Rice (Oryza sativa) is the second staple food largely grown and widely consumed in Pakistan. About 10% of the total crop area of Pakistan is cultivated by rice that takes a part in value addition of almost 1.3% - 1.6%...Rice (Oryza sativa) is the second staple food largely grown and widely consumed in Pakistan. About 10% of the total crop area of Pakistan is cultivated by rice that takes a part in value addition of almost 1.3% - 1.6% in the total Gross Domestic Product (GDP). Due to global warming, temperature has a profound impact on rice crop phenology. Low temperature is the main factor of delay in rice plant growth and very high temperature results in stressed and short heighted plant so the crop sown in a region at the same time is not ready to harvest at same hours but a delay is observed. The study area under investigation was district Sheikhupura, Nankana, Lahore, Gujranawala and Hafizabad, which are famous for rice productivity. Landsat 7, 8 freely available thermal dataset are used to calculated pixel based temperature values to evaluate growth using agricultural growth indicators. The total covered area was 13,480 km2 in which 484 km2 area was marked as less growth rate area with low temperature values due to water body and excess of vegetation over there. About 7960 km2 area is marked as good for growth experiencing optimum temperature for rice plant. Approximately 4944 km2 area is marked as stressed rice plant area experiencing high temperature values adjacent to urban population. An attempt is made here to map this effect of temperature-based growth variability of the rice plant across the study area.展开更多
Augmented reality(onwards,AR)technologies are now much more complex and feature a higher number of details thanks to advancements in information and communication technologies(ICTs).Today’s systems can be easily adap...Augmented reality(onwards,AR)technologies are now much more complex and feature a higher number of details thanks to advancements in information and communication technologies(ICTs).Today’s systems can be easily adapted and packaged into smartphone apps which enable a wide range of applications in real and clinical practice.The impact on behavioral health treatments,rehabilitation and healthcare system in general is just beginning to become evident[1]but still,more research providing evidence-based practices is required.展开更多
In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing tech...In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing technology.This study was conducted by analysing the Landsat 8 satellite images,soil data of field surveys,laboratory analyses and statistical computations.Soil properties such as soil moisture and soil salinity were estimated using soil moisture index(SMI)and soil salinity index(SSI),respectively.The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index(SpHI).They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes.All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis.Soil moisture increased after 30 days,while the salt content was only trace amounts.Both proposed models detected 4.49–7.59 of soil pH,4.66 in bare soil model and 6.62 in paddy leaf model.During the planting period,the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24%of accuracy.The spatial relationship between soil pH,soil salinity and soil moisture are linear but varied in correlation level from weak,moderate to strong.Based on the bare soil model,the relationship between soil pH and soil moisture shows a weak negative relationship with R28.37%and a strong positive relationship with R281.94%in paddy area and bare soil area respectively,as like as in paddy area based on the paddy leaf model with R2100%.The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R234.89%.展开更多
Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the environment.Therefore, the ability to p...Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the environment.Therefore, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.展开更多
文摘Rice (Oryza sativa) is the second staple food largely grown and widely consumed in Pakistan. About 10% of the total crop area of Pakistan is cultivated by rice that takes a part in value addition of almost 1.3% - 1.6% in the total Gross Domestic Product (GDP). Due to global warming, temperature has a profound impact on rice crop phenology. Low temperature is the main factor of delay in rice plant growth and very high temperature results in stressed and short heighted plant so the crop sown in a region at the same time is not ready to harvest at same hours but a delay is observed. The study area under investigation was district Sheikhupura, Nankana, Lahore, Gujranawala and Hafizabad, which are famous for rice productivity. Landsat 7, 8 freely available thermal dataset are used to calculated pixel based temperature values to evaluate growth using agricultural growth indicators. The total covered area was 13,480 km2 in which 484 km2 area was marked as less growth rate area with low temperature values due to water body and excess of vegetation over there. About 7960 km2 area is marked as good for growth experiencing optimum temperature for rice plant. Approximately 4944 km2 area is marked as stressed rice plant area experiencing high temperature values adjacent to urban population. An attempt is made here to map this effect of temperature-based growth variability of the rice plant across the study area.
文摘Augmented reality(onwards,AR)technologies are now much more complex and feature a higher number of details thanks to advancements in information and communication technologies(ICTs).Today’s systems can be easily adapted and packaged into smartphone apps which enable a wide range of applications in real and clinical practice.The impact on behavioral health treatments,rehabilitation and healthcare system in general is just beginning to become evident[1]but still,more research providing evidence-based practices is required.
文摘In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing technology.This study was conducted by analysing the Landsat 8 satellite images,soil data of field surveys,laboratory analyses and statistical computations.Soil properties such as soil moisture and soil salinity were estimated using soil moisture index(SMI)and soil salinity index(SSI),respectively.The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index(SpHI).They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes.All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis.Soil moisture increased after 30 days,while the salt content was only trace amounts.Both proposed models detected 4.49–7.59 of soil pH,4.66 in bare soil model and 6.62 in paddy leaf model.During the planting period,the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24%of accuracy.The spatial relationship between soil pH,soil salinity and soil moisture are linear but varied in correlation level from weak,moderate to strong.Based on the bare soil model,the relationship between soil pH and soil moisture shows a weak negative relationship with R28.37%and a strong positive relationship with R281.94%in paddy area and bare soil area respectively,as like as in paddy area based on the paddy leaf model with R2100%.The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R234.89%.
基金The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the projects PID2020-117954RB-C21 and TED2021-131311B-C22.
文摘Floods remain one of the most devastating weather-induced disastersworldwide, resulting in numerous fatalities each year and severelyimpacting socio-economic development and the environment.Therefore, the ability to predict flood-prone areas in advance is crucialfor effective risk management. The objective of this research is to assessand compare three convolutional neural networks, U-Net, WU-Net, andU-Net++, for spatial prediction of pluvial flood with a case study at atropical area in the north of Vietnam. They are relative new convolutionalgorithms developed based on U-shaped architectures. For this task, ageospatial database with 796 historical flood locations and 12 floodindicators was prepared. For training the models, the binary crossentropywas employed as the loss function, while the Adaptive momentestimation (ADAM) algorithm was used for the optimization of themodel parameters, whereas, F1-score and classification accuracy (Acc)were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models,achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and informationto enhance decision-making processes and facilitate the implementation of effective risk management strategies.
