Global Land-Use Optimization
Global Land-Use Optimization
Ideas for Change

Global Land-Use Optimization

Editor-in-Chief

Laura Del Vecchio

image

NASA @ Creative Commons

The integration of global-to-local approaches to improve land-use management and natural resources conservation
The integration of global-to-local approaches to improve land-use management and natural resources conservation

The planning and management of agricultural systems —with varying expectations from both producers and society— are complex and dependent on biological and socioeconomic aspects. Acknowledging such terms is crucial when developing tools to support decision-making. Recently, several design-support tools, models, methodologies, and multi-criteria optimization of land-use have been developed to meet a variety of needs.

Glancing at our Blue Marble

We are almost 74 years ahead from when the first photograph of the Earth was taken. The Soviet Union was the first nation to dispatch a missile towards space, but the North Americans took the first shots of the Earth from space. On October 24th, 1946, White Sands Missile Range launched a V-2 missile carrying a 35 mm motion picture camera, responsible for taking images at an altitude of 65 miles from Earth's atmosphere; the very beginning of outer space. The images did not reveal the curvature of the Earth but did display the planet against the black enormity of space. The film landed on Earth and survived the collision thanks to a steel case that protected the photographs.

From that day onwards, thousands of other missiles reached spacial territories, but it was not until 1976, with the Apollo 17 mission, that the term Blue Marble was coined for planet Earth. The Blue Marble made reference to the Earth's circular surface of extended oceans covered with a deep cloud layer, comprising territories from the Mediterranean Sea to Antarctica. Since then, many other methodologies and technologies using different types of satellites were created, which later enrolled projects related to Internet coverage, espionage, territorial scanning, land-use modeling, and others.

The images harnessed by satellites, after decades of use, have recorded —and still do— the memoir of human activity. The data collected reflects the synergies between human activity and the response of ecosystems in front of continuous manipulation. Deforestation tracking, the management of ecosystems, agriculture, and climate-change monitoring are some of the most prominent examples of variables being registered. But what satellites and other registering tools really show is that the continuous effects produced by climate change are intrinsically related to how lands are distributed despite the uses given.

Land Use Transformative Actions

The acceleration of climate change is mainly determined by the impacts that agriculture and industrial activity produce on the planet. Yet, said conclusion is tricky due to the intricacies that land for crops, livestock, and factories knock-on the economy —especially considering the fact that the global human population is expected to reach ∼9 billion people by 2050. Complying with the challenge to ensure food security worldwide is exceptionally complex because we also need to reduce greenhouse gas emissions, bypass additional effects on natural ecosystems and wildlife, as well as minimizing fertilizer and pesticide application.

According to a report released in 2019 by Nature Communications written by several specialists including Elke Stehfest and Hugo Valin (whose work influenced many other scientists and experts in the matter) "[...] although agricultural production has increased about 60% during the last 40 years, global cropland area has increased by only 5% as a result of increased agricultural productivity." This means that we are not necessarily physically expanding land-use, but stressing natural resources until its limits. It is, therefore, key for this analysis, the consideration about the interaction between demand and production for tomorrow's better territorial management.

Since the Land Use and Land Cover (LULC) program was founded by the International Geosphere and Biosphere Program (IGBP) and the International Human Dimension Program (IHDP) on Global Change, research conducted on land-use sparked the interest of many climate and environmental organizations to develop strategies to improve land-use management. Still, the sustainable management of land-use demands constant revision of human activities at all scales in order to guarantee the preservation of water bodies, the atmosphere, soil health, as well as the life on land, such as biodiversity (humans included) and food production. The problem is that there is no balance between sustainability and the demand of human society for resources —yet.

The overall land-use administration is not as easy as regionally scrambling crops for specific purposes and dedicating others to conservation. There are multiple interplays dependent on land-use that increase its obstacles to actual change, such as regulations and ownership, demand and supply systems, and various bureaucratic processes. These pose increased pressure on experts and state authorities to develop tools that, at the same time, can preserve the integrity of all previous aspects while they are not yet transcended, but also respond to food security, land scarcity, loss of biodiversity, and sustainable development in general.

To pursue improved economic growth based on sustainability both in social, political, and ecological terms, the application of emerging technologies and policies to monitor, analyze, and certify land-use can make the agriculture sector become more efficiently and consciously sound.

The Linkage Between Emerging Technologies and Climate Policy and Land-use

The human footprint is estimated to affect 83% of Earth's land surface and has deteriorated over 60% of natural ecosystems in the last 50 years. Although land-use occupies central concerns of future environmental changes, analytical and modeling emerging technologies being developed are still in early stages and the intricacy in results is extensive. This means that some technological objects with higher Technological Readiness Level (TRL) are not thus far fully available to drive conclusions in land-use management or climate policy, mainly because they are being deployed in other areas and the outcomes shown in climate policy and land-use are not yet known. However, what we do know is that agriculture plays a crucial part in emitting and storing greenhouse gases and is closely related to how land is used in this industry.

