Humans are dependent on natural resources to fulfill their basic needs—food, clothes and shelter. However, there has been decline in these resources over last few decades with unprecedented population growth. Resources like water, arable land and fossil fuels have become scarce due to over-consumption. With world population expected to increase to 8.3 billion by 2030, food demand too is expected to increase by 50 per cent (Bruinsma, 2009) and demand for energy from hydropower and other renewable energy resources by 60 per cent (UNESCO, 2009). In the recent years, several countries have been facing severe water shortage. According to the United Nations (2012), water is unavailable to 40 per cent of the population of sub-Saharan Africa while 783 million people do not have access to drinking water. Almost 850 million people are undernourished in the region running the risk of starvation and 1.5 billion people do not have access to energy (Schillebeeckx et al., 2012). In India, most cities are expected to run dry by the year 2020. According to Jain (2014), over the last 60 years, per capita availability of water in India has fallen by 70 per cent making India a water stressed nation. All these issues exist both globally and in India and are indicators of the non-sustainable use of natural resources. Continuous monitoring, therefore is much required to identify the pattern of depletion for timely intervention.
Monitoring of natural resources can be carried out at multiple scales (Fig. 1). While investigations at the local level can be done by field ecologists, such as water resource experts and wildlife scientists to name a few, the continuous monitoring of landscape at a large scale and at a regular interval can be possible only by using geospatial technologies.
Geospatial technology is an amalgamation of state-of-the-art remote sensing, GISc/GIS and global navigation satellite system (GNSS) technology for the mapping of the Earth’s resources. While remote sensing has the capability of providing a synoptic view at a consistent temporal interval with scores of earth observing satellites, integration with GISc is what makes multi-scale and multi-dimensional data analysis feasible. With further integration with GNSS technology, it is possible to get precise location to calibrate and validate the geospatial information received from remote sensing, seamlessly integrating multi-scale information.
The longest remotely sensed time series data available from 1981 onwards is through the Advanced Very High Resolution Radiometer (AVHRR) multi-spectral sensor on National Oceanic and Atmospheric Administration’s (NOAA) Polar Orbiting Environmental Satellites (POES) onboard at daily temporal resolution. The preprocessed vegetation indicator product (normalised difference vegetation index—NDVI) is available for analysis at weekly and biweekly scale. This makes the continuous seasonal and long term monitoring of vegetation for a large area feasible. There are more recent data sets at a finer grain size available since 2000 such as Moderate Resolution Imaging Spectroradiometer (MODIS)and sensor on Aqua and Terra platform that have made mapping and monitoring of vegetation and water resources feasible (Sanchez et al., 2016). However, for better resolution at local scales the Landsat series could be useful—available from the early 1980s to the present. Similarly, gridded rainfall data is available from 1900 onwards (CRU 0.5o x 0.5o monthly data, GPCC 1o x 1o daily data), collected from different sources. The satellite based rainfall observations are available from 1960s onwards and hybrid product at a relatively finer resolution (CHIRPS at 0.05o x 0.05o). Similarly soil moisture product is observed through microwave sensors (AMSR-E, ASCAT). With advancement in technology, NASA launched SMAP in 2015 that allows direct measurement of soil moisture.
In India, mapping and monitoring of natural resources using geospatial technology mostly comes under the purview of Department of Space (DoS) and the Indian Space Research Organisation(ISRO). Over the years, ISRO has launched several Earth observing satellites making continuous monitoring of different resources possible. The first Indian remote sensing satellite was launched in 1988 and since then ISRO has launched several satellites in continuation of the IRS series (1B, 1C, 1D, P2, P3, P4, P6) and others such as Cartosat-1, and -2, -2A, -2B and Resourcesat-1 and -2, and RISAT (ISRO, undated) ISRO is also collaborating with National Aeronautics and Space Administration (NASA) to launch the NASA-ISRO Synthetic Aperture Radar (NISAR) mission with the objective of acquisition of global images of ecosystem changes (Press Information Bureau, 2015).This will enable the understanding of disturbances, degradation and biomass variability.
Additionally, several theme specific national level organisations such as Forest Survey of India (FSI) are engaged in specialised activities. There are several universities such as Indian Institute of Technology (IIT), Indian Institute of Science (IISc), The Energy and Resources Institute (TERI), School of Advanced Studies and non-profit organisations such as Ashoka Trust for Research in Ecology and the Environment (ATREE) who have been entrusted with responsibilities in developing and advancing geospatial technology for mapping and monitoring various natural resources. While there have been several studies focussed on assessing the status of various resources and how it is changing over time, rarely has complete attribution at pixel level been carried out, except for qualitative assessment (Reddy et al., 2017).
