Land Use and Land cover Change and Human dimensions

Land cover and land use term are often used interchangeably, their actual meanings are quite distinct. Land cover refers to the surface cover on the ground, whether natural vegetation, snow/glaciers, surface water/wetlands, bare soil, beaches, or similar features. Identifying, delineating, and mapping land cover is important for global monitoring studies, human impact on natural systems, and conservation planning. 

Land use refers to the purpose the land serves, for example, agriculture, recreation, wildlife habitat, or human settlement and infrastructure. Land use applications involve both baseline mapping and subsequent monitoring, since timely information is required to know what current quantity of land is in what type of use and to identify the land use changes from year to year. This knowledge will help develop strategies to balance conservation, conflicting uses, and developmental pressures. Issues driving land use studies include the removal or disturbance of productive land, urban encroachment, and depletion of forests.

Land-use and land-cover changes affect local, regional, and global climate processes. Choices about land-use and land-cover patterns have affected and will continue to affect our vulnerability to the effects of climate change. In addition to emissions of heat-trapping greenhouse gases from energy, industrial, agricultural, and other activities, humans also affect climate through changes in land use (activities taking place on land, like growing food, cutting trees, or building cities) and land cover (the physical characteristics of the land surface, including grain crops, trees, or concrete). For example, cities are warmer than the surrounding countryside because the greater extent of paved areas in cities affects how water and energy are exchanged between the land and the atmosphere. This increases the exposure of urban populations to the effects of extreme heat events. Decisions about land use and land cover can therefore affect, positively or negatively, how much our climate will change and what kind of vulnerabilities humans and natural systems will face as a result.

The conventional approach to collect LULCC information in India at the national scale is through compilation of available records from the Directorate/Bureau of Economics and Statistics (DES/BES). Land use information derived from agricultural inventory of individual field plots is also available in nine-fold classification system comprising of land, irrigated area and total area under crops from different states and union territories of the country. Land cover / use studies are multidisciplinary in nature, and thus the participants involved in such work are numerous and varied, ranging from, forestry specialists, agriculture scientist, wildlife and conservation foundations, government agencies dealing with rural development, water resource managers, urban planning, watershed development, hydrological modelling, and climate researchers. Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g., forests, grasslands, croplands, lakes, wetlands. Dynamic land cover maps include transitions of land cover classes over time and hence captures land cover changes. The data collected by conventional approach are not sufficient for contemporary land use planning and assess the human induced negative impacts. Following are the drivers of LULC change:

• Proximate driver is an event which is closest to, or immediately responsible for causing the change;
• Underlying driver is usually thought of as the “real” reason for LULCC induced occurrences.

Land use is influenced by the socioeconomics, characteristics of the local biophysical environment that determine, to a considerable extent, land suitability for a range of uses. For agricultural land uses, the existence and state of landscape capital—such as irrigation and land drainage work, water supply networks, etc. Agriculture has nonlinear interaction between and within natural and human forcing (Meiyappan et al. 2016).

Land cover and land use mapping?

Identification of land cover establishes the baseline from which monitoring activities (change detection) can be performed and provide the ground cover information for baseline thematic maps. Geographers normally prepare land cover and land use maps using survey base maps and tedious field surveys. Drawing upon the training, field experience, geographic knowledge, power of observation and patience, geographers and surveyors used to map the land use and land cover. It used to be time-consuming task often with limited accuracies.

Aerial photographs became primary source of land use and land cover maps during post World War II using photo-interpretation techniques (by involving (tone/colour, texture, and pattern). The aerial photographs dramatically reduced the mapping time and improved the accuracy. However, monitoring the change was difficult as it was costly and not accessible due to secrecy issues. In early seventies, advent of satellite remote sensing transformed land cover and land use mapping at various scales. Last five decades have seen unprecedented growth in mapping technologies with improved resolution (20 cm to 50 m), precise location systems, geographic information system (GIS), internet and power of computing. Now it is possible to map land use and land cover maps in regular intervals with multidisciplinary scientists. Remote sensing and Geoinformatics specialization along with knowledge of computers and programming skills have become a necessity to study land cover land use and its dynamics. 

Land cover and Land use change analysis also require appropriate methodology so that actual change and misclassification can be separated. Machine learning techniques can separate change and no change areas more efficiently.

Resources needed?

