Eslam Farg | NARSS - Academia.edu (original) (raw)
Papers by Eslam Farg
The current study aims to utilize the use of multi-remote sensing data for land use land cover ch... more The current study aims to utilize the use of multi-remote sensing data for land use land cover changes and trend analysis for the New Damietta city in Damietta governorate. Three different sensors were used in this study in different dates (SPOT-4 2007, SPOT-5 2011, and Kanopus-V1 2016). The FAO classification system (FAO-LCCS) was used to identify the different land use/cover classes in the study area. Results showed 13 main land use/land cover classes exist in the study area. The land use/land cover maps are produced for 2007, 2011 and 2016 with overall accuracies of 0.91, 0.92, 0.91 and kappa statistics of 0.88, 0.86 and 0.89 respectively. Results revealed that four different classes had a significant change over the study period. These classes are urban areas, cultivated lands, fish farms and bare areas. Trend analysis revealed that urban areas had the Original Research Article Arafat et al.; JGEESI, 14(3): 1-12, 2018; Article no.JGEESI.40132 2 highest increase rate (+2.76 km2/year short term & +2.73 km2/year long-term) while cultivated land and bare areas suffer from the highest decrease rates (-1 km 2 /year short and long-term,-1.54 km 2 /year short-term and-1.59 km 2 /year long term respectively).
The Egyptian Journal of Remote Sensing and Space Science
The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels tha... more The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of Agricultural statistics in surveying cropland. The use of remote sensing help in an accurate crop inventory under complex landscape conditions based on the spectral characteristics differences of crops. The current study was carried out in Abu El Matamir district, Behira Governorate, located in western Nile delta Egypt. The main objective of the current study is using time series analysis of remote sensing data in crop discrimination. In this study, 160 locations of ground truth points collected during different growth stages of summer season crops. Two different sensors images used in this study represented by single date image of RapidEye and multi-date Landsat 8 OLI satellites. The acquired satellite images from both sensors atmospherically and geometrically corrected. Moreover different vegetation indices calculated such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) for cultivated crops in the study area during the whole growing season. Preliminary statistical analysis applied to the collected field data to show the distribution of the cultivated crops types. Moreover, unsupervised Iso-Data applied for multi-date Landsat 8 OLI images and calculated VI's series for overall growth season. Results showed higher overall kappa accuracy with 0.82 and 0.79 respectively. NDVI showed the best representation of the crop pheno-logical changes during the crop growth season and showed higher accuracy in strategic crops discrimination than the single date image with higher spatial resolution.
Hyperspectral ground measurements can be used for giving timely information about crops in specif... more Hyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers. In the current study, ASD field Spec4 spectroradiometer were used to monitor the variation and differences of the summer crop vegetation cover reflectance. Furthermore, two hyperspectral vegetation indices calculated from the data represented by Normalized Difference Vegetation Index NDVI HS and Soil Adjusted Vegetation Index SAVI HS in east Nile Delta, Egypt. The results obtained showed that the mid-season stage had the highest values of calculated VI's that return to the high reflection from the plant canopy at the near infrared and high absorption at the red wavelength, also the initial growth stage VI's values lower than the mid-season stage and higher than the late season. In addition, the analysis of spectral signatures differences showed the late growth stage was the highest reflection overall the visible range (blue, green and red).
The spectral signatures of natural objects in the visible and near-infrared spectral range are in... more The spectral signatures of natural objects in the visible and near-infrared spectral range are influenced by the object's physical and biochemical properties. These signatures can be compiled in a database and used to retrieve information of land cover types and their physical composition from actual hyperspectral observations. This paper describes development process of hyperstectral database of reflectance from different land cover types in Egypt. It has been compiled from data obtained using a ground-based spectroradiometer system that covers the spectral range from 350 to 2500 nm at 1 nm resolution. The database is accessible through a website http://www.spectraldb.narss.sci.eg/spectral, where the system includes also metadata that describes the site environment and measurement processes. The system provides flexible mechanisms and friendly interfaces to allow accessing the database by the non-specialized people, whereas spectral data can be sorted by sites, species or selected environmental parameters. The system presents sample results from different vegetation and soil covers. Development of such a database is essential for different remote sensing applications , satellite's calibrations, data dissemination and linkage with other databases for scientific researches purposes .
The main objectives of this research is to determine the optimal hyperspectral range and waveband... more The main objectives of this research is to determine the optimal hyperspectral range and waveband/s in the spectral range of (400–2500 nm) to discriminate between four different varieties of Potato crop (Diamond, Everest, Mondial and Rosetta) that are cultivated in old and newly cultivated lands of Egypt and to propose detailed spectral reflectance characterization for these four varieties which will enable more accurate surveying of these varieties through satellite imagery. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the crop. An average of thirty measurements for each variety was considered in the process. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400–2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey's HSD analysis was used to choose the optimal spectral zone that could be used to differentiate between the four varieties. Then, linear discrimination analysis (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each variety could be spectrally identified. The results of Tukey's HSD showed that NIR is the best spectral zone for the discrimination between the four varieties. The other five spectral zones showed close spectral characterizations between at least two varieties. The results of (LDA) showed the optimal waveband to identify each variety. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to estimate potato crop acreage. The study was carried out in AlBuhayrah governorate of Egypt.