The process of continuous monitoring of any natural resource using geospatial technology can be followed in six steps (Fig. 2). The first step involves perceiving any change in resource. For example, noticing any change in forest cover. The second step involves quantification that implies assigning numbers to the change like answering: how much forest cover has been cleared? In the third step, attempts are made to understand if those changes are transformatory or just short-term variabilities in the status of natural resource. For example, seasonal change in vegetation cover may not be so important as compared to long term deforestation. Assessment of real change is an important step, but it is equally important to ascertain the factors responsible that could be proximal or distal. One such example is the forest fire in Uttarakhand that scientists have attributed to a combination of local anthropogenic fires enhanced by prevailing climatic conditions namely, strong El Nino phase (Kale et al., 2017). The comprehensive understanding along with associated drivers responsible can help future projections for all renewable and non-renewable resources. This can further help in formulating policy interventions to stop or slow down the depletion of natural resources. The continuous process of the six step cycle also needs to be followed up with successive monitoring activities.
In India, geospatial technology has been used for mapping various natural resources—one example being the long-term trend in vegetation and ground water in Maharashtra. The first study (Fig. 3) represents the change in vegetation condition over the years for different land covers. The 2005 map of land use/land cover has been developed by ISRO (Roy et al., 2015). The graphs are shown for cropland, built up and natural forest and the output of time series analysis of monthly MODIS NDVI product—from 2001 to 2016. The NDVI value ranges between -1 and 1 with positive values representing vegetation. The higher the value, healthier is the vegetation. We can see that there is substantial decline in vegetation in built up area (Fig. 3a). At the same time, there is an increase in value of NDVI for cropland for all months suggesting a change in the cropping pattern or crop intensification (Fig. 3b), while the third graph describes forest seasonality over the years (Fig. 3c). A shift in green uptime can also be seen (40 per cent green uptime in later half of June has moved to later half of July between 2001 and 2016). Similarly green downtime has also shifted by almost 15 days (early to late December). These changes mandate further probing.
The second example relates to groundwater conditions in Maharashtra (Fig. 4). The groundwater depth for different well locations across states are available through WRIS portal, developed as a joint venture between Central Water Commission (CWC), DoS and Ministry of Water Resources, Government of India. These point locations are commonly used to understand the spatial structure of the availability of water resource through the use of geospatial technology. The point observations are converted to represent a surface using spatial interpolation because of the inherent characteristic of spatial continuity of groundwater. The map shows statistically significant change (z score) in groundwater depth in the month of August between 2001 and 2015 across Maharashtra. The large negative values represent increase in groundwater table whereas the large positive values are areas where there is substantial decline in groundwater table.
Monitoring Natural Resources | Endnote
Over the years, scientists have worked on bringing together datasets available at different scales from various satellites to gain complementary information. With the multi-sensor integration and the narrowing gap between geospatial technology and artificial intelligence web based technologies, it is now possible to seek information at a very fine scale. This will help build a resounding understanding to help provide interventions at local scales and provide guidance to policy makers.
References
Bruinsma J., 2009. The resource Outlook to 2050: by how much do land, water use and crop yields need to increase by 2050? Expert meeting on how to feed the world in 2050, Food and Agriculture Organization of the United Nations.Available at: https://bit.ly/2At2jVE
ISRO, undated. The Saga of Indian Remote Sensing Satellite System, Available at: https://bit.ly/2POIIoo.
Jain A., 2014. Complacency: compounding the urban water crisis. Indian Journal of Public Administration 60(1):105-116
Kale M.P., R. M. Ramachandran, S. N.Pardeshi, M. Chavan, P. K. Joshi, …and P.S. Roy, 2017. Are Climate Extremities Changing Forest Fire Regimes in India? An Analysis Using MODIS Fire Locations During 2003–2013 and Gridded Climate Data of India Meteorological Department. National Academy of Sciences, India Section A: Physical Sciences, 87(4):
827-843.
Press Information Bureau, 2015. ISRO and NASA jointly working on NASA-ISRO Synthetic Aperture Radar (NISAR) mission, Department of Space,July, 2015.
Roy P. S., A. Roy, P. K. Joshi, M. P. Kale, V. K. Srivastava,…and Y. Sharma, 2015. Development of decadal (1985-1995-2005) land use and land cover database for India, Remote Sensing, 7(3): 2401-2430.
Reddy C.S.,V. P. Alekhya, K. R. L. Saranya, K. Athira, C. S. Jha…andV. K. Dadhwal, 2017. Monitoring of fire incidences in vegetation types and Protected Areas of India: Implications on carbon emissions, Journal of Earth System Science,
126(1):11.
Sánchez N., J. Martínez-Fernández,A. González-Zamora, 2016. A combined approach with SMOS and MODIS to monitor agricultural drought, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.
Schillebeeckx S. J. D., P. Parikh, R. Bansal and G. George, 2012. An integrated framework for rural electrification: Adopting a user-centric approach to business model development,Energy Policy 48:687-697
United Nations, 2012. The Millennium Development Goals Report 2012, Available at: https://bit.ly/1jKWMXz
The United Nations Educational, Scientific and Cultural Organization, 2009. The United Nations World Water Development Report 3: Water in a changing world. The United Nations Educational, Scientific and Cultural Organization. https://bit.ly/2O1bKzt
