Research
Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari
The hyperspectral image analysis technique, one of the most advanced remote sensing tools, has been used as a possible means of identifying from a single pixel or in the field of view of the sensor. An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel, endmember, and a set of corresponding fractions of the spectral signature in the pixel, abundances, and this problem is known as un-mixing. The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation, extracted from the spectral endmember selection procedures, including the reflectance calibration of Landsat ETM+ image using ENVI software, minimum noise fraction (MNF), pixel purity index (PPI), and n-dimensional visualization. The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation, an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene. The endmember extraction tendency to the type of endmembers being derived, and the number of endmembers, estimated by an algorithm, with respect to the number of spectral bands, and the number of pixels being processed, also the required input data, and the kind of noise, if any, in the signal model surveying done. Results of the present study using the hyperspectral image analysis technique ascertain that Landsat ETM+ data can be used to generate valuable vegetative information for the District Vehari, Punjab Province, Pakistan.
Phenologically-Tuned MODIS NDVI-Based Time Series (2000-2012) For Monitoring Of Vegetation and Climate Change in North-Eastern Punjab, Pakistan
One of the main factors determining the daily variation of the active surface temperature is the state of the vegetation cover. It can well be characterized by the Normalized Difference Vegetation Index (NDVI). The NDVI has the potential ability to signal the vegetation features of different eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. The vegetation phenology is the expression of the seasonal cycles of plant processes and contributes vital current information on vegetation conditions and their connections to climate change. The NDVI is computed using near-infrared and red reflectances, and thus has both an accuracy and precision. A gapless time series of MODIS NDVI (MOD13A1) composite raster data from 18th February, 2000 to 16th November, 2012 with a spatial resolution of 500 m was utilized. Time-series terrestrial parameters derived from NDVI have been extensively applied to global climate change, since it analyzes each pixel individually without the setting of thresholds to detect change within a time series.
Landsat ETM+ and MODIS EVI/NDVI Data Products for Climatic Variation and Agricultural Measurements in Cholistan Desert
The landsat ETM+ has shown great potential in agricultural mapping and monitoring due to its advantages over traditional receive procedures in terms of cost effectiveness and timeliness in availability of information over larger areas and ingredient the temporal dependence of multitemporal image data to identify the changing pattern of vegetation cover and consequently enhance the interpretation capabilities. Integration of multi-sensor and multitemporal satellite data effectively improves the temporal attribute and accuracy of the results. Since 2000, NASA's MODIS sensors (onboard Terra satellite) has provided composite data at 16- days interval to produce estimates of gross primary production (GPP) that compare well with direct measurements. The MODIS Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) which are independent of climatic drivers, also appears as valuable surrogate for estimation of seasonal patterns in GPP.
Detection of Change in Vegetation Cover Using Multi-Spectral and Multi-Temporal Information for District Sargodha
Detection of change is the measure of the distinct data framework and thematic change information that can direct to more tangible insights into underlying process involving land cover and landuse changes. Monitoring the locations and distributions of land cover changes is important for establishing links between policy decisions, regulatory actions and subsequent landuse activities. Change detection is the process that helps in determining the changes associated with landuse and land cover properties with reference to geo-registered multi-temporal remote sensing information. It assists in identifying change between two or more dates that is uncharacterized of normal variation. After image to image registrations, the normalized difference vegetation index (NDVI), the transformed normalized differ-rence vegetation index (TNDVI), the enhanced vegetation index (EVI) and the soil-adjusted vegetation index (SAVI) values were derived from Landsat ETM+ dataset and an image differencing algorithm was applied to detect changes. This paper presents an application of the use of multi-temporal Landsat ETM+ images and multi-spectral MODIS (Terra) EVI/NDVI time-series vegetation phenology metrics for the District Sargodha. The results can be utilized as a temporal landuse change model for Punjab province of Pakistan to quantify the extent and nature of change and assist in future prediction studies. This will support environmental planning to develop sustainable landuse practices.
Land Degradation Pattern Using Geo-Information Technology for Kot Addu, Punjab Province, Pakistan
One of the most important global phenomena that are currently threatening the ecosystem is land degradation and is mainly caused by the climatic changes and human influence. Land degradation is the reduction in the capability of the land to produce benefits from a particular land use under a specified form of land management. Land degradation is the consequence of important processes, which is active in arid and semi-arid ecosystems, where water is the original limiting factor in execution of land application. Remotely sensed data provide timely, accurate and reliable information on degraded lands at definite time intervals in a cost effective manner. In this research, the TM/ETM+ images were used to study changes occurred in the first decade of the new millennium; May 2001 to April 2011. In the present study, efforts have been made to identify and map areas affected by land degradation in Kot Addu tehsil of Muzaffargarh, Punjab province, Pakistan. The Normalized Difference Vegetation Index (NDVI), change detection technique was applied upon TM/ETM+ images and further unsupervised classification was used for extraction of information regarding the desert, bare soil, cultivatable land and cultivated land. The NDVIs properties help mitigate a large part of the variations that result from the overall remotesensing system. The result shows that the desert is 458.73 km2 (17%), bare soil is 1160.33 km2 (43%), cultivated land is 647.62 km2 (24%) and cultivatable land is 431.75 km2 (16%) in April 2011. The values of the Kappa statistics were used to compare the performance of the classifiers. The data sets were analyzed using ArcGIS software in the Geographic Information System environment and can be implemented in the drylands of Pakistan.
NOAA AVHRR NDVI/MODIS NDVI PredActs PotentAal to Forest Resource Management in Aatalca DAstrAct of Turkey
Çatalca, located on the ridge between the Marmara and the Black Sea, is a rural district of Istanbul having the temperate climate. Landuse involves farming and forestry. This study makes a contribution and revises the applicability of two medium spatial resolution satellite sensors, NOAA AVHRR NDVI and MODIS (Terra) NDVI, for prediction to potential forest resource management in Çatalca district of Turkey on various spatial scales. The NOAA AVHRR NDVI sensor was chosen in view of its unique value for long-term climate impact studies. The MODIS (Terra) sensor, as a newer generation sensor specifically designed for, inter alia, terrestrial applications, since it provides the opportunity for observations at higher spatial and spectral resolution compared to NOAA AVHRR (NDVI). The required data preparation for the integration of MODIS data into GIS is described with a focus on the projection from the MODIS/Sinusoidal projection to the national coordinate systems. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. This paper summarizes a set of remote sensing applications of NOAA AVHRR NDVI/MODIS (Terra) NDVI datasets in estimation and monitoring of seasonal and inter annual ecosystem dynamics which were designed for forest resource management and can be implemented over Turkey.
