviving the agricultural sector to shift from its sole dependence on crude oil for foreign exchange earnings. Thus, the Cocoa Belt (agro-ecological region) of southwest Nigeria is important to the national economy. With the increasing demand for land to grow export crops and to meet other needs such as settlement expansion, land use is changing. Land-use data and mapping are essential inputs for the process of formulating, implementing, and monitoring policy with the aim of reducing the impact of land-cover/land-use (LCLU) change. Land-use types, their spatial extent and dynamics over a 25 year period are examined from multispectral images of the Landsat Thematic Mapper and Enhanced Thematic Mapper Plus. This study examines the main drivers of LCLU change and the environmental impact. Results show that forest conversion to agricultural lands is the main trend, and cultivation is the main cause of forest loss in the study area. The need to produce food for the teeming population, coupled with the government's policy to expand export crop production is resulting in the loss of native forest, including areas designated as forest reserves. Results underscore the need for deliberate land-use planning and management in this belt. This study reveals the situation of unplanned and rapid changes to land use in the context of a developing country where explicit policies to cater for such activities are absent.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.