Pub Date : 2021-04-01DOI: 10.11648/J.AJRS.20210901.15
Pragati Singh, A. Singh, R. Upadhyay
Upgrading water use in agricultural crops requires advancements in location of crop water stress for irrigation scheduling, at different phases of the developing season to limit crop physiological harm and yield reduction. Potential of satellite data provide spatial and temporal dynamics of crop growth condition under water stress and analyse for suggestion of irrigation. This study is based on real time open-source web-based Google Earth Engine (GEE) approach for irrigation scheduling at field level based on its water stress condition. Sentinel-2 data was used for detecting water stress using the NDVI and NDWI indices. NDVI shows the crop growth and health whereas NDWI its water stress condition, by combining both the indices we have generated a new index, which is Crop Water Stress Index (CWSI) to schedule the irrigation. The real time Sentinel-2 data was used extract NDVI and NDWI indices and by combining both the indices a new indice was generated for detecting crop water stress condition to schedule the irrigation in real time. The output comes in five group of water stress condition as; No Stress, Low stress, Moderate stress, High stress and Severe stress. Using the result of CWSI the immediate irrigation should be given to those fields which are facing severe and high stress, delayed in moderate and low stress and no irrigation in no-stress. The overall study indicates that, GEE provide a real time better platform for analysing Crop Water Stress situation for scheduling proper irrigation practices for proper growth of crops to improve the production and income of farmers as well as It helps the irrigation planner for proper management of canals and other irrigation resources to the wastage of water.
{"title":"A Web Based Google Earth Engine Approach for Irrigation Scheduling in Uttar Pradesh India Using Crop Water Stress Index","authors":"Pragati Singh, A. Singh, R. Upadhyay","doi":"10.11648/J.AJRS.20210901.15","DOIUrl":"https://doi.org/10.11648/J.AJRS.20210901.15","url":null,"abstract":"Upgrading water use in agricultural crops requires advancements in location of crop water stress for irrigation scheduling, at different phases of the developing season to limit crop physiological harm and yield reduction. Potential of satellite data provide spatial and temporal dynamics of crop growth condition under water stress and analyse for suggestion of irrigation. This study is based on real time open-source web-based Google Earth Engine (GEE) approach for irrigation scheduling at field level based on its water stress condition. Sentinel-2 data was used for detecting water stress using the NDVI and NDWI indices. NDVI shows the crop growth and health whereas NDWI its water stress condition, by combining both the indices we have generated a new index, which is Crop Water Stress Index (CWSI) to schedule the irrigation. The real time Sentinel-2 data was used extract NDVI and NDWI indices and by combining both the indices a new indice was generated for detecting crop water stress condition to schedule the irrigation in real time. The output comes in five group of water stress condition as; No Stress, Low stress, Moderate stress, High stress and Severe stress. Using the result of CWSI the immediate irrigation should be given to those fields which are facing severe and high stress, delayed in moderate and low stress and no irrigation in no-stress. The overall study indicates that, GEE provide a real time better platform for analysing Crop Water Stress situation for scheduling proper irrigation practices for proper growth of crops to improve the production and income of farmers as well as It helps the irrigation planner for proper management of canals and other irrigation resources to the wastage of water.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126786235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-26DOI: 10.11648/J.AJRS.20210901.14
J. A. Maghah, Reeves M. Fokeng
Globally, forest reserves are created with a premier objective to conserve important biodiversity and to ensure ecosystems services provision. Unfortunately, forest reserves in the global south are threatened by the tremendous rise in human numbers and the unsustainable exploitation of forest resources. This is the problem facing protected areas (PAs), including forest reserves in Cameroon. The Bafut-Ngemba Forest Reserve (BNFR) is just a case in point of the many transformed and ecological twisted forest reserves in the Western Highlands of Cameroon. The BNFR is no biodiversity paradise as the humanisation of the reserve has taken an unprecedented toll in recent times. The study updated forest cover changes within the reserve from previous studies spanning across 2010-2021 as a baseline data towards the effective design of sustainable forest conservation planning. Satellite remote sensing employing high resolution ASTER (15m) and real-time Google Earth images were used to assess the forest cover dynamics. Between 2010 and 2015, forest loss was mild, either -27.135ha. From 2015-2021, in just less than 6 years, 696.397ha of forest cover was lost. For the entire study period (2010-2021), at total of 723.532ha of forest is estimated to have been lost. Forest loss in the BNFR is linked to some four anthropogenic stressors; farmland encroachment, eucalyptus colonisation, wood harvesting and cattle grazing alongside inter-annual fires used for pasture regeneration and rangeland management. Conservation efforts are urgently needed should the remaining threatened biodiversity, mostly avifauna is to be protected in line with monitoring progress to global targets and SDG 15.1.1.
