Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Delrue, Josefien; Bydekerke, Lieven; Eerens, Herman; Gilliams, Sven; Piccard, Isabelle; Swinnen, Else

doi:10.1080/01431161.2012.747016

Cited by 26 publications

(16 citation statements)

References 12 publications

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“…Furthermore, important commission errors could be a result of the large pixel size where non-crop areas that surround crop areas (mainly grassland or shrubland) could be mapped as cropland areas, as explained by Wardlow and Egbert [52] and recently by Vintrou et al [32]. In addition, some studies have also highlighted the difficulties in mapping cultivated areas, mainly in the Sahel, due to the high degree of ambiguity of assigning a single class to a heterogeneous landscape composed of natural vegetation and croplands, which have spectral, textural and temporal similarities [10,11,23]. Consequently, the Cropland/Natural vegetation mosaic class of the MODIS LCP may group different classes together.…”

Section: Relationship Between Spatial Accuracy Landscape Fragmentatimentioning

confidence: 98%

“…Fritz et al [7] and Hannerz et al [11] also found large disagreements in Africa, particularly in the Sahelian belt where the cropping density is lower. In Sub-Saharan African landscapes, crops are particularly difficult to discriminate due to the parcel sizes, which are often smaller than the pixel size [10,11], and landscape fragmentation [7,28]. In addition, depending on the environmental (e.g., climate or topography), historical, political, social and technological contexts, the spatial extent of croplands and cropping systems are highly variable between and within countries.…”

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confidence: 99%

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How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

et al. 2014

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Abstract:Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R 2 = 0.86 *** at the national scale to R 2 = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%-70%) compared to the eastern part (17%-43%); (ii) Theoretical user and producer accuracies for OPEN ACCESS Remote Sens. 2014, 6 8542 a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region.

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Section: Relationship Between Spatial Accuracy Landscape Fragmentatimentioning

confidence: 98%

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confidence: 99%

How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

et al. 2014

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“…Early warning systems e.g., the Famine Early Warning Systems Network (FEWS-NET) and the Global Information and Early Warning System (GIEWS), for food security require accurate and up-to-date spatial information about the cropland to monitor food production [7]. Nevertheless, mapping cropland remains challenging in this region, especially because of the agricultural landscape fragmentation, the spatial heterogeneity of the cropland, the diversity of the cropping systems and the mosaic of cropland, fallow and natural grassland [14,15]. Table 1.…”

Section: Introductionmentioning

confidence: 99%

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

2016

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Abstract:Early warning systems for food security require accurate and up-to-date information on the location of major crops in order to prevent hazards. A recent systematic analysis of existing cropland maps identified priority areas for cropland mapping and highlighted a major need for the Sahelian and Sudanian agrosystems. This paper proposes a knowledge-based approach to map cropland in the Sahelian and Sudanian agrosystems that benefits from the 100-m spatial resolution of the recent PROBA-V sensor. The methodology uses five temporal features characterizing crop development throughout the vegetative season to optimize cropland discrimination. A feature importance analysis validates the efficiency of using a diversity of temporal features. The fully-automated method offers the first cropland map at 100-m using the PROBA-V sensor with an overall accuracy of 84% and an F-score for the cropland class of 74%. The improvements observed compared to existing cropland products are related to the hectometric resolution, to the methodology and to the quality of the labeling layer from which reliable training samples were automatically extracted. Classification errors are mainly explained by data availability and landscape fragmentation. Further improvements are expected with the upcoming enhanced cloud screening of the PROBA-V sensor.

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“…Accuracy of higher-level cropland products such as cropping intensities, crop types, crop watering methods (e.g., irrigated or rainfed), planted or left fallow, crop health, crop productivity (productivity per unit of land, kg·m −2 ), and crop water productivity (productivity per unit of water or crop per drop, kg·m −3 ) are dependent on having a precise cropland extent product as a baseline product. In Africa, these products are particularly helpful due to the absence of high resolution cropland products that map field level details of croplands making them an invaluable baseline product for all higher-level products such as crop type, crop productivity, and crop water productivity [5,6].…”

Section: Introductionmentioning

confidence: 99%

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

et al. 2017

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A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of

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Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Cited by 26 publications

References 12 publications

How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

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