Lesson 31 Optimal Land Use


Inappropriate and uncontrolled use of natural resources can downgrade their quality and destroy them. Sustainable development and optimized use of natural resources involves effective utilization of the existing resources without damaging the assets and preserves these valuable resources for the future generation. Soil loss or erosion is the most important problem developing due to disturbances of natural resources setting and needs to be considered for sustainability. There are many factors affecting the type and extent of erosion in a watershed. One of the factors is how the lands are used. Over the past years, this issue has played an important role in erosion, as a result of technological advancements introduced in nature. Therefore, the kind of use of lands is an important factor in erosion and production of sediments in watersheds. There are many other constraints like limited availability of water, availability of budget etc which enforces to use land in optimal way.

31.1 Objectives of Optimal Land Use

It takes 300 years for 1 cm of soil (depth) to be formed. Therefore, in order to preserve it as a natural asset along with maximization of income, it is vital to prevent soil erosion.

31.2 Methods and Possibilities in Optimal Land Use

At present, scientific and optimized management of agriculture and natural resources are considered to be important items in sustainable development. In order to achieve sustainability and optimized land allocation, we can use linear programming, multi-objective linear programming, and Geographic Information System (GIS) approaches.

Different researches show that by using linear programming the area of land uses may be modified in such a way that maximum profit and minimum erosion can be resulted. Although finalizing superb economic choices should be accompanied by taking into account biological considerations, ecosystems’ sustainability and social issues. The application of different optimization methods have been developed in recent years in such a way that most of administrative and logical measures have been based on relevant research.

Many researchers have already applied the above techniques for the optimization of land use. Benli and Kodal (2003) in their study on the optimization of land use in southeast of Antalya, Turkey, highlighted programming for the purpose of maximizing profit obtained from agricultural lands, in spite of shortage of water. Nguyen and Egashira (2004) emphasized the increase in the use of agricultural and forest lands in Tran Yen, Japan, through appropriate land allocation for different uses. Singh and Singh (1999) investigated the multi-objective linear programming model for optimizing land use in the north of China. The results show that if the resources are used properly, the preservation of soil and provision of food and income for rural inhabitants will be continuously improved. Nikkami et al. (2002) utilized the optimization model to decrease environmental and economic effects of soil erosion caused by mismanagement of land use activities in one of the sub-basins of Damavand watershed, Iran. Nikkami et al. (2009) used multi-objective linear programming in a study on the basin Kharestan watershed which is situated north-west of Iqlid, in the province of Fars, Iran. They determined the optimal land use level to decrease erosion and increase the income of the inhabitants of the basin, concluding that the current land use levels were not appropriate for decreasing erosion and increasing the income of the inhabitants. The results showed that if land use is optimized, the degree of soil erosion and the profitability of the entire watershed under standard land use circumstances will respectively decrease 53.2% and increase 207.98%. The modelling of spatial use distribution of agricultural lands to maximize profit in two regions in England. Multi-objective linear programming was utilized to enhance income and decrease soil erosion in the basin of Brim and watershed, in Iran. The findings indicate that the application of optimization of land use can contribute to total income up to 18.62% and decrease soil erosion about 7.87%.

31.3 Use of Remote Sensing and GIS Techniques in Achieving Optimal Land Use

Traditional data gathering methods ranging from sample surveys to systematic land use surveys are generally too expensive and time consuming to obtain optimal land use. The timely accurate agricultural information using remote sensing techniques are of strategic importance for determining the food policy and management of the food crisis in case of crop damage due to disasters like severe drought, flood etc. Remote sensing allows the spatio-temporal analysis of land use and land cover changes. It supplies the needed geo database to build informative and rich understanding of natural resources. The role of GIS is in storing, managing a great deal of data about the images and all the related attributes to allow their manipulation, analysis and finally presentation according to choice. The ability of GIS in spatially accurate representation facilitates the analysis, computations, prediction, retrieving through many types of processing, especially overlaying of different layers extracted from multi date remotely sensed data. 