Traditional methods for center pivot evaluation depend on the water depth distribution along the ... more Traditional methods for center pivot evaluation depend on the water depth distribution along the pivot arm. Estimation and mapping the water depth under pivot irrigation systems using remote sensing data is essential for calculating the coefficient of uniformity (CU) of water distribution. This study focuses on estimating and mapping water depth using Landsat OLI 8 satellite data integrated with Heerman and Hein (1968) modified equation for center pivot evaluation. Landsat OLI 8 image was geometrically and radiometrically corrected to calculate the vegetation and water indices (NDVI and NDWI) in addition to land surface temperature. Results of the statistical analysis showed that the collected water depth in catchment cans is also highly correlated negatively with NDVI. On the other hand water, depth was positively correlated with NDWI and LST. Multi-linear regression analysis using stepwise selection method was applied to estimate and map the water depth distribution. The results showed R 2 and adjusted R 2 0.93 and 0.88 respectively. Study area or field level verification was applied for estimation equation with correlation 0.93 between the collected water depth and estimated values.
Crop water requirements are represented by the actual crop evapotranspiration. Estimation of crop... more Crop water requirements are represented by the actual crop evapotranspiration. Estimation of crop evapotranspiration (ET c ) and crop coefficient using remote-sensing data is essential for planning the irrigation water use in arid and semiarid regions. This study focuses on estimating the crop coefficient (K c ) and crop evapotranspiration (ET c ) using SPOT-4 satellite data integrated with the meteorological data and FAO-56 approach. Reference evapotranspiration (ET o ) were estimated using FAO Penman-Monteith and tabled single crop coefficient values were adjusted to real values. SPOT-4 images geometrically and radio metrically corrected were used to drive the vegetation indices (NDVI and SAVI). Multi linear regression analysis was applied to develop the crop coefficient (K c ) prediction equations for the different growth stages from vegetation indices. The results showed R 2 were 0.82, 0.90 and 0.97 as well as adjusted R 2 were 0.80, 0.86 and 0.96 for developing, mid-season and late-season growth stage respectively.
Crop discrimination through satellite imagery is still problematic. Accuracy of crop classificati... more Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 -2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 -2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey's HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey's HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.
Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the ... more Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.
The current study aims to utilize the use of multi-remote sensing data for land use land cover ch... more The current study aims to utilize the use of multi-remote sensing data for land use land cover changes and trend analysis for the New Damietta city in Damietta governorate. Three different sensors were used in this study in different dates (SPOT-4 2007, SPOT-5 2011, and Kanopus-V1 2016). The FAO classification system (FAO-LCCS) was used to identify the different land use/cover classes in the study area. Results showed 13 main land use/land cover classes exist in the study area. The land use/land cover maps are produced for 2007, 2011 and 2016 with overall accuracies of 0.91, 0.92, 0.91 and kappa statistics of 0.88, 0.86 and 0.89 respectively. Results revealed that four different classes had a significant change over the study period. These classes are urban areas, cultivated lands, fish farms and bare areas. Trend analysis revealed that urban areas had the Original Research Article Arafat et al.; JGEESI, 14(3): 1-12, 2018; Article no.JGEESI.40132 2 highest increase rate (+2.76 km2/year short term & +2.73 km2/year long-term) while cultivated land and bare areas suffer from the highest decrease rates (-1 km 2 /year short and long-term,-1.54 km 2 /year short-term and-1.59 km 2 /year long term respectively).
The Egyptian Journal of Remote Sensing and Space Science
The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels tha... more The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of Agricultural statistics in surveying cropland. The use of remote sensing help in an accurate crop inventory under complex landscape conditions based on the spectral characteristics differences of crops. The current study was carried out in Abu El Matamir district, Behira Governorate, located in western Nile delta Egypt. The main objective of the current study is using time series analysis of remote sensing data in crop discrimination. In this study, 160 locations of ground truth points collected during different growth stages of summer season crops. Two different sensors images used in this study represented by single date image of RapidEye and multi-date Landsat 8 OLI satellites. The acquired satellite images from both sensors atmospherically and geometrically corrected. Moreover different vegetation indices calculated such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI) for cultivated crops in the study area during the whole growing season. Preliminary statistical analysis applied to the collected field data to show the distribution of the cultivated crops types. Moreover, unsupervised Iso-Data applied for multi-date Landsat 8 OLI images and calculated VI's series for overall growth season. Results showed higher overall kappa accuracy with 0.82 and 0.79 respectively. NDVI showed the best representation of the crop pheno-logical changes during the crop growth season and showed higher accuracy in strategic crops discrimination than the single date image with higher spatial resolution.
