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.     

A digital rock density map of New Zealand

Digital geological maps of New Zealand (QMAP) are combined with 9256 samples with rock density measurements from the national rock catalogue PETLAB and supplementary geological sources to generate a first digital density model of New Zealand. This digital density model will be used to compile a new geoid model for New Zealand. The geological map GIS dataset contains 123 unique main rock types spread over more than 1800 mapping units. Through these main rock types, rock densities from measurements in the PETLAB database and other sources have been assigned to geological mapping units. A mean surface rock density of 2440 kg/m³ for New Zealand is obtained from the analysis of the derived digital density model. The lower North Island mean of 2336 kg/m³ reflects the predominance of relatively young, weakly consolidated sedimentary rock, tephra, and ignimbrite compared to the South Island’s 2514 kg/m³ mean where igneous intrusions and metamorphosed sedimentary rocks including schist and gneiss are more common. All of these values are significantly lower than the mean density of the upper continental crust that is commonly adopted in geological, geophysical, and geodetic applications (2670 kg/m³) and typically attributed to the crystalline and granitic rock formations. The lighter density has implications for the calculation of the geoid surface and gravimetric reductions through New Zealand.     

Predicting the spectral information of future land cover using machine learning

Application of machine learning models to study land-cover change is typically restricted to the change detection of categorical, i.e. classified, land-cover data. In this study, our aim is to extend the utility of such models to predict the spectral band information of satellite images. A Random Forests (RF)-based machine learning model is trained using topographic and historical climatic variables as inputs to predict the spectral band values of high-resolution satellite imagery across two large sites in the western United States, New Mexico (10,570 km²), and Washington (9400 km²). The model output is used to obtain a true colour photorealistic image and an image showing the normalized difference vegetation index values. We then use the trained model to explore what the land cover might look like for a climate change scenario during the 2061–2080 period. The RF model achieves high validation accuracy for both sites during the training phase, with the coefficient of determination (R²) = 0.79 for New Mexico site and R² = 0.73 for Washington site. For the climate change scenario, prominent land-cover changes are characterized by an increase in the vegetation cover at the New Mexico site and a decrease in the perennial snow cover at the Washington site. Our results suggest that direct prediction of spectral band information is highly beneficial due to the ability it provides for deriving ecologically relevant products, which can be used to analyse land-cover change scenarios from multiple perspectives.     

Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey

We used a single-beam, first return profiling LIDAR (Light Detection and Ranging) measurements of canopy height, intensive biometric measurements in plots, and Forest Inventory and Analysis (FIA) data to quantify forest structure and ladder fuels (defined as vertical fuel continuity between the understory and canopy) in the New Jersey Pinelands. The LIDAR data were recorded at 400 Hz over three intensive areas of 1 km² where transects were spaced at 200 m, and along 64 transects spaced 1 km apart (total of ca. 2500 km²). LIDAR and field measurements of canopy height were similar in the three intensive study areas, with the 80th percentile of LIDAR returns explaining the greatest amount of variability (79%). Correlations between LIDAR data and aboveground tree biomass measured in the field were highly significant when all three 1 km² areas were analyzed collectively, with the 80th percentile again explaining the greatest amount of variability (74%). However, when intensive areas were analyzed separately, correlations were poor for Oak/Pine and Pine/Scrub Oak stands. Similar results were obtained using FIA data; at the landscape scale, mean canopy height was positively correlated with aboveground tree biomass, but when forest types were analyzed separately, correlations were significant only for some wetland forests (Pitch Pine lowlands and mixed hardwoods; r² = 0.74 and 0.59, respectively), and correlations were poor for upland forests (Oak/Pine, Pine/Oak and Pine/Scrub Oak, r² = 0.33, 0.11 and 0.21, respectively). When LIDAR data were binned into 1-m height classes, more LIDAR pulses were recorded from the lowest height classes in stands with greater shrub biomass, and significant differences were detected between stands where recent prescribed fire treatments had been conducted and unburned areas. Our research indicates that single-beam LIDAR can be used for regional-scale (forest biomass) estimates, but that relationships between height and biomass can be poorer at finer scales within individual forest types. Binned data are useful for estimating the presence of ladder fuels (vertical continuity of leaves and branches) and horizontal fuel continuity below the canopy.     

