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See detailAn easy and low-cost method for preprocessing and matching small-scale amateur aerial photography for assessing agricultural land use in Burkina Faso
Wellens, Joost ULg; Midekor, Akoly; Traore, Farid ULg et al

in International Journal of Applied Earth Observation and Geoinformation (2013), 23

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See detailAssessing urbanisation effects on rainfall-runoff using a remote sensing supported modelling strategy
Verbeiren, Boud; Van de Voorde, Tim; Canters, Frank et al

in International Journal of Applied Earth Observation and Geoinformation (2013), 21

This paper aims at developing a methodology for assessing urban dynamics in urban catchments and the related impact on hydrology. Using a multi-temporal remote sensing supported hydrological modelling ... [more ▼]

This paper aims at developing a methodology for assessing urban dynamics in urban catchments and the related impact on hydrology. Using a multi-temporal remote sensing supported hydrological modelling approach an improved simulation of runoff for urban areas is targeted. A time-series of five medium resolution urban masks and corresponding sub-pixel sealed surface proportions maps was generated from Landsat and SPOT imagery. The consistency of the urban mask and sealed surface proportion timeseries was imposed through an urban change trajectory analysis. The physically based rainfall-runoff model WetSpa was successfully adapted for integration of remote sensing derived information of detailed urban land use and sealed surface characteristics. A first scenario compares the original land-use class based approach for hydrological parameterisation with a remote sensing sub-pixel based approach. A second scenario assesses the impact of urban growth on hydrology. Study area is the Tolka River basin in Dublin, Ireland. The grid-based approach of WetSpa enables an optimal use of the spatially distributed properties of remote sensing derived input. Though change trajectory analysis remains little used in urban studies it is shown to be of utmost importance in case of time series analysis. The analysis enabled to assign a rational trajectory to 99% of all pixels. The study showed that consistent remote sensing derived land-use maps are preferred over alternative sources (such as CORINE) to avoid over-estimation errors, interpretation inconsistencies and assure enough spatial detail for urban studies. Scenario 1 reveals that both the class and remote sensing sub-pixel based approaches are able to simulate discharges at the catchment outlet in an equally satisfactory way, but the sub-pixel approach yields considerably higher peak discharges. The result confirms the importance of detailed information on the sealed surface proportion for hydrological simulations in urbanised catchments. In addition a major advantage with respect to hydrological parameterisation using remote sensing is the fact that it is site- and period-specific. Regarding the assessment of the impact of urbanisation (scenario 2) the hydrological simulations revealed that the steady urban growth in the Tolka basin between 1988 and 2006 had a considerable impact on peak discharges. Additionally, the hydrological response is quicker as a result of urbanisation. Spatially distributed surface runoff maps identify the zones with high runoff production. It is evident that this type of information is important for urban water management and decision makers. The results of the remote sensing supported modelling approach do not only indicate increased volumes due to urbanisation, but also identifies the locations where the most relevant impacts took place. [less ▲]

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See detailEstimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data
Kouadio, Amani Louis ULg; Duveiller, Gregory; Djaby, Bakary ULg et al

in International Journal of Applied Earth Observation and Geoinformation (2012), 18

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation ... [more ▼]

Earth observation data, owing to their synoptic, timely and repetitive coverage, have been recognized as a valuable tool for crop monitoring at different levels. At the field level, the close correlation between green leaf area (GLA) during maturation and grain yield in wheat revealed that the onset and rate of senescence appeared to be important factors for determining wheat grain yield. Our study sought to explore a simple approach for wheat yield forecasting at the regional level, based on metrics derived from the senescence phase of the green area index (GAI) retrieved from remote sensing data. This study took advantage of recent methodological improvements in which imagery with high revisit frequency but coarse spatial resolution can be exploited to derive crop-specific GAI time series by selecting pixels whose ground-projected instantaneous field of view is dominated by the target crop: winter wheat. A logistic function was used to characterize the GAI senescence phase and derive the metrics of this phase. Four regression-based models involving these metrics (i.e., the maximum GAI value, the senescence date and the thermal time taken to reach 50% of the green surface in the senescent phase) were related to official wheat yield data. The performances of such models at this regional scale showed that final yield could be estimated with an RMSE of 0.57 ton ha−1, representing about 7% as relative RMSE. Such an approach may be considered as a first yield estimate that could be performed in order to provide better integrated yield assessments in operational systems. [less ▲]

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See detailSoil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Bartholomeus, Harm; Kooistra, Lammert; Stevens, Antoine et al

in International Journal of Applied Earth Observation and Geoinformation (2011), 13(1), 81-88

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping ... [more ▼]

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation. [less ▲]

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See detailEmpirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco
BALAGHI, Riad; Tychon, Bernard ULg; EERENS, Herman et al

in International Journal of Applied Earth Observation and Geoinformation (2008), 10

In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to ... [more ▼]

In Morocco, no operational system actually exists for the early prediction of the grain yields of wheat (Triticum aestivum L.). This study proposes empirical ordinary least squares regression models to forecast the yields at provincial and national levels. The predictions were based on dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The Global Land Cover raster map (GLC2000) was used to select only the NDVI pixels that are related to agricultural land. Provincial wheat yields were assessed with errors varying from 80 to 762 kg ha 1, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 73 kg ha 1 error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84 kg ha 1 error, at least 1 month before harvest. At the provincial and national levels, most of the yield variation was accounted for by NDVI. The proposed models can be used in an operational context to early forecast wheat yields in Morocco. [less ▲]

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