Based on the objective and classification scheme (details), land use and land cover scientist, may choose appropriate satellite data (spatial resolution i.e., minimum resolving capacity on ground). For many applications scientist will need multitemporal data (seasonal) within the same year. For monitoring the changes, one will require satellite data at desired interval. Once satellite is launched the satellite remains in orbit for a decade or so. Initial days the visual interpretation (as used to be practice for aerial photointerpretation) was the main medium of LULC mapping. Still, many uses this as a technique to prepare LULC maps in the complex landscapes. However, the visual interpretation is subjective due to interpreter’s ability and capturing multidisciplinary perspective. It is also possible to carry out on screen digitization using image processing and GIS software. The mapping will require computer hardware and software for image processing. The software can be selected from commercial and open-source suit. However, many cloud-based platforms are available which provide open-source satellite data (10 m or coarser), ancillary data and data analytics interface. One of the basic requirement for classification accuracy and consistency in of LULC mapping is long-term multispectral-multiresolution, temporal and seasonal (science grade data) and other variable parameters like, climatic zones, soil types, topography (derived slope and aspect).

Importance of LULC studies with reference to India?

With Increase in population, demand for food grew exponentially, resulting in conversion of the forest land into agriculture in early 1960’s. Development has also triggered urbanization, highway construction, mining of minerals and construction material, reclamation of wetlands in big cities to grab land. The river basins are getting silted due to high run off. Better planning for environmentally sustainable development essentially requires current LULC maps. Land degradation due to poor land use also require land restoration for enhancing carbon stock for sequestration process.

Watershed development for sustainable water use require hydrological modelling to access runoff, infiltration, and evapotranspiration. LULC maps are essential inputs to establish hydrological model in various scales of the water basins. Such a scientific approach can prioritize sites for restoration to reduce runoff and construct rainwater harvesting structures for sustaining surface/ground water and maintain soil moisture. Agriculture suitability, drought and flood mitigation and compensation to the farmers for the disaster damage by insurance companies also require updated LULC change maps.

Land use/land cover (LULC) change have emerged on the global stage due to the realization that changes occurring on the land surface also influence climate, ecosystem, and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. For example, conversion of forest to agriculture or forest land to urban area impacts on the global carbon cycle and add or remove carbon dioxide (or, more generally, carbon) from the atmosphere, influencing climate. LULC change has been identified as second most important factor affecting global warming. Inter-Governmental Panel for Climate Change (IPCC-UN) estimates that land-use change (e.g. conversion of forest into agricultural land) contributes a net 1.6 ± 0.8 Gt carbon per year to the atmosphere.

Unexpected findings from LULC studies in India?

Unplanned urban growth and changes of Land-use land cover (LULC) is the warning scenario for towns and cities in India. It is estimated that there is change in Indian urban population at 3.3% between 2001 and 2011, with 29.5% of this urban growth. The transformation of agriculture and natural land to impervious areas has induced local microclimate variations and changes in the surface energy budget. We observe heat islands within cities and urban flooding in major metropolitan cities. Excessive withdrawal is making cities water less in summer season. Urban growth in productive agriculture land is alarming sign for food security.

We are losing productive land due to excessive irrigation, degradation of forests and poor land management practices leading to soil erosion and soil salinity. Water demanding crops are being grown in some severely water stressed regions like Marathwada (Maharashtra). Cropping systems are changing most productive lands of Indo-Gangetic plains. Politically motivated free electricity is leading to excessive ground water use in the states/regions like Punjab, Haryana and western UP.

The coastal regions are being encroached for construction in tourist spots reducing beaches and enhancing coastal erosion. The mangrove forests are being encroached for agriculture and multiple use (including commercial aquaculture) resulting in loss/degradation of mangrove forests.

Roy et al. (2015) studied decadal land use land cover of India using satellite data of 1985, 1995 and 2005 (of three cropping seasons). They reported significant increase in Urban (0.41%), Cropland (1.79%) and significant decrease in fallow (0.91%), forest (1.11%) & wasteland (0.30%) during 1985 to 2015. The study also indicated that the major source of forest loss was for cropland expansion in areas of low cropland productivity (due to soil degradation and lack of irrigation), followed by industrial development and mining/quarrying activities, and excessive economic dependence of villages on forest resources.

Decadal Change National Land use and Land cover data base of India using Satellite remote sensing (Roy et al. 2015)

How can LULC studies be improved? What are its biggest advantages and disadvantages?

