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Book (stand-alone)Geospatial assessment of cultivated cropland extent in the Sudan
July to September 2023, Technical report
2023Also available in:
No results found.The Sudan, a vast Sahelian nation in northeastern Africa, heavily relies on its agriculture sector, supporting the livelihoods of 60–80 percent of its population. However, the recent armed conflict that began in mid-April 2023 has significantly impacted agricultural cultivation, exacerbating the existing food insecurity situation in the country. To address these challenges, a geospatial assessment was undertaken to estimate the extent of cultivated cropland during the summer cropping season (July to September 2023) in comparison to the previous 5-year average (2018 to 2022). This assessment aims to provide insights into the conflict's potential impact on agricultural cultivation and facilitate decision-making to manage both immediate and long-term food security challenges. This technical report outlines the methodology, data, and key findings for July, August and September 2023. -
BookletGeospatial cropland monitoring and crop type mapping of the Gezira irrigation scheme in the Sudan
2019/20-2023/24 (September to March)
2024Also available in:
No results found.Sudan is facing an increasing risk of severe food insecurity due to the ongoing conflict that began in April 2023, which has significantly impacted the agricultural sector. Irrigated farming is vital to Sudan's agricultural system, and the Gezira project stands as the largest and most important irrigation scheme. Recognizing the critical role of the Gezira irrigation scheme in Sudan’s agriculture and food security sector, this report presents an assessment of changes in cultivated cropland areas within the scheme. The analysis focuses on the growing cycle of dominant crops from September to March over the period from 2019/20 to 2023/24. By monitoring the extent of cropland for three major crops—wheat, sorghum, and cotton—this analysis reveals trends and shifts in crop cultivation and productivity, with a particular focus on wheat production.Utilizing remote sensing data from Sentinel-2 satellite imagery, an innovative methodology was developed to address challenges in data collection in conflict-affected areas. Time-series analyses of vegetation indices and crop calendars throughout different periods of the crop growth cycle were employed to classify crop types and monitor crop rotation. Very high-resolution imagery was specifically used for verification purposes to ensure accuracy.The analysis revealed a reduction in the cultivated area and a shift from wheat to sorghum cultivation. Alongside a sharp decline in wheat yield, overall crop production is expected to exacerbate food insecurity levels throughout Sudan. This situation highlights the urgent need for integrated strategies to enhance agricultural resilience and food security under these challenging conditions. -
Book (stand-alone)USE OF NOAA REMOTE SENSING DATA FOR ASSESSMENT OF THE FOREST AREA OF LIBERIA 1993
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No results found.In the context of developing a practicable and cost effective method for obtaining a country’s forest area by remote sensing, the computer processing of NOAA AVHRR HRPT data covering Liberia was investigated. The only cloud-free scene then recorded turned out to be severely and unevenly affected by atmospheric haze. To mitigate the effects of this, the country was divided into six areas (strata) of more uniform haze conditions. Pixel DN values were obtained for forest and adjoining formations on transects within each stratum, for the first four AVHRR channels and three transforms: NDVI (2-1/2 + 1), IND3 (3-2/3 + 2), IND4(4-2/4 + 2). After analyzing the transects and comparing them with the available reference data (a mixture of large scale colour composite Landsat TM and MSS images for 1989 and 1986 respectively), channels 2, 3, 4 and IND3 were retained for processing. This was done by applying three methods to each stratum - thresholding, maximum likelihood classification using cluste ring signatures (hybrid), m.l.c. using training area signatures - and directed at separating the 5 main classes distinguishable on the Landsat images: Closed forest, Disturbed forest, Shifting cultivation and regrowth, Cultivation and Other.
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