Assessment of remotely sensed and statistical inventories of African agricultural fields

Hannerz, Fredrik; Lotsch, Alexander

doi:10.1080/01431160801891762

Cited by 28 publications

(19 citation statements)

References 24 publications

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“…Even higher variability among the datasets becomes visible at the district level ( Figure 12). These deviations in cropped area at different scales, even among the remote sensing products, confirm observations made by previous researchers about the generally low level of agreement in such comparisons [11,12]. On the other hand, the graph shows a relatively increased congruence between the three data sources for districts in the UER (standard deviations of 15.…”

Section: Plausibility Analysis Using Official Census Datasupporting

confidence: 88%

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

et al. 2017

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Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer's accuracy and user's accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond.

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Section: Plausibility Analysis Using Official Census Datasupporting

confidence: 88%

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

et al. 2017

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“…Worldwide, cropland distribution estimates derived from GlobCover are more than 20% higher than those derived from MODIS [20,30,54]. These differences can be attributed to a number of factors, including the use of different classification algorithms with considerably diverse parameters, diverse satellite datasets used for different algorithms, dissimilar spatial resolutions, and the different temporal windows used to develop the land cover and cropland maps [9][10][11][12]28]. The land cover and cropland maps used for geospatial modeling can therefore have a theoretically huge influence on the outputs.…”

Section: Discussionmentioning

confidence: 99%

“…This is because, in different countries, the cropland or agricultural classes that are often used to describe different classes in land cover maps are not clearly defined, and there is no common agreement as to what they constitute, Hence, establishing a suitable definition of cropland will assist in the mapping of all the classes that come under the category of cropland/agricultural class [9][10][11][12][13]15,52]. Moreover, in future, this may also facilitate the sharing of data, thereby helping to ensure no large gaps in crop coverage or mismatches with other maps that are available globally.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the continuously growing wealth of low-and middle-income developing countries, an increase in annual food production of 60-70% will be required to feed their rapidly growing populations [1,3,7]. Mapping cropland extent can yield timely, updated, and accurate cropland information that provides essential inputs to crop monitoring systems and early warning systems, such as CropWatch, Global Information and Early Warning System (GIEWS), the Early Warning Crop Monitor and the Famine Early Warning Systems Network (FEWSNET), and Forecasting Agricultural output using Space, Agrometeorological and Land-based observations (FASAL) [9][10][11][12]. Such mapping represents an important step in agricultural production assessment and has direct benefits for the early forecasting of cropping pattern distribution and spread of diseases, and also provides information for climate change studies [13].…”

Section: Introductionmentioning

confidence: 99%

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A Review of the Available Land Cover and Cropland Maps for South Asia

Patil¹,

Gumma²

2018

Agriculture

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A lack of accuracy, uniqueness and the absence of systematic classification of cropland categories, together with long-pending updates of cropland mapping, are the primary challenges that need to be addressed in developing high-resolution cropland maps for south Asia. In this review, we analyzed the details of the available land cover and cropland maps of south Asia on national and regional scales in south Asia and on a global scale. Here, we highlighted the methodology adopted for classification, datasets used, classification system used for classifying different land covers and croplands and the resolution of datasets available. This listed review of different available datasets can help the reader to know which datasets to be used in their study and to understand which methodology to be chosen to further developing the accurate high-resolution land cover and cropland maps for advanced studies and for better understanding of ground reality in a timely updated version. We tried to identify the major concerns, particularly the inadequacy of knowledge regarding the spatial distribution of major crop types within south Asia, which hinder policy and strategic investment and delay the efforts to improve food security for a rapidly growing human population at a time of constant market instability and changing global climate. The overall focus of this paper is on reviewing the need for timely updated high-resolution cropland maps of south Asia.

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“…The combination of field size, residual tree canopies, and crop diversity magnify the within-class variability of crop field while blurring the spectral distinction between croplands and savannas. The difficulty in distinguishing cropland from savanna is evidenced by the disagreement between products derived from remote sensing and statistical inventories of African agriculture [16,17]. Given these discrepancies, and the absence of a single reliable dataset to inform policy decisions regarding food security issues and land use, an effective and rigorous methodology for mapping croplands from satellite images is much needed for sub-Saharan savanna landscapes [18].…”

Section: Introductionmentioning

confidence: 99%

Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

2015

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Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region's food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We OPEN ACCESSRemote Sens. 2015, 7 15296 present summary results for this methodology applied to a land cover classification of Zambia's Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.

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Assessment of remotely sensed and statistical inventories of African agricultural fields

Cited by 28 publications

References 24 publications

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

A Review of the Available Land Cover and Cropland Maps for South Asia

Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

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