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 changes of urban vegetation cover and aboveground carbon stocks using LiDAR and Landsat imagery data in Auckland,New Zealand

ABSTRACT

Urban vegetation can help to offset carbon emissions. However, urban vegetation cover is vulnerable to urbanization. This study attempts to detect the change in vegetation cover and to quantify its impact on aboveground carbon (AGC) stocks in Auckland, New Zealand, between 1989 and 2014. Field-measured vegetation parameters were used to calculate the amount of carbon stored in plants at the plot-level. Plot-level AGC stocks were linked with vegetation spectral/structural features derived from Landsat images and Light Detection and Ranging (LiDAR) data. These data were also used to map vegetation cover and to estimate AGC stock. Vegetation cover decreased from 394.0 km² in 1989 to 379.4 km² in 2014. AGC stock in 1989 was estimated at 1,001,184 Mg C from Landsat 4 data. The total AGC in 2014 was estimated at 1,459,530 Mg C from Landsat 8 data. Thus, total AGC stock increased by 458,346 Mg C (45.8%) in spite of a 3.7% decrease in vegetation cover (14.6 km²) during the same period. The increase in AGC stock was derived partly from tree growth and tree plantings. Vegetation growth contributed more to the increase in AGC stock than its gain from non-vegetation to vegetation changes. The AGC stored in trees and shrubs estimated at 1,333,011 Mg C from the 2014 Landsat data is 5.7% lower than 1,414,607 Mg C estimated from the 2013 LiDAR data, due to the inability of optical imagery to capture the sub-canopy structure of forests and the saturation effect. Thus, LiDAR data provided a more accurate estimate of AGC stock, especially when the stock density is high (e.g. >97.9 Mg C ha^–1).     

Comparative analysis of methods for determining the metabolic rate in order to provide a balance between man and the environment

The incorrect determination of metabolic rate can be linked to discrepancies between the model of the PMV (Predicted Mean Vote) and real thermal sensation collected in field studies. Aiming to improve the correlation of the PMV model and the real thermal sensation, this work established new values for the metabolic rate: one way being called “calculated” using Newton's Method and the other called "measured" using a metabolic analyzer. Welder's activities were evaluated, through the measurements of environmental and personal variables. New values of metabolic rate were determined for this activity. The values found for the calculated form and the measured one were, respectively, 178.63 and 145.46 W/m², different from the range provided by the table of ISO 8996 (2004) for this activity (75–125 W/m²). In order to verify which of the values of the metabolic rate was closer to the real thermal sensation of PMV, a linear regression was made between the PMV and the real thermal sensation in three ways: S × PMV_tabulated (R² = 0.1749), S × PMV_calculated (R ² = 0.7481) and S × PMV_measured (R² = 0.7854). It was found that the values measured by the instrument gave a higher coefficient of determination which was chosen for the correction of the table. The correction of the table provides a value of M_predicted, that is a value of metabolic rate that corrects the values provided by the tables of ISO 8996 (2004), by means of a correction coefficient. For the welder's activities in a metal-mechanics industry, tabulated values can be multiplied by the correction coefficient 1.4648 in order to minimize inaccuracies. The PMV_predicted, obtained through the M_predicted, when related to the actual thermal sensation, provides a coefficient of determination of 0.7511, thereby improving the model of the PMV.     

Evaluation of a non-point source pollution model,AnnAGNPS, in a tropical watershed

Impaired water quality caused by human activity and the spread of invasive plant and animal species has been identified as a major factor of degradation of coastal ecosystems in the tropics. The main goal of this study was to evaluate the performance of AnnAGNPS (Annualized Non-Point Source Pollution Model), in simulating runoff and soil erosion in a 48 km² watershed located on the Island of Kauai, Hawaii. The model was calibrated and validated using 2 years of observed stream flow and sediment load data. Alternative scenarios of spatial rainfall distribution and canopy interception were evaluated. Monthly runoff volumes predicted by AnnAGNPS compared well with the measured data (R² = 0.90, P < 0.05); however, up to 60% difference between the actual and simulated runoff were observed during the driest months (May and July). Prediction of daily runoff was less accurate (R² = 0.55, P < 0.05). Predicted and observed sediment yield on a daily basis was poorly correlated (R² = 0.5, P < 0.05). For the events of small magnitude, the model generally overestimated sediment yield, while the opposite was true for larger events. Total monthly sediment yield varied within 50% of the observed values, except for May 2004. Among the input parameters the model was most sensitive to the values of ground residue cover and canopy cover. It was found that approximately one third of the watershed area had low sediment yield (0–1 t ha⁻¹ y⁻¹), and presented limited erosion threat. However, 5% of the area had sediment yields in excess of 5 t ha⁻¹ y⁻¹. Overall, the model performed reasonably well, and it can be used as a management tool on tropical watersheds to estimate and compare sediment loads, and identify “hot spots” on the landscape.     

