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Combining Use of TRMM and Ground Observations of Annual Precipitations for Meteorological Drought Trends Monitoring in Morocco
Rachid Hadria,
Abdelghani Boudhar,
Hamza Ouatiki,
Youssef Lebrini,
Loubna Elmansouri,
Fouad Gadouali,
Hayat Lionboui Hayat Lionboui,
Tarik Benabdelouahab
Issue:
Volume 7, Issue 2, December 2019
Pages:
25-34
Received:
31 August 2019
Accepted:
25 September 2019
Published:
10 October 2019
Abstract: The monitoring of drought statewide is a difficult issue especially when the national network of meteorological stations is sparse or do not cover the entire country. In this paper, rainfall satellite estimates derived from Tropical Rainfall Measuring Mission (TRMM) product have been used to evaluate the ability of remote sensing data to study the trends of annual precipitation in Morocco between 1998 and 2012. The standardized precipitation index, SPI, has been chosen to monitor meteorological drought in Morocco. Firstly, the accuracy of TRMM product to estimate annual rainfall was evaluated. Annual precipitations derived from 5113 daily TRMM data were compared to the corresponding rainfall measurements from 23 rain gauges. The results showed a general good linear relationship between TRMM and rain gauges data. When considering annual record, the Pearson correlation coefficient, R², was equal to 0.73 and the root mean square error, RMSE, was equal to 159.8mm/year. The correlation between rain gauge measurements and TRMM rainfall had been clearly improved when working with long-term annual average precipitation. The R² increased to 0.79 and the RMSE decreased to 115,2mm. Secondly, the Mann-kendall tau coefficient, the Theil Sen slope and the contextual Mann-Kendall significance were used to analyze the SPI trends over Morocco. This analysis showed that mainly two regions appeared to be subject of significant trends during the studied period: The extreme north eastern of Morocco manifests a positive SPI trends and is more and more subject of extreme rainfall while the extreme south of the country is suffering from a decrease of annual precipitation which could represent significant socio-economic risks in these areas.
Abstract: The monitoring of drought statewide is a difficult issue especially when the national network of meteorological stations is sparse or do not cover the entire country. In this paper, rainfall satellite estimates derived from Tropical Rainfall Measuring Mission (TRMM) product have been used to evaluate the ability of remote sensing data to study the ...
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Using Remote Sensing Technics for Land Use Land Cover Changes Analyses from 1950s to 2000s in Somone Tropical Coastal Lagoon, Senegal
Ndéye Yacine Barry,
Mamadou Lamine Ndiaye,
Celestin Hauhouot,
Bienvenu Sambou
Issue:
Volume 7, Issue 2, December 2019
Pages:
35-49
Received:
3 September 2019
Accepted:
24 September 2019
Published:
14 October 2019
Abstract: In many developing countries, some natural areas are faced with gaps in appropriate map coverage mainly on land use and land cover (LULC) changes. This situation makes it difficult to plan and implement natural environmental protection and natural resource management programs. Remote sensing and geographic information systems (GIS) are excellent tools for mapping LULC changes. This study investigated LULC changes in ‘Somone’ coastal lagoon in Senegal using multisource remote sensed data. Data sets included aerial photographs recorded in March 1954, and February 1978, as well as satellite images recorded in February 2003 and April 2016. All images were geometrically corrected and segmented. Photos and/or images interpretations were made with the aid of computer and post-classification change detection technique was applied to classify multisource data and to map changes. Stratified sampling was used to assess all classification results. The accuracies of image classifications averaged 65% (1954), 62% (1978), 79% (2003) and 88% (2016). The post-classification analysis resulted in the largest overall accuracy of 66, 72.7, 72.4 and 80.6% for the 1954–1978, 1978-2003 and 2003–2016 image pairs, respectively. Results indicated an increase in Settlements, from 0.29% in 1954 to 9.21% in 2016, the expansion of the Sabkha, from 5.29% in 1954 to 18.48% in 2016. The mangrove forest has experimented a reduction between 1954 and 1978 (from 4.07% to 0.56%) and a regeneration (linked to the protection and preservation policies within the protected area) from the year 2003 to 2016 (from 1.44% to 2.65%). However, the forest areas were greatly reduced (from 51.06% in 1954 to 10.86% in 2016) and replaced by Settlements (urbanization) as well as Croplands.
Abstract: In many developing countries, some natural areas are faced with gaps in appropriate map coverage mainly on land use and land cover (LULC) changes. This situation makes it difficult to plan and implement natural environmental protection and natural resource management programs. Remote sensing and geographic information systems (GIS) are excellent to...
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The Positional Effect in Soft Classification Accuracy Assessment
Jianyu Gu,
Russell G. Congalton
Issue:
Volume 7, Issue 2, December 2019
Pages:
50-61
Received:
26 August 2019
Accepted:
15 October 2019
Published:
24 October 2019
Abstract: Recent research has included the rapid development of soft classification algorithms and soft classification accuracy assessment beyond the traditional hard approaches. However, less consideration has been given to whether conditions and assumptions generated for the hard classification accuracy assessment are appropriate for the soft one. Positional error is one of the most significant uncertainties that need to be considered. This research examined the impacts of positional errors on the accuracy measures derived from the soft error matrix using NLCD 2011 as reference data and several coarser maps generated from NLCD 2011 as classification maps at the spatial resolutions of 150m, 300m, 600m, and 900m. Eight study sites, with a spatial extent of 180km×180km, of different landscape characteristics were investigated using a two-level classification scheme. Results showed that with existing registration accuracies achieved by current global land cover mapping, the errors in overall accuracy (OA-error) were 2.13% -39.98% and 2.53%-48.82% for the 8 and 15 classes, respectively and the errors in Kappa (Kappa-error) were 6.64%-57.09% and 7.08%-58.81% for the 8 and 15 classes, respectively if soft classifications were implemented based on images where spatial resolutions varied from 150m to 900m. More complex landscape characteristics and classes in the classification scheme produced a greater impact of the positional error on the accuracy measures. To keep both OA-error and Kappa-error under 10 percent, the average required registration accuracy should achieve 0.1 pixels. This paper strongly recommends the addition of uncertainty analysis due to positional error in future global land cover mapping.
Abstract: Recent research has included the rapid development of soft classification algorithms and soft classification accuracy assessment beyond the traditional hard approaches. However, less consideration has been given to whether conditions and assumptions generated for the hard classification accuracy assessment are appropriate for the soft one. Position...
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