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Ishfaq Ahmad Umer Saeed Muhammad Fahad Asmat Ullah M. Habib ur Rahman Ashfaq Ahmad Jasmeet Judge 《Journal of the Indian Society of Remote Sensing》2018,46(10):1701-1711
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha?1, while remote sensing showed the RMSE of 397 kg ha?1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool. 相似文献
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Muhammad Khurshid Mohammad Nafees Abdullah Khan Mehmet Somuncu Ashfaq Ahmad Khan Wajid Rashid 《地理学报(英文版)》2019,29(10):1758-1770
Journal of Geographical Sciences - Pastoralism is a viable socio-economic system-shaped by landless and agro-pastoral communities in many pastoral regions of the world. This system is mainly based... 相似文献
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Mudimu George T. Zuo Ting Shah Ashfaq Ahmad Nalwimba Nkumbu Ado Abdou Matsalabi 《GeoJournal》2021,86(6):2927-2943
GeoJournal - Despite the Zimbabwean State’s narrative and discourse that in fast track land reform areas ‘land leasing is illegal’, there is a surge in land leasing. This article... 相似文献
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Shah Ashfaq Ahmad Wu Wenya Gong Zaiwu Pal Indrajit Khan Jahangir 《Natural Hazards》2021,105(2):1977-2005
Natural Hazards - Children spend more than two-thirds of their total daytime in schools and becoming more persuasive in shielding them from potential hazards. Schools have a responsibility to... 相似文献
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Batibeniz Fulden Ashfaq Moetasim &#;nol Bar&#;&#; Turuncoglu Ufuk Utku Mehmood Shahid Evans Katherine J. 《Climate Dynamics》2020,54(9):4109-4127
Climate Dynamics - We employ a Lagrangian based moisture back trajectory method on an ensemble of four reanalysis datasets to provide a comprehensive understanding of moisture sources over the... 相似文献
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Khawar Ashfaq AHMED Sarfraz KHAN Mahmood SULTAN UMAIR Bin Nisar Kalim ULLAH Al -Hseinat MU’AYYAD 《《地质学报》英文版》2019,93(6):1711-1720
The precise seismic substructural interpretation of the Turkwal oil field in the Central Potwar region of district Chakwal of Pakistan has been carried out. The research work was confined to the large fore-thrust that serves as an anticlinal structural trap through ten 2D seismic lines. A precise seismic substructural model of the Eocene Chorgali Limestone with precise orientation of thrust and oblique slip faults shows the presence of a huge fracture, which made this deposit a good reservoir. The abrupt surface changes in dip azimuth for the Eocene Chorgali Limestone verifies the structural trends and also the presence of structural traps in the Turkwal field. The logs of three wells (Turkwal deep X-2, Turkwal-01 and Fimkassar-01) were analyzed for petrophysical studies, well synthetic results and generation of an Amplitude Versus Offset (AVO) model for the area. The AVO model of Turkwal deep X-2 shows abrupt changes in amplitude, which depicts the presence of hydrocarbon content. Well correlation technique was used to define the overall stratigraphic setting and the thickness of the reservoir formation in two wells, Turkwal-01 and Turkwal deep X-2. The Eocene Chorgali Limestone in Turkwal-01 is an upward thrusted anticlinal structure and because of the close position of both wells to the faulted anticlinal structure, its lesser thickness differs compared to Turkwal deep X-2. The overall results confirm that the Turkwal field is comparable to several similar thrust-bound oil-bearing structures in the Potwar basin. 相似文献
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Natural Hazards - Integrated disaster risk management in a changing climate is a key concern for disaster reduction and global sustainable development now and in the future. This study conducted... 相似文献
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Influence of SST biases on future climate change projections 总被引：1，自引：0，他引：1
We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection. 相似文献
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Farhat Abbas Ashfaq Ahmad Mohammad Safeeq Shafaqat Ali Farhan Saleem Hafiz Mohkum Hammad Wajid Farhad 《Theoretical and Applied Climatology》2014,116(3-4):671-680
Asymmetrical monsoons during the recent past have resulted into spatially variable and devastating floods in South Asia. Analysis of historic precipitation extremes record may help in formulating mitigation strategies at local level. Eleven indices of precipitation extremes were evaluated using RClimDex and daily time series data for analysis period of 1981–2010 from five representative cities across Punjab province of Pakistan. The indices include consecutive dry days, consecutive wet days, number of days above daily average precipitation, number of days with precipitation ≥10 mm, number of days with precipitation ≥20 mm, very wet days, extremely wet days, simple daily intensity index, maximum 1-day precipitation quantity, maximum 5 consecutive day precipitation quantity, and annual total wet-day precipitation. Mann-Kendall test and Sen’s slope extremes were used to detect trends in indices. Droughts and excessive precipitation were dictated by elevation from mean sea level with prolonged dry spells in southern Punjab and vice versa confirming spatial trends for precipitation extremes. However, no temporal trend was observed for any of the indices. Summer in the region is the wettest season depicting contribution of monsoons during June through August toward devastating floods in the region. 相似文献