Classifying remote sensing imageries to obtain reliable and accurate land use and land cover (LULC) information remains a challenge as it depends on many factors such as complexity of landscape, the remote sensing data selected (spectral, temporal, and spatial resolution), image processing and classification methods, etc. The Machine learning techniques namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) have promise to improve the classification and bring in consistency of classification. The studies carried to assess accuracy using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE) indicate overall accuracy in the range of 92-89 %. Results of different studies show that Kappa coefficient of all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest Kappa accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82.

Indian agriculture peaks during monsoon season as “Kharif” crop. During this period creating crop growth spectral profile for identification of various crops and production/ yield are major limitation. While the use of microwave remote sensing (all weather – penetrates clouds) for land use/land cover (LULC) classifications has some promise. There are a wide variety of microwave imaging parameters to choose from, parameters such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multiple diverse radar images for LULC classifications. The beneficial use of a combination of x- and c-band microwave data, the potential of multitemporal very high-resolution multi-polarization SAR-X data, and the profitable integration and comparison of microwave and optical remote sensing images show promise for LULC classifications.

In rainfed areas, farmers practice mixed cropping in small holdings during “Kharif” season. This also poses major challenge for satellite remote sensing and classification techniques.

Has the study of LULC evolved to cover an aspect not anticipated, or is unusual?

The land surface is rapidly changing everyday due to certain natural reasons and other impacts of the society. Over the last few decades, the hottest topics in the field of remote sensing and geographic information system (GIS) have evolved from observing land use and land cover changes which in turn have potential of changing the nature of the earth. For example, polar ice melt and glacier melting has cascading effect on river flow, formation of glacial lakes and their collapse and in long-term bringing in sea level rise, coastal inundation, ocean, and atmospheric circulations. The regional and global level modifications related to the events on nature of the earth (like extreme events leading to drought, flash floods, urban floods, heat waves and events like cyclones and hurricanes) are due to land use/land cover change. LULCC at various scales (local to global levels) is considered as matter of utmost importance in the natural atmosphere and is an interesting area for the researchers. 

There is archaeological and palynological evidence from many parts of the world for human-induced landscape changes during the Late Holocene. This raises the issue of whether the LULC changes associated with the Neolithic agricultural revolution, from ca 10,000-year BP onwards in the Middle East, were large enough to affect climate. LULC impact on climate in more recent millennia appears more probable and evident. It is believed that weakening of monsoon started about 8000-10000 years ago with organized agriculture by human civilization in major river basins. With the world's population heading towards ten billion by 2050, the present consumption trend means destroying forests for pastures, agriculture, industrial growth and urbanisation. This will lead to more emissions of greenhouse gases, pollution and species extinctions. It is estimated that with present food system the planet will exceed all planetary boundaries for climate change, water use, land use and biochemical enrichment. Hence it is logical that humans consider to change food system and move towards sustainable system.

References

Roy P S, Roy A, Joshi P K, Kale M P, Srivastava V K, Srivastava S K, Dwevidi R S, Joshi C, Behera M D, Meiyappan P , Sharma Y, Jain A K, Singh J S, Palchowdhuri Y, Ramachandran R M, Pinjarla B, Chakravarthi V, Babu N, Gowsalya M S, Thiruvengadam P, Kotteeswaran M, Priya V, Yelishetty K M V N, Maithani S, Talukdar G, Mondal I, Rajan K S, Narendra P S, Biswal S, Chakraborty A, Padalia H, Chavan M, Pardeshi S N, Chaudhari S A, Anand A, Vyas A, Reddy M K, Ramalingam M, Manonmani R, Behera P, Das P, Tripathi P, Matin S, Khan M L, Tripathi O P, Deka J, Kumar P and Kushwaha D (2015) Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India. Remote Sensing (7) 2401-2430; doi: 10.3390/rs70302401.

Meiyappan P, Roy P S, Sharma Y, Ramachandran R M, Joshi P K, DeFries R S and Jain A. K. (2016) Dynamics and determinants of land change in India: Integrating satellite data with village socioeconomics. Regional Environmental Change. DOI 10.1007/s10113-016-1068-2.

https://unfccc.int/topics/science/workstreams/cooperation-with-the-ipcc/the-fifth-assessment-report-of-the-ipcc