Research
Spectral Characteristics and Mapping of Rice Fields using Multi-Temporal Landsat and MODIS Data: A Case of District Narowal
Availability of remote sensed data provides powerful access to the spatial and temporal information of the earth surface. Real-time earth observation data acquired during a cropping season can assist in assessing crop growth and development performance. As remote sensed data is generally available at large scale, rather than at field-plot level, use of this information would help to improve crop management at broad-scale. Utilizing the Landsat TM/ETM+ ISODATA clustering algorithm and MODIS (Terra) the normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) datasets allowed the capturing of relevant rice cropping differences. In this study, we tried to analyze the MODIS (Terra) EVI/NDVI (February, 2000 to February, 2013) datasets for rice fractional yield estimation in Narowal, Punjab province of Pakistan. For large scale applications, time integrated series of EVI/NDVI, 250-m spatial resolution offer a practical approach to measure crop production as they relate to the overall plant vigor and photosynthetic activity during the growing season. The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS/Sinusoidal to the national coordinate systems. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results and will support environmental planning to develop sustainable land-use practices. These results have important implications for parameterization of land surface process models using biophysical variables estimated from remotely sensed data and assist for forthcoming rice fractional yield assessment.
Leptochloa Fusca Cultivation for Utilization of Salt-Affected Soil and Water Resources in the Cholistan Desert
In the Cholistan Desert, 0.44 million ha are salt-affected low lying and clayey in nature locally known as 'dhars', where rainwater as well as saline groundwater could be utilized for growing salt grasses like Leptochloa fusca as forage during summer. L. fusca is a promising candidate grass for economic utilization and better management of sodic, high pH, saline soil and water resources of the Cholistan Desert. L. fusca is known to be a versatile, halophytic, primary colonizer, easily propagatable, perennial, nutritive and palatable forage plant species. The grass has the good biomass producing potential and can grow equally well both under upland and submerged saline soil environment.
Run-Off Farming in Reducing Rural Poverty in the Cholistan Desert
This study provides an overview of the potential impact of employing indigenous rainwater- harvesting technology in alleviating poverty in the Cholistan Desert of Pakistan. Ideal characteristics for run-off farming catchments result from the combination of landforms and soil properties. Many soils in the region exhibit low to very low infiltration and high levels of run-off. It has been demonstrated that there is a direct relationship between water availability and poverty reduction. This study outlines both the advantages and disadvantages of the indigenous rainwater-harvesting technology in reducing rural poverty and recommends its use with modern water harvesting techniques
The Utilization of MODIS and Landsat TM/ETM+ for Cotton Fractional Yield Estimation in Burewala
Estimates of crop yield are desirable for managing agricultural lands. Remote sensing is the one technology that can give an unbiased view of large areas, with spatially explicit information distribution and time repetition, and has thus been widely used to estimate crop yield and offers great potential for monitoring production, yet the uncertainties associated with large-scale crop yield estimates are rarely addressed. In this study, we tried to estimate cotton cropped area using the supervised classification; planting dates for 11 years (1998 to 2009) of Landsat imagery, and fractional yield using MODIS (Terra) the normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) in an intensive agricultural region of Burewala, Punjab province of Pakistan. Vegetation indices are widely used for assessing and monitoring ecological variables such as vegetation cover and above-ground biomass. Monitoring the spatial distribution of cotton yield helps identifying sites with yield constraints. The newly available satellite images from the MODIS sensor provide enhanced atmospheric correction, cloud detection, improved geo-referencing, comprehensive data quality control and the enhanced ability to monitor vegetation development. The high temporal resolution of the MODIS datasets can provide an efficient and consistent way for biomass and fractional yield monitoring and assessment. The reflected radiation provides an indication of the type and density of canopy. The condition, distribution, structure and the development of the vegetation through the phenological stages can affect the relation between yield and NDVI. The high spatial resolution Landsat images were applied to extract the area under cotton cultivation within the landscape and to determine the cotton fraction among other land uses within the coarse spatial resolution MODIS pixels.