{"title":"The Degradation of the Bafut-Ngemba Forest Reserve Revisited: A Spatio-temporal Analysis of Forest Cover Change Dynamics","authors":"J. A. Maghah, Reeves M. Fokeng","doi":"10.11648/J.AJRS.20210901.14","DOIUrl":"https://doi.org/10.11648/J.AJRS.20210901.14","url":null,"abstract":"Globally, forest reserves are created with a premier objective to conserve important biodiversity and to ensure ecosystems services provision. Unfortunately, forest reserves in the global south are threatened by the tremendous rise in human numbers and the unsustainable exploitation of forest resources. This is the problem facing protected areas (PAs), including forest reserves in Cameroon. The Bafut-Ngemba Forest Reserve (BNFR) is just a case in point of the many transformed and ecological twisted forest reserves in the Western Highlands of Cameroon. The BNFR is no biodiversity paradise as the humanisation of the reserve has taken an unprecedented toll in recent times. The study updated forest cover changes within the reserve from previous studies spanning across 2010-2021 as a baseline data towards the effective design of sustainable forest conservation planning. Satellite remote sensing employing high resolution ASTER (15m) and real-time Google Earth images were used to assess the forest cover dynamics. Between 2010 and 2015, forest loss was mild, either -27.135ha. From 2015-2021, in just less than 6 years, 696.397ha of forest cover was lost. For the entire study period (2010-2021), at total of 723.532ha of forest is estimated to have been lost. Forest loss in the BNFR is linked to some four anthropogenic stressors; farmland encroachment, eucalyptus colonisation, wood harvesting and cattle grazing alongside inter-annual fires used for pasture regeneration and rangeland management. Conservation efforts are urgently needed should the remaining threatened biodiversity, mostly avifauna is to be protected in line with monitoring progress to global targets and SDG 15.1.1.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122711304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-22DOI: 10.11648/J.AJRS.20210901.13
Jean Paul Nkundabose, Félix Nshimiyimana, Gratien Twagirayezu, Olivier Irumva
The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.
{"title":"Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda","authors":"Jean Paul Nkundabose, Félix Nshimiyimana, Gratien Twagirayezu, Olivier Irumva","doi":"10.11648/J.AJRS.20210901.13","DOIUrl":"https://doi.org/10.11648/J.AJRS.20210901.13","url":null,"abstract":"The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"442 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122787362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-23DOI: 10.11648/J.AJRS.20210901.12
Arisetra Razafinimaro, A. R. Hajalalaina, Zojaona Tantely Reziky, E. Delaître, A. Andrianarivo
This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.
{"title":"Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve","authors":"Arisetra Razafinimaro, A. R. Hajalalaina, Zojaona Tantely Reziky, E. Delaître, A. Andrianarivo","doi":"10.11648/J.AJRS.20210901.12","DOIUrl":"https://doi.org/10.11648/J.AJRS.20210901.12","url":null,"abstract":"This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134076949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-05DOI: 10.11648/J.AJRS.20210901.11
A. H. N. Mfondoum, Paul Gérard Gbetkom, Sofia Hakdaoui, R. Cooper, Armel Fabrice Mvogo Moto, Brian Njumeneh
Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaounde (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaounde and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.
{"title":"Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise","authors":"A. H. N. Mfondoum, Paul Gérard Gbetkom, Sofia Hakdaoui, R. Cooper, Armel Fabrice Mvogo Moto, Brian Njumeneh","doi":"10.11648/J.AJRS.20210901.11","DOIUrl":"https://doi.org/10.11648/J.AJRS.20210901.11","url":null,"abstract":"Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaounde (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaounde and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-24DOI: 10.11648/J.AJRS.20190702.13
Jianyu Gu, R. Congalton
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.
{"title":"The Positional Effect in Soft Classification Accuracy Assessment","authors":"Jianyu Gu, R. Congalton","doi":"10.11648/J.AJRS.20190702.13","DOIUrl":"https://doi.org/10.11648/J.AJRS.20190702.13","url":null,"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.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131707894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-14DOI: 10.11648/J.AJRS.20190702.12
Ndeye Yacine Barry, M. Ndiaye, Célestin Hauhouot, B. Sambou
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.
{"title":"Using Remote Sensing Technics for Land Use Land Cover Changes Analyses from 1950s to 2000s in Somone Tropical Coastal Lagoon, Senegal","authors":"Ndeye Yacine Barry, M. Ndiaye, Célestin Hauhouot, B. Sambou","doi":"10.11648/J.AJRS.20190702.12","DOIUrl":"https://doi.org/10.11648/J.AJRS.20190702.12","url":null,"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.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127415484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-11DOI: 10.11648/J.AJRS.20190702.11
R. Hadria, A. Boudhar, H. Ouatiki, Y. Lebrini, L. Elmansouri, F. Gadouali, H. Lionboui, T. Benabdelouahab
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.
