Muhammad Usman

Bio: Muhammad Usman is an academic researcher from University of Würzburg. The author has contributed to research in topics: Irrigation & Groundwater recharge. The author has an hindex of 13, co-authored 40 publications receiving 534 citations. Previous affiliations of Muhammad Usman include Martin Luther University of Halle-Wittenberg & Dresden University of Technology.

Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

Abstract: Detailed analysis of Land Use/Land Cover (LULC) using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three different techniques: error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coefficients (K). The average overall accuracies for rabi and kharif are 82.83% and 78.21%, respectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation between cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

Effect of Nitrogen on Yield and Oil Quality of Sunflower (Helianthus Annuus L.) Hybrids under Sub Humid Conditions of Pakistan

Abstract: Sunflower (Helianthus annuus L.) has emerged as an economically important crop in Pakistan due to its significant share in vegetable oil production. The plant metabolic processes require protein to increase the vegetative, reproductive growth and yield of the crop. The protein is wholly dependent upon the amount of nitrogen fertilization available for plant use. A two-year field study was conducted in 2008 and 2009. The objective was to determine the effect of different nitrogen (N) levels (N1 = 0 kg·ha–1, N2 = 60 kg·ha–1, N3 = 120 kg·ha–1, N4 = 180 kg·ha–1 and N5 = 240 kg·ha–1) on three sunflower hybrids (Hysun-33, Hysun-38 and Poineer-64A93) in agro-climatic conditions of Gujranwala, a sub-humid region in the centre of the Punjab province of Pakistan. A randomized complete block design split plot experiment was set-up with cultivars in the main plots and N levels in the subplots. Results showed that Hysun-38 gave maximum TDM (15815 kg·ha–1) and maximum grain yield (3389 kg·ha–1), while minimum TDM (14640 kg·ha–1) and grain yield (3125 kg·ha–1) was observed in Hysun-33. Among different N rates evaluated, N4 gave maximum TDM (17890 kg·ha–1) and grain yield (3809 kg·ha–1) compared to the other N rates. The maximum oil content (46.2%) was observed in Hysun-38 without application of N fertilizer (N1), while the minimum oil content (40.6%) was observed from N5 treatment. In conclusion, the application of 180 kg·ha–1 N to Hysun-38 provided the best combination for good yield in sunflower crop under the prevailing sub-humid conditions of Pakistan.

An overview of flood mitigation strategy and research support in South Asia: implications for sustainable flood risk management

Abstract: South Asia faces increasing flooding risks due to climatic and socio-economic changes. Various measures have been adopted by the governments of the countries in this region. However, these measures are not adequate to protect the vulnerable communities from ever-increasing flood losses. This study assesses the policy and planning processes and flood-related scientific research in India, Pakistan and Bangladesh. Based on a systematic review, a comparison of the existing flood management systems of the three countries is undertaken, and a framework for sustainable flood management in the region is suggested. Insights from the literature show that Bangladesh has been able to develop an effective governance structure to address flood hazards, while Pakistan’s approach to flood management planning is found to be largely inadequate. This inadequacy is mainly attributed to missing links in policy formulation and planning processes, along with a lack of institutional coordination. The results of the literature an...

Development and application of crop water stress index for scheduling irrigation in cotton (Gossypium hirsutum L.) under semiarid environment.

Abstract: Studies were conducted to determine the crop water stress index (CWSI) for irrigation scheduling of cotton crop under irrigated semiarid environment. The investigations were carried out at the Post Graduate Agriculture Research Station (PARS), University of Agriculture, Faisalabad (UAF), Pakistan (latitude 31°25'N, longitude 73°09'E and altitude 184.4 m from sea level), during summer season 2006. Five treatments with different irrigation conditions were managed under randomized complete block design (RCBD). These treatments included T 0 no irrigation except rainfall (NI); T 1 irrigation at vegetation stage (VS-1); T 2 irrigation at vegetation and flowering stage (VS-1 + FS-1); T 3 irrigation at vegetation, flowering and boll formation stage (VS-1 +FS-1 +BF-1) and T 4 irrigation at vegetation, flowering, boll formation and at late stage (VS-1 +FS-1 + BF-1 + LS-1). Lower and upper baselines were developed for the cotton crop. For developing upper baseline (fully water-stressed), cotton was grown as a separate treatment where irrigation application was restricted. Lower baseline was developed by using canopy and air temperatures attained on clear sky days with 5-8 days of irrigation and rainfall application to field. The seasonal CWSI values for each irrigation treatment were calculated as the average for the entire season. The mean value of CWSI for treatment T o , T 1 , T 2 , T 3 and T 4 were 0.76, 0.60, 0.42, 0.28 and 0.24, respectively. The trends in CWSI were consistent with moisture content in the soil. The relationship between yield and seasonal mean CWSI values was primarily linear: Y = -2320.3CWSI + 3048.5 (r 2 = 0.95, r = 0.97, SE = 0.05, P<0.01). This relation can be used to predict the yield of cotton, and data thus generated will be beneficial for further research.

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

Statistical Methods For Environmental Pollution Monitoring

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Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Abstract: Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review

Abstract: As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (Ts) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between Ts and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. Ts increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on Ts; at the vegetation level, Ts is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.