François Kayitakire

Mapping global cropland and field size

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale

Mapping cropland areas is of great interest in diverse fields, from crop monitoring to climate change and food security. Recognizing the value of a reliable and harmonized crop mask that entirely covers the African continent, the objectives of this study were to (i) consolidate the best existing land cover/land use datasets, (ii) adapt the Land Cover Classification System (LCCS) for harmonization, (iii) assess the final product, and (iv) compare the final product with two existing datasets. Ten datasets were compared and combined through an expert-based approach in order to create the derived map of cropland areas at 250 m covering the whole of Africa. The resulting cropland mask was compared with two recent cropland extent maps at 1 km: one derived from MODIS and one derived from five existing products. The accuracy of the three products was assessed against a validation sample of 3,591 pixels of 1km regularly distributed over Africa and interpreted using high resolution images, which were collected using the Geo-Wiki tool. The comparison of the resulting crop mask with existing products shows that it has a greater agreement with the expert validation dataset, in particular for places where the cropland represents more than 30% of the area of the validation pixel.

The Need for Improved Maps of Global Cropland

Food security is a key global concern. By 2050, the global population will exceed 9 billion, and a 50% increase in annual agricultural output will be required to keep up with demand. There are significant additional pressures on existing agricultural land through increased competition from the biofuel sector and the need to elevate feed production, which is being driven by higher levels of meat consumption in low- and middle-income countries.

A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa

Monitoring vegetation conditions is a critical activity for assessing food security in the Horn of Africa. Remote sensing from space offers a unique opportunity to obtain consistent and timely information over large and often inaccessible areas where field observations are scattered, non-homogenous, or frequently unavailable. In this study we outline a method to assess objectively the performance and characteristics of seasonal vegetation development solely on the basis of time series of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from Satellite Pour l’Observation de la Terre SPOT-VEGETATION (SPOT-VGT) imagery. Key phenological indicators such as the start and end of growing periods are derived from a statistical analysis of the time series to characterize the spatial and temporal evolution of successive seasons. These indicators are then utilized to compute a proxy of the seasonal gross primary production (GPP) as the cumulative FAPAR during the growing season. Vegetation condition and associated risk of food deficit for specific seasons and locations are finally derived from the comparison of the seasonal GPP proxy with its average value computed over the whole time series. The impact on vegetation of the severe drought experienced by the Horn of Africa between late 2010 and late 2011 is discussed.

Discrimination automatique de peuplements forestiers à partir d'orthophotos numériques couleur: un cas d'étude en Belgique

Orthophotos are often used to establish forest maps by visual interpretation and manual stand delineation. A classification method of forest stands using digitized colour orthophotos with very high spatial resolution (0.80 m) is proposed. It combines digital vector and raster data in a per-parcel classification approach. The classification variables are reflectances in the red, green, and blue bands, and six texture indices derived from the grey level co-occurrence matrix (GLCM). The effect of the calibration sample size and the number of variables on the classification accuracy was investigated. Calibration and validation sets were formed by respectively 220 and 219 parcels. The maximum global accuracy (79%) is achieved by using the six variables that contribute most to the discriminatory power of the model. These variables were reflectances in the red, green, and blue bands and three texture indices: contrast, correlation, and variance. According to the discriminatory power analysis, the global accuracy should be improved by including in the model the three remaining variables if the calibration set were large enough. Nine forest stand types based on species composition and class height were identified. The identified species are common spruce, fir, larch, oak, and beech. Three classes were discriminated in common spruce stands. In terms of the producers accuracy, more than 80% of the area was classified with an accuracy higher than 75%. In terms of users accuracy, more than 70% of the area was classified with an accuracy higher than 80%. These results show the importance of texture consideration in the analysis of very high spatial resolution imagery in forest remote sensing.

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions.

From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities

Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thresholds on rainfall estimations. However, the coarse spatial resolution and the possible inaccuracy of these estimations are limiting factors. In this context, the remote sensing approach, which consists of deriving green-up onset dates from satellite remote sensing data, appears as an interesting alternative. It builds upon a novel statistic model that translates vegetation onset detections derived from MODIS time series into sowing probabilities at the village level. Results for Niger show that this approach outperforms the standard method adopted in the region based on rainfall thresholds.