{"title":"Combining Use of TRMM and Ground Observations of Annual Precipitations for Meteorological Drought Trends Monitoring in Morocco","authors":"R. Hadria, A. Boudhar, H. Ouatiki, Y. Lebrini, L. Elmansouri, F. Gadouali, H. Lionboui, T. Benabdelouahab","doi":"10.11648/J.AJRS.20190702.11","DOIUrl":"https://doi.org/10.11648/J.AJRS.20190702.11","url":null,"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.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128156357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-20DOI: 10.11648/J.AJRS.20190701.13
Wenjia Yan, Y. Zhang, Tianpeng Yang, Xiaohui Liu
Accurate simulation of microwave scattering characteristics of wheat canopy can provide valuable insights into the scattering mechanisms of wheat crops. In this study, a wheat canopy scattering model (WCSM) was developed on a basis of first-order microwave radiative transfer equation. Several WCSM inputs, including wheat canopy and soil parameters, were measured in situ at the time (or near the time) of the satellite observation. The backscattering coefficients of wheat fields were then simulated at various incident angles and polarization modes. Four C-band quad-polarized (Radarsat-2 and Gaofen-3) SAR data were used to evaluate the WCSM performance in four key growth stages of winter wheat from stem elongation to ripening in 2017. Results showed that the WCSM simulated backscattering coefficients of wheat fields with error lower than 1.8 dB. This study demonstrates that the proposed WCSM is effective in characterizing the C-band backscatter features of wheat crops for various growth phases. It also indicated that the operational potential of C-band satellite SAR systems such as the Radarsat-2 and the China Gaofen-3 SAR in monitoring wheat growth for food safety in important agricultural regions.
{"title":"A Microwave Scattering Model for Simulating the C-Band SAR Backscatter of Wheat Canopy","authors":"Wenjia Yan, Y. Zhang, Tianpeng Yang, Xiaohui Liu","doi":"10.11648/J.AJRS.20190701.13","DOIUrl":"https://doi.org/10.11648/J.AJRS.20190701.13","url":null,"abstract":"Accurate simulation of microwave scattering characteristics of wheat canopy can provide valuable insights into the scattering mechanisms of wheat crops. In this study, a wheat canopy scattering model (WCSM) was developed on a basis of first-order microwave radiative transfer equation. Several WCSM inputs, including wheat canopy and soil parameters, were measured in situ at the time (or near the time) of the satellite observation. The backscattering coefficients of wheat fields were then simulated at various incident angles and polarization modes. Four C-band quad-polarized (Radarsat-2 and Gaofen-3) SAR data were used to evaluate the WCSM performance in four key growth stages of winter wheat from stem elongation to ripening in 2017. Results showed that the WCSM simulated backscattering coefficients of wheat fields with error lower than 1.8 dB. This study demonstrates that the proposed WCSM is effective in characterizing the C-band backscatter features of wheat crops for various growth phases. It also indicated that the operational potential of C-band satellite SAR systems such as the Radarsat-2 and the China Gaofen-3 SAR in monitoring wheat growth for food safety in important agricultural regions.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-19DOI: 10.11648/J.AJRS.20180701.12
N. Al-Mutairi, M. Alsahli, M. Ibrahim, R. A. Samra, M. El-Gammal
Digital elevation models (DEMs) are essential tools utilized in several branches of science, including environmental, geological, and geospatial studies. Unfortunately, high-accuracy DEM data such as LiDAR are not publicly available, and the coverage is limited. Therefore, the use of alternative methods, such as interpolation techniques (i.e., kriging, inverse distance weighting, radial basis functions), is greatly advantageous for the production of enhanced DEMs. The results of this study show that interpolated DEMs had minimal errors (RMSE = 1.44) with an increase of about 28% from the original DEM. However, the spatial resolution of interpolated DEM data was enhanced significantly by 83%. The deterministic interpolation methods provided more accurate estimations for producing DEMs in the coastal zones of Kuwait than geostatistical interpolation methods. The reference elevation data were collected using GPS and accurate topographic maps (1:25,000), and elevation points from the interpolated DEM were matched significantly (R2 = 0.88; R2 = 94, respectively). Given the lack of accurate DEM data, the interpolated DEM produced in this study are held in high regard and highly recommended for use in the coastal zone of Kuwait.
{"title":"Spatial Enhancement of DEM Using Interpolation Methods: A Case Study of Kuwait’s Coastal Zones","authors":"N. Al-Mutairi, M. Alsahli, M. Ibrahim, R. A. Samra, M. El-Gammal","doi":"10.11648/J.AJRS.20180701.12","DOIUrl":"https://doi.org/10.11648/J.AJRS.20180701.12","url":null,"abstract":"Digital elevation models (DEMs) are essential tools utilized in several branches of science, including environmental, geological, and geospatial studies. Unfortunately, high-accuracy DEM data such as LiDAR are not publicly available, and the coverage is limited. Therefore, the use of alternative methods, such as interpolation techniques (i.e., kriging, inverse distance weighting, radial basis functions), is greatly advantageous for the production of enhanced DEMs. The results of this study show that interpolated DEMs had minimal errors (RMSE = 1.44) with an increase of about 28% from the original DEM. However, the spatial resolution of interpolated DEM data was enhanced significantly by 83%. The deterministic interpolation methods provided more accurate estimations for producing DEMs in the coastal zones of Kuwait than geostatistical interpolation methods. The reference elevation data were collected using GPS and accurate topographic maps (1:25,000), and elevation points from the interpolated DEM were matched significantly (R2 = 0.88; R2 = 94, respectively). Given the lack of accurate DEM data, the interpolated DEM produced in this study are held in high regard and highly recommended for use in the coastal zone of Kuwait.","PeriodicalId":417484,"journal":{"name":"American Journal of Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114921224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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