31.4 Models for Watershed Processes Simulation

Watersheds are modeled to facilitate well-studied designs and informed management decisions. In engineering and management practices, it is important to understand complex interactions occurring today as well as predict impacts years, perhaps even decades, into the future. In recent years, watershed management practices that were once praised for their broad benefits to society have become the focus of harsh criticisms for their adverse and unexpected environmental or socio-economic impacts. Watershed models help us predict future impacts of projects and management policies, which in turn contributes to improved water resources system design, planning, and operation, and thus more sustainable water resources management. The watershed has been widely acknowledged to be the appropriate unit of analysis for water and natural resources planning and management problems. However, many of the environmental processes and socio-economic activities occurring within a watershed are simply too complex, dynamic, and spatially variable to be precisely monitored and thoroughly understood. As population grows, continued human encroachment into natural systems seems inevitable, with expanding communities needing increased water supplies to carry on various development activities in the watershed. Paradoxically, both water shortage (drought) and overabundance (flooding) will become even more problematic for many communities. Expectations will remain high for using water as a means of socio-economic development and ecosystem conservation and enhancement. It is unlikely that these expectations can be met without the aid of analytical tools such as computer watershed models. Watershed models are mathematical representations of watershed processes and affected socio-economic and environmental systems. They have become a fundamental and integrated element of any engineering project or management practice that is deemed to alter diverse natural processes. Models help us gain insights into hydrological, ecological, biological, environmental, hydro-geochemical, and socio-economic aspects of watersheds, and thus contribute to systematized understanding of how watershed sub-systems function, which is essential to integrated water resources management and decision making. There are numerous watershed models, having various levels of sophistication and providing diverse types of information, but all watershed models share one common characteristic, that is, they are all simplifications of actual watershed processes. Another common characteristic of all models is that they require data, or observations, in order for their parameters (i.e., equation coefficients) to be estimated accurately. The process of adjusting model parameters to obtain a good match between model output and real-world observations is called calibration. Additionally, an independent set of observations should be used to test, or verify, the calibrated model in order to evaluate the expected accuracy of model results. If the expected accuracy is not acceptable, additional data should be gathered, or a simpler model may be warranted. Although these steps of calibration and verification may be costly and time-consuming, they are critical to ensuring accurate results and fostering confidence in predicted outcomes. A chronological synthesis of watershed modeling provides an overview of how modeling goals have evolved from describing only physical processes to the integration of social, economic, and environmental objectives in support of decision making.

Keywords: Optimal Land Use, Watershed Processes Simulation, Remote Sensing, GIS Techniques.


  • Benli, B., Kodal, S. (2003). A Non-linear Model for Farm Optimization with Adequate and Limited Water Supplies: application to the South-east Anatolian Project (GAP) Region. Agric. Water Manage. 62: 187-203.

  • Nguyen, T.T. and Egashira, K. (2004). Land Use Effectiveness by Farm Households after Land and Forest Allocation at Tran Yen district, Yen Bai province. J. Fac. Agric. Kyushu Univ., 49: 461-466.

  • Nikkami, D., Elektorowicz, M., Mehuys, G.R. (2002). Optimizing the Management of Soil Erosion.Water Qual. Res. J. Canada 37(3): 577-586.

  • Nikkami, D., M. Shabani and H. Ahmadi, (2009). Land Use Scenarios and Optimization in a Watershed. J. Applied Sci., 9: 287-295.

  • Rounsevell, M.D.A., J.E. Annetts, E. Audsley, T. Mayr and I. Reginster, (2003). Modelling the Spatial Distribution of Agricultural Land Use at the Regional Scale. Agric. Ecosyst. Environ., 95: 465-479.

  • Sadeghi, S.H.R., Kh. Jalili and D. Nikkami, (2009). Land Use Optimization in Watershed Scale. Land Use Policy, 26: 186-193.

  • Singh, A.K., Singh, J.P. (1999). Production and Benefit Maximization through Optimal Crop Planning—A Case Study of Mahi Command. Indian J. Soil Conserv. 27 (2): 152-157.

Suggested Reading

  • Giri, C. P. (2012). Remote Sensing of Land Use and Land Cover: Principles and Applications (Vol. 8). CRC Press.

  • Campbell, J. B. (2002). Introduction to Remote Sensing. Guilford Press.

  • Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2004). Remote Sensing and Image Interpretation (No. Ed. 5). John Wiley & Sons Ltd.

  • Jensen, J. R. (2009). Remote Sensing of the Environment: An Earth Resource Perspective 2/e. Pearson Education India.

Last modified: Friday, 7 February 2014, 11:24 AM