In 2001, the Kyoto Protocol parties decided that they would either entirely or partially compensate their emissions by carbon-sinking greenhouse gases through reforestation, cropland management, revegetation, and afforestation. However, studying the spread across of such practices is genuinely troublesome to follow-up and challenging to monitor the outcomes produced by explicitly reconfiguring land-use. Still, a detailed vision of lands and the identification of their heterogeneity can indeed lead to more precise conclusions in land management, climate policing, and crop production. How do we do that, then?

2.0 Blue Marble

The Department of Aerospace Engineering of the Indian Institute of Science from Bangalore has been conducting research based on the urban growth analysis of the Bangalore region to test the technology method Remote Sensing Data (RDS) to cover the exploitation of natural resources caused by human impact. The method performs a classification based on the spectral patterns of several multi-date and multi-sensor satellite images.

Once the image samples are collected, the harnessed satellite images present researchers a significant study of the changes occurring in natural ecosystems caused by urban development. This conclusion was processed through a classification module; a parametric algorithm where the data distribution is concluded for the input data, known as Maximum Likelihood Classification (MLC). The input data in this model is made of two parameters, the mean and covariance vectors used in discriminating functions to find discrimination within the distribution system. The image output samples were then assigned to their spectral classes with the help of the MSS, in which the research assessment was verified against the Google Earth® tool in both pre and post-classification stages to compare the outcomes.

The classification analysis performed by the algorithm indicated staggering results that the city of Bangalore is being widely affected due to rapid industrialization with severe consequences in the decrease of wetlands and rainwater catchment areas. By comparing images from 1973, 1992, and 2011, they noticed that the studied area suffered a considerable drop in water regions to the present while urban spaces are growing exponentially. Their final statement was "[...] the influence and impact of human settlements on the environment can be seen and justified."

This study sheds light on the accurate projections that the observation of the Earth may lead to contrasting possible future scenarios related to land-use. Nevertheless, even if satellite imaging is used as a consistent planning model to address solutions for land-use management, it is not enough for such a comprehensive and intricate issue. Other solutions focusing on simulation instead of monitoring are also helpful for policymakers and state authorities to optimize probability and avoid uncertainties.

Generating Land Alternatives

The research carried in the town of Chelan, Washington, USA, indicates that the technology method Spatial Optimization is a promising approach for generating alternatives for land-use and additional directions in spatial decision-making. This technological solution provides two different inputs: a set of initial variables and values used to present acceptable targets to be later tested in terms of performance. Based on the original variables, the method generates a new model where a series of constraints are added to the initial values. The combination of these values and variables builds a performance standard in which any subsequent solution generated must, at least, perform as great as the preset values (according to specialists, at least 90% optimal). The target values are finally utilized in model iterations to make sure all objectives will not decrease in performance. While the results were primarily focused on urban data, the model used a density-based design to drive probabilistic land-use planning that can possibly demonstrate real-world applications to encourage sustainability in general land-use optimization.

Still, spatial intricacies are essentially multidimensional and demand repetitive analysis. In other words; the assessment and simulation of territorial spaces are daunting due to the unpredictable movements that can take place in different areas. The variables used to generate traditional land simulations may not agree with real-life aspects sometimes, such as unclear changes in the climate produced by the constant release of greenhouse gases to the atmosphere. Spatial Optimization could be an interesting solution to this challenge, which allows for operating a number of tests that vary in model parameter values. From the standardizing viewpoint, Spatial Optimization is not the last measure in the decision-making process but simply a means for producing alternatives for extra planning and analysis. Furthermore, optimization in land-use allocation does not need to be perceived as extremely precise. Both data and hypotheses generated should be suitable enough to give better results than those obtained by common sense.

Climate Policy Powered by Computing Technologies

This topic leads us to a more complicated issue: creating novel policies to determine actions for land-use management. Climate policy can contribute significantly to combat global warming. Still, socioeconomic and political practices should be accountable for the measures they create —which is not as easy as said than done. The 5th Assessment Report of the Intergovernmental Panel on Climate Change invites decision-makers to combine adaptation and mitigation into their activities to accomplish sustainable development goals.

However, the literature on climate policy integration (CPI) has few examples that examine the interplay between climate change adaptation and mitigation and the application of said measures in conjunction. CPI discussions are focused on mainstream climate change approaches: integrating either climate change mitigation or climate change adaptation with sectoral policies. Not both altogether. If combining adaptation and mitigation into climate policies, experts argue that it can help decrease the risks of climate impacts and damages, support local people to address trade-offs, and employ interactions between agriculture and forestry to reduce threats to food security and biodiversity.

