An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

Accuracy improvement of ASTER stereo satellite generated DEM using texture filter

The grid DEM(digital elevation model) generation can be from any of a number of sources:for instance,analogue to digital conversion of contour maps followed by application of the TIN model,or direct elevation point modelling via digital photogrammetry applied to airborne images or satellite images.Currently,apart from the deployment of point-clouds from LiDAR data acquisition,the generally favoured approach refers to applications of digital photogrammetry.One of the most important steps in such deployment i...     

Geo-scape,a granularity depended spatialization tool for visualizing multidimensional data sets

Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of gran...     

Extraction of Alteration Mineral Information from Moderate Remote Sensing Images Using MPS Method

Although alteration minerals related to metallogenesis is very important in mineral exploration, information of alteration mineral is weakly expressed in remote sensing imagery, which is often subject to interfering noise and sometimes limited in spectral and spatial resolutions. Because of easy access, moderate images are the main sources of alteration mineral information. Therefore, it is very important to develop alteration mineral information extraction methods from remote sensing images. In this paper, a combined method based on Mask, principal component analysis (PCA) and support vector machine method (SVM) was used to extract alteration mineral information from Enhanced thematic mapper plus remote sensing data with limited spectral and spatial resolutions. First, a mask image of the remote sensing imagery was created to remove interference information such as vegetation, shadow and water. Then, PCA was employed to collect sample data relating to iron, argillic, and carbonatization alteration. Finally, SVM was used to deal with alteration anomaly and build a feature extraction model of high accuracy. The Mask-PCA-SVM model is used to extract alteration mineral information from remote sensing images of Hatu area, Xinjiang Uygur Autonomous Regions, China. The results show that the new methods proposed in this paper can coincide well with known deposits occurrences, rate reached 86.51%. While, the consistent rate with known deposits of the ratio model, PCA model and Spectral angle mapper model were only 3.37, 65.08 and 69.05% respectively. This suggests that the proposed model can find the actual distribution of mineral deposits more effectively by reducing interference to a greater degree.     

A Stochastic Approach to Estimate Distribution of Built-Up Area in Regions with Thick Tree Cover

Buildings and other human-made constructions have been accepted as an indicator of human habitation and are identified as built-up area. Identification of built-up area in a region and its subsequent measurement is a key step in many fields of studies like urban planning, environmental studies, and population demography. Remote sensing techniques utilising medium resolution images (e.g. LISS III, Landsat) are extensively used for the extraction of the built-up area as high-resolution images are expensive, and its processing is difficult. Extraction of built land use from medium resolution images poses a challenge in regions like Western-Ghats, North-East regions of India, and countries in tropical region, due to the thick evergreen tree cover. The spectral signature of individual houses with a small footprint are easily overpowered by the overlapping tree canopy in a medium resolution image when the buildings are not clustered. Kerala is a typical case for this scenario. The research presented here proposes a stochastic-dasymetric process to aid in the built-up area recognition process by taking Kerala as a case study. The method utilises a set of ancillary information to derive a probability surface. The ancillary information used here includes distance from road junctions, distance from road network, population density, built-up space visible in the LISS III image, the population of the region, and the household size. The methodology employs logistic regression and Monte Carlo simulation in two sub processes. The algorithm estimates the built-up area expected in the region and distributes the estimated built-up area among pixels according to the probability estimated from the ancillary information. The output of the algorithm has two components. The first component is an example scenario of the built-up area distribution. The second component is a probability surface, where the value of each pixel denotes the probability of that pixel to have a significant built-up area within it. The algorithm is validated for regions in Kerala and found to be significant. The model correctly predicted the built-up pixel count count over a validation grid of 900 m in 95.2% of the cases. The algorithm is implemented using Python and ArcGIS.     

Analysis of the Seasonal Variation of CO<Subscript>2</Subscript> Concentration in China Based on GOSAT Satellite Data

Carbon dioxide (CO₂) is one of the major gases that contribute to the global warming. Therefore, studying the distribution of CO₂ can help people understand the carbon cycle. Based on the GOSAT retrieved CO₂ products, the temporal and spatial distribution and seasonal variation of CO₂ concentration were analyzed from 2011 to 2015. CO₂ concentration has obvious seasonal variation. It was low in summer, and was high in spring, and the annual increase was about 2 ppm. Nevertheless, the annual growth rate of CO₂ concentration in summer was higher than that in spring, it was 0.5425% in summer and was 0.46% in spring. CO₂ concentration was low in the northwest and was high in the southeast. The growth rate of CO₂ was 2.8 ppm in the northwest and was 3.42 ppm in the southeast. More human’s activities made CO₂ concentration higher in the southeast than that in other regions.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

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.     

Accuracy Assessment of Digital Elevation Model Generated by SAR Stereoscopic Technique Using COSMO-Skymed Data

Radargrammetry technique using the stereoscopic synthetic aperture radar (SAR) images is used for the generation of a digital elevation model (DEM) of a region requires only the amplitude images. SAR stereoscopic technique is analogous to the stereo-photogrammetric technique where the optical stereoscopic images are used for DEM generation. While the advantages of the SAR images are their indifference to atmospheric transparency and solar illumination conditions, the side-looking geometry of the SAR increases the complexity in the SAR stereo analysis. The availability of high spatial and temporal resolution SAR data in recent years has facilitated generation of high-resolution DEM with greater vertical accuracy using radargrammetric technique. In the present study, attempt has been made to generate the DEM of Dehra Dun region, India, from the COSMO-Skymed X-band SAR data-pair acquired at 8 days interval through the radargrammetry technique. Here, radargrammetric orientation approach has been adopted to generate the DEM and various issues and processing steps with the radargrammetry technique have been discussed. The DEM was validated with ground measured elevation values using a differential global positioning system and the root-mean-square error of the DEM was found as 7.3 m. The DEM was compared with the reference DEM of the study area generated from the Cartosat-1 stereo data with a model accuracy of 4 m.     

Study on Sampling Rule and Simplification of LiDAR Point Cloud Based on Terrain Complexity

It is difficult to obtain digital elevation model (DEM) in the mountainous regions. As an emerging technology, Light Detection and Ranging (LiDAR) is an enabling technology. However, the amount of points obtained by LiDAR is huge. When processing LiDAR point cloud, huge data will lead to a rapid decline in data processing speed, so it is necessary to thin LiDAR point cloud. In this paper, a new terrain sampling rule had been built based on the integrated terrain complexity, and then based on the rule a LiDAR point cloud simplification method, which was referred as to TCthin, had been proposed. The TCthin method was evaluated by experiments in which XUthin and Lasthin were selected as the TCthin’s comparative methods. The TCthin’s simplification degree was estimated by the simplification rate value, and the TCthin’s simplification quality was evaluated by Root Mean Square Deviation. The experimental results show that the TCthin method can thin LiDAR point cloud effectively and improve the simplification quality, and at 5 m, 10 m, 30 m scale levels, the TCthin method has a good applicability in the areas with different terrain complexity. This study has theoretical and practical value in sampling theory, thinning LiDAR point cloud, building high-precision DEM and so on.