Remote sensing applications: an overview

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives

Development of decadal (1985–1995–2005) land use and land cover database for India

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study

Assessing the transferability of machine learning algorithms using cloud computing and earth observation datasets for agricultural land use/cover mapping

Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management

Data mining techniques on satellite images for discovery of risk areas

The high rates of cholera epidemic mortality in less developed countries is a challenge for health fa- cilities to which it is necessary to equip itself with the epidemiological surveillance. To strengthen the capacity of epidemiological surveillance, this paper focuses on remote sensing satellite data processing using data mining methods to discover risk areas of the epidemic disease by connecting the environ- ment, climate and health. These satellite data are combined with field data collected during the same set of periods in order to explain and deduct the causes of the epidemic evolution from one period to another in relation to the environment. The existing technical (algorithms) for processing satellite im- ages are mature and efficient, so the challenge today is to provide the most suitable means allowing the best interpretation of obtained results. For that, we focus on supervised classification algorithm to process a set of satellite images from the same area but on different periods. A novel research method- ology (describing pre-treatment, data mining, and post-treatment) is proposed to ensure suitable means for transforming data, generating information and extracting knowledge. This methodology consists of six phases: (1.A) Acquisition of information from the field about epidemic, (1.B) Satellite data acquisition, (2) Selection and transformation of data (Data derived from images), (3) Remote sensing measurements, (4) Discretization of data, (5) Data treatment, and (6) Interpretation of results. The main contributions of the paper are: to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic. This will allow national governments, local authorities and the public health officials to effective management according to risk areas. The case study concerns the knowledge discovery in databases related to risk areas of the cholera epidemic in Mopti region, Mali (West Africa). The results generate from data mining association rules indicate that the level of the Niger River in the wintering periods and some societal factors have an impact on the variation of cholera epidemic rate in Mopti town. More the river level is high, at 66% the rate of contamination is high

Geospatial Modelling of Ungulate Habitat Relationships in Tadoba-Andhari Tiger Reserve, Maharashtra

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Assesing the spatial transferability of Random Forest models trained on the basis of the WSF3D dataset for cities

The main objective of this research is to evaluate if the Random Forest models trained on the basis of spatial metrics derived solely from the WSF3D dataset can be transferred from one city to another. Though with the increased availability of remotely sensed data, new machine learning techniques are constantly emerging for land use mapping, the challenges of collecting validation data and spatial transferability are yet to be addressed. The WSF3D dataset and the technique of "Dissimilarity Index" are used to address these challenges. The main factors allowing model transferability; the association between prediction accuracies and transferability of cities; and the morphological similarities existing between transferable cities are analysed. The "Area of Applicability" is identified, to make assessments for successfully transferring a model to areas where validation data is not available

Assessment of Statistical Models for Rainfall Forecasting Using Machine Learning Technique

As heavy rainfall can lead to several catastrophes; the prediction of rainfall is vital. The forecast encourages individuals to take appropriate steps and should be reasonable in the forecast. Agriculture is the most important factor in ensuring a person's survival. The most crucial aspect of agriculture is rainfall. Predicting rain has been a big issue in recent years. Rainfall forecasting raises people's awareness and allows them to plan ahead of time to preserve their crops from the elements. To predict rainfall, many methods have been developed. Instant comparisons between past weather forecasts and observations can be processed using machine learning. Weather models can better account for prediction flaws, such as overestimated rainfall, with the help of machine learning, and create more accurate predictions. Thanjavur Station rainfall data for the period of 17 years from 2000 to 2016 is used to study the accuracy of rainfall forecasting. To get the most accurate prediction model, three prediction models ARIMA (Auto-Regression Integrated with Moving Average Model), ETS (Error Trend Seasonality Model) and Holt-Winters (HW) were compared using R package. The findings show that the model of HW and ETS performs well compared to models of ARIMA. Performance criteria such as Akaike Information Criteria (AIC) and Root Mean Square Error (RMSE) have been used to identify the best forecasting model for Thanjavur station

Triennial Report: 2012-2014

Triennial Report Purpose [Page] 3 Geographical Information Science Center of Excellence [Page] 5 SDSU Faculty [Page] 6 EROS Faculty [Page] 13 Research Professors [Page] 19 Postdoctoral Fellows [Page] 24 GSE Ph.D Program [Page] 36 Ph.D. Fellowships [Page] 37 Ph.D. Students [Page] 38 Recent Ph.D. Graduates [Page] 46 Masters Students [Page] 56 Previous Ph.D. Students [Page] 58 Center Scholars Program [Page] 59 Research Staff [Page] 60 Administrative and Information Technology Staff [Page] 62 Computer Resources [Page] 66 Research Funding [Page] 67 Glancing Back, Looking Forward [Page] 68 Appendix I Alumni Faculty and Staff Appendix II Cool Faculty Research and Locations Appendix III Non-Academic Fun Things To Do Appendix IV Publications 2012-2014 Appendix V Directory Appendix VI GIScCE Birthplace Map Appendix VII How To Get To The GIScC