A study commissioned by The Sustainable Biosphere Initiative of the Ecological Society of America in 1997 foresaw such a proposal and evaluated the importance of combining climate change adaptation and mitigation into land-use planning. The research insists that, when applied, scientists can support policy-makers, stakeholders, farmers, and the general public in providing data connecting climate change with land-use management, defining the consequences of climate change precisely in ecological, social, economic, political, and health terms, and finally, associating people's lifestyles and energy choices to environmental effects (such as their decisions on transportation and consumption habits).

Still, this is a hard task for scientists to do alone. The need for integrating data with assessments based on Earth System Models (ESMs) is exponentially growing to address the solutions mentioned above in terms of adaptation in mitigation. A model recently developed, called Computable General Equilibrium (CGE) is a computing application that is expected to play a key part in understanding the role of natural and human systems and their correspondent interactions. The tool analyzes the economic effects of international climate policy at the macro-level and captures macro-economic and global feedback through changes in relative prices of inputs and outputs.

The CGE model performs equations described by model variables and a detailed database that is constantly updated. The equations performed by the model usually display outcomes in terms of cost-minimizing behavior by producers, average-cost pricing, and household demands based on optimizing behavior. The model works on long-term projections based on structural uncertainties in socioeconomic factors that are evidently diversified. However, the range of model outcomes for creating such scenarios tends to be general and results may rely symmetrically on model characteristics only.

Given the magnitude of land-use projections for informing policy-makers in the areas of climate change, food security, and biodiversity protection, it is necessary to examine results projected by the model instead of applying directly its outcomes. This will lead to a better understanding of the possible evolution of land-use and the food system as a whole.

Final Considerations

By combining data from global economic and biophysical models with detailed local land use and spatial data, the use of such emerging technologies aforementioned could become a toolbox to enable policymakers and stakeholders to visualize the outcomes of various long-term scenarios with a focus on changing land-use across the globe.

These technologies may help monitor many variables intended to enhance biodiversity, reduce greenhouse gas emissions, and alleviate the risk of nutrient leaching. It could be particularly useful in establishing fair international governance on sectorial cooperation for emerging countries to introduce better-suited subsidies, help create internal policies to support food security, and overcome unfair trade agreements.

To overcome global famine risks, food security demonstrates a new need to reconsider trade agreements, which have been a detriment to the competitiveness of developing countries while benefitting developed ones. The models mentioned previously could make it possible to find an optimal match between land and crops, hopefully helping create a worldwide agricultural commons. Besides, they could seek better-suited types of permitted subsidies and internal policies to support food security worldwide. However, the distribution of these technologies demands additional development of policy instruments that can also support land-sharing instead of only land-sparing activities.

19 topics
Adapting to Climate Change
Agricultural Policy and Rural Development
Agricultural-based Economic Development
Agricultural Trade and Standards
Agriculture and Climate Change
Anti-Corruption & Standards of Integrity
Basic Energy Services
Biological Diversity
Decentralization & Local Governance
Economic Policy
Education
Energy Efficiency
Environment Policy, Economics, and Management
Food and Nutrition Security
Forest
Global Health
Green and Climate Finance
Green Economy
Land Governance
7 SDGs
02 Zero Hunger
03 Good Health and Well-Being
11 Sustainable Cities and Communities
13 Climate Action
15 Life On Land
16 Peace, Justice, and Strong Institutions
17 Partnerships for the Goals

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Related Content

2 technology domains
3 technology methods
  • Maximum Likelihood Classification (MLC)
  • Remote Sensing Data
  • Spatial Optimization
1 technology applications
5 industries
  • Agriculture
  • Energy
  • Environment & Resources
  • Government & Citizenship
  • Food
19 topics
  • Adapting to Climate Change
  • Agricultural Policy and Rural Development
  • Agricultural-based Economic Development
  • Agricultural Trade and Standards
  • Agriculture and Climate Change
  • Anti-Corruption & Standards of Integrity
  • Basic Energy Services
  • Biological Diversity
  • Decentralization & Local Governance
  • Economic Policy
  • Education
  • Energy Efficiency
  • Environment Policy, Economics, and Management
  • Food and Nutrition Security
  • Forest
  • Global Health
  • Green and Climate Finance
  • Green Economy
  • Land Governance
7 SDGs
  • 02 Zero Hunger
  • 03 Good Health and Well-Being
  • 11 Sustainable Cities and Communities
  • 13 Climate Action
  • 15 Life On Land
  • 16 Peace, Justice, and Strong Institutions
  • 17 Partnerships for the Goals