Hyperspectral ground measurements can be used for giving timely information about crops in specif... more Hyperspectral ground measurements can be used for giving timely information about crops in specific areas and thereby providing valuable data for decision makers. In the current study, ASD field Spec4 spectroradiometer were used to monitor the variation and differences of the summer crop vegetation cover reflectance. Furthermore, two hyperspectral vegetation indices calculated from the data represented by Normalized Difference Vegetation Index NDVI HS and Soil Adjusted Vegetation Index SAVI HS in east Nile Delta, Egypt. The results obtained showed that the mid-season stage had the highest values of calculated VI's that return to the high reflection from the plant canopy at the near infrared and high absorption at the red wavelength, also the initial growth stage VI's values lower than the mid-season stage and higher than the late season. In addition, the analysis of spectral signatures differences showed the late growth stage was the highest reflection overall the visible range (blue, green and red).
The spectral signatures of natural objects in the visible and near-infrared spectral range are in... more The spectral signatures of natural objects in the visible and near-infrared spectral range are influenced by the object's physical and biochemical properties. These signatures can be compiled in a database and used to retrieve information of land cover types and their physical composition from actual hyperspectral observations. This paper describes development process of hyperstectral database of reflectance from different land cover types in Egypt. It has been compiled from data obtained using a ground-based spectroradiometer system that covers the spectral range from 350 to 2500 nm at 1 nm resolution. The database is accessible through a website http://www.spectraldb.narss.sci.eg/spectral, where the system includes also metadata that describes the site environment and measurement processes. The system provides flexible mechanisms and friendly interfaces to allow accessing the database by the non-specialized people, whereas spectral data can be sorted by sites, species or selected environmental parameters. The system presents sample results from different vegetation and soil covers. Development of such a database is essential for different remote sensing applications , satellite's calibrations, data dissemination and linkage with other databases for scientific researches purposes .
The main objectives of this research is to determine the optimal hyperspectral range and waveband... more The main objectives of this research is to determine the optimal hyperspectral range and waveband/s in the spectral range of (400–2500 nm) to discriminate between four different varieties of Potato crop (Diamond, Everest, Mondial and Rosetta) that are cultivated in old and newly cultivated lands of Egypt and to propose detailed spectral reflectance characterization for these four varieties which will enable more accurate surveying of these varieties through satellite imagery. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the crop. An average of thirty measurements for each variety was considered in the process. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400–2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey's HSD analysis was used to choose the optimal spectral zone that could be used to differentiate between the four varieties. Then, linear discrimination analysis (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each variety could be spectrally identified. The results of Tukey's HSD showed that NIR is the best spectral zone for the discrimination between the four varieties. The other five spectral zones showed close spectral characterizations between at least two varieties. The results of (LDA) showed the optimal waveband to identify each variety. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to estimate potato crop acreage. The study was carried out in AlBuhayrah governorate of Egypt.
Traditional methods for center pivot evaluation depend on the water depth distribution along the ... more Traditional methods for center pivot evaluation depend on the water depth distribution along the pivot arm. Estimation and mapping the water depth under pivot irrigation systems using remote sensing data is essential for calculating the coefficient of uniformity (CU) of water distribution. This study focuses on estimating and mapping water depth using Landsat OLI 8 satellite data integrated with Heerman and Hein (1968) modified equation for center pivot evaluation. Landsat OLI 8 image was geometrically and radiometrically corrected to calculate the vegetation and water indices (NDVI and NDWI) in addition to land surface temperature. Results of the statistical analysis showed that the collected water depth in catchment cans is also highly correlated negatively with NDVI. On the other hand water, depth was positively correlated with NDWI and LST. Multi-linear regression analysis using stepwise selection method was applied to estimate and map the water depth distribution. The results showed R 2 and adjusted R 2 0.93 and 0.88 respectively. Study area or field level verification was applied for estimation equation with correlation 0.93 between the collected water depth and estimated values.
Crop water requirements are represented by the actual crop evapotranspiration. Estimation of crop... more Crop water requirements are represented by the actual crop evapotranspiration. Estimation of crop evapotranspiration (ET c ) and crop coefficient using remote-sensing data is essential for planning the irrigation water use in arid and semiarid regions. This study focuses on estimating the crop coefficient (K c ) and crop evapotranspiration (ET c ) using SPOT-4 satellite data integrated with the meteorological data and FAO-56 approach. Reference evapotranspiration (ET o ) were estimated using FAO Penman-Monteith and tabled single crop coefficient values were adjusted to real values. SPOT-4 images geometrically and radio metrically corrected were used to drive the vegetation indices (NDVI and SAVI). Multi linear regression analysis was applied to develop the crop coefficient (K c ) prediction equations for the different growth stages from vegetation indices. The results showed R 2 were 0.82, 0.90 and 0.97 as well as adjusted R 2 were 0.80, 0.86 and 0.96 for developing, mid-season and late-season growth stage respectively.
Crop discrimination through satellite imagery is still problematic. Accuracy of crop classificati... more Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 -2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 -2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey's HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey's HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.
Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the ... more Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.