Towards refined estimation of vegetation carbon stock in Auckland,New Zealand using WorldView-2 and LiDAR data: the impact of scaling

ABSTRACT

It is necessary to estimate carbon (C) stored in urban vegetation for the purpose of carbon accounting and trading. This study aims to develop a refined method for reliably estimating above-ground carbon (AGC) stock of urban vegetation from integrated WorldView-2 imagery and Light Detection And Ranging (LiDAR) data in Auckland, New Zealand. Also assessed in this study is the impact of image resolution on regional AGC estimates by vegetation type. The integration of WorldView-2 imagery with a 2-m digital surface model produced from LiDAR data enables urban vegetation to be mapped into trees (101.5 km²), shrubs (64.9 km²), and grasses (172.2 km²) at a producer’s accuracy over 95.9%. The AGC stock of trees, shrubs and grasses is estimated at 1,134,287, 207,606, and 127,427 Mg C, respectively, from the vegetation map. Overall, the total AGC of all types of vegetation does not vary significantly with image spatial resolution over the range of 5 to 30 m if estimated using the same model. This is because high AGC densities are generalised at a coarser resolution, but the larger pixel size compensates for the decrease. Although the spatial resolution does not affect the most significant spectral predicators of plot-level AGC noticeably, it has an obvious effect on both model accuracy and complexity. Thus, the impact of image resolution on AGC would be pronounced if it were estimated using different models that were the best at a given resolution. Of the three vegetation types, the AGC of shrubs is the most variable with spatial resolution, followed by trees. Thus, the AGC of relatively small but more spatially fragmented vegetation parcels is more susceptible to change in image spatial resolution. The estimation model based on spectral features of vegetation has the lowest room-mean-square-error at 15 m. More research is needed to confirm whether it is true in other natural environments in future studies.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

Efficient and exact quantum compression

We present a divide and conquer based algorithm for optimal quantum compression/decompression, using O(n(log⁴n)log log n) elementary quantum operations. Our result provides the first quasi-linear time algorithm for asymptotically optimal (in size and fidelity) quantum compression and decompression. We also outline the quantum gate array model to bring about this compression in a quantum computer. Our method uses various classical algorithmic tools to significantly improve the bound from the previous best known bound of O(n³) for this operation.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.     

All-terrain vehicle use in agriculture: Exposure to whole body vibration and mechanical shock

Whole body vibration (WBV) and mechanical shock were measured in 12 New Zealand farmers during their daily use of all-terrain vehicles (ATVs). As per the International Organization for Standardization (ISO) guidelines for WBV exposure, frequencies between 0 and 100 Hz were recorded via a seat-pad tri-axial accelerometer during 20 min of ATV use. The farmers were also surveyed to estimate seasonal variation in daily ATV usage as well as 7-day and 12-month prevalence of spinal pain. Frequency-weighted vibration exposure and total riding time were calculated to determine the daily vibration dose value (VDV). The daily VDV of 16.6 m/s^1.75 was in excess of the 9.1 m/s^1.75 action limit set by ISO guidelines suggesting an increased risk of low back injury from such exposure. However, the mean shock factor R, representing cumulative adverse health effects, was 0.31 indicating that these farmers were not exposed to excessive doses of mechanical shock. Extrapolation of daily VDV data to estimated seasonal variations of farmers in ATV riding time demonstrated that all participants would exceed the ISO recommended maximum permissible limits during the spring lambing season, as compared to lower exposures calculated for summer, autumn and winter. Low back pain was the most commonly reported complaint for both 7 day (50%) and 12 month prevalence (67%), followed by the neck (17% and 42%) and the upper back (17% and 25%) respectively. The results demonstrate high levels of vibration exposure within New Zealand farmers and practical recommendations are needed to reduce their exposure to WBV.     

MGB-IPH model for hydrological and hydraulic simulation of large floodplain river systems coupled with open source GIS

Large-scale hydrological models are useful tools for water resources studies, however, river network flow routing is generally represented using simplified methods, which may lead to simulation errors in flat regions. We present recent improvements to the large-scale hydrological model MGB-IPH to improve its capability of simulating large river basins with extensive floodplains. We also describe the coupling of MGB-IPH to an open source GIS and a large set of developed pre-processing tools with a user-friendly interface for remote sensing data preparation and output visualization. The new features implemented are demonstrated applying the model to the whole Araguaia river basin (380,000 km²). Results are compared to the previous MGB-IPH routing method, observed flow and water level data and remote sensing imagery, showing improvement in the representation of floodplain inundation dynamics. The test case also shows that the proposed model software framework amplifies possibilities of large-scale simulation of ungauged basins.     

Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery

This paper presents the methodology used to detect temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta (VMD) based on MODIS time-series imagery (Wavelet-based Filter for detecting spatio-temporal changes in Flood Inundation; WFFI). This methodology involves the use of a wavelet-based filter to interpolate missing information and reduce the noise component in the time-series data, as proposed in a previous study. The smoothed time profiles of Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), and the Difference Value between EVI and LSWI (DVEL) are obtained from MOD09 8-day composite time-series data (resolution: 500 m; time period: 2000-2005). The proposed algorithm was applied to produce time-series inundation maps (WFFI products) for the five annual flood seasons over the period from 2000 to 2004. The WFFI products were validated via comparisons with Landsat-derived results and inundation maps based on RADARSAT images, hydrological data, and digital elevation model data. Compared with the RADARSAT-derived inundation maps at the province level, the obtained RMSE range from 364 to 443 km² and the determination coefficients [R²] range from 0.89 to 0.92. Compared with Landsat-derived results at the 10-km grid level, the obtained RMSE range from 6.8 to 15.2 km² and the determination coefficients [R²] range from 0.77 to 0.97. The inundated area of flooded forests/marsh to the northeast of Tonle Sap Lake were underestimated, probably because of extensive vegetation cover in this area. The spatial characteristics of the estimated start dates, end dates, and duration of inundation cycles were also determined for the period from 2000 to 2004. There are clear contrasts in the distribution of the estimated end dates and duration of inundation cycles between large-scale floods (2000-2002) and medium- and small-scale floods (2003 and 2004). At the regional scale, the estimated start dates for the southern part of An Giang Province during 2003 and 2004 was distinctly later than that for surrounding areas. The results indicate that these triple-cropping areas enclosed by dikes increased in extent from 2003 to 2004. In contrast, the estimated end dates of inundation at the Co Do and Song Hau State Farms were clearly earlier than those for surrounding areas, although the estimated start dates were similar. Temporal changes in the inundation area of Flood pixels in the Dong Thap and Long An Provinces are in excellent agreement with daily water-level data recorded at Tan Chau Station. The estimated area of Long-term water body increased in size from 2000 to 2004, especially in coastal areas of the Ca Mau and Bac Lieu Provinces. Statistical data for Vietnam indicate that this trend may reflect the expansion of shrimp-farming areas. The WFFI products enable an understanding of seasonal and annual changes in the water distribution and environment of the Cambodia and the VMD from a global viewpoint.     

Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km² area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r² = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r² = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.     

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.     

Water resources of Cyprus under changing climatic conditions: Modelling approach,validation and limitations

We examine trends in the water resources of Cyprus by focussing on water flux changes in the important Kouris catchment. Our modelling approach is general and is a synthesis of an adapted conceptual daily rainfall-runoff model, radiation transfer models that use high resolution MODIS satellite climatological data and GCM scenarios for future climatic change. We used climatic data as input to our models, downscaled to the catchment resolution from two climate scenarios: the mild RCP2.6 and the extreme RCP8.5, to estimate water resources by the end of the 21st century. The models show that the present mean annual rainfall resource of 174 Mm³ will be reduced to 162 Mm³ and 132 Mm³, for the mild and extreme scenario, respectively. The present mean discharge of 21.5 Mm³ into the Kouris dam from the catchment will decrease to 16.6 Mm³ and 6.9 Mm³ under the mild and extreme scenario, respectively.     

Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices

This study reports results of a classification tree approach to mapping the wetlands of the Congo Basin, focusing on the Cuvette Centrale of the Congo River watershed, an area of 1,176,000 km². Regional expert knowledge was used to train passive optical remotely sensed imagery of the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors, JERS-1 active radar L-band imagery, and topographical indices derived from 3 arc sec elevation data of the Shuttle Radar Topography Mission (SRTM). All data inputs were resampled to a common 57 m resolution grid. A classification tree bagging procedure was employed to produce a final map of per-grid cell wetland probability. Thirty bagged trees were ranked and the median result was selected to produce the final wetland probability map. Thresholding the probability map at < 0.5 yielded a proportion of wetland cover for the study area of 32%, equivalent to 360,000 km². Wetlands predominate in the CARPE Lake Tele-Lake Tumba landscape located in the western part of the Democratic Republic of the Congo and the south-eastern Republic of Congo, where they constitute 56% of the landscape. Local topography depicting relative elevation for sub-catchments proved to be the most valuable discriminator of wetland cover. However, all sources of information (i.e. optical, radar and topography) featured prominently in contributing to the classification tree procedure, reinforcing the idea that multi-source data are useful in the characterization of wetland land cover. The method employed freely available data and a fully automated process, except for training data collection. Comparisons to existing maps and in situ field observations indicate improvements compared to previous efforts.