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Editorial

Perspectives and Advancements on “Land Use and Land Cover Mapping in a Changing World”

CREA-Research Centre for Agricultural Policies and Bioeconomy, Via Barberini 36, 00187 Rome, Italy
Submission received: 16 November 2022 / Accepted: 17 November 2022 / Published: 23 November 2022
(This article belongs to the Special Issue Land Use and Land Cover Mapping in a Changing World)

1. Introduction

It is increasingly recognized that land use and land cover changes driven by anthropogenic pressures are increasingly impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. Mapping and monitoring land use and land cover change dynamics are essential for preserving the environment and natural capital and ensuring the sustainability of ecosystem services.
In recent years, the rise of new geospatial technologies around computational techniques and the Internet brought forth a revolution in mapping creation, visualization, and dissemination, bringing new prospects for land mapping and monitoring and enabling near-real-time and cost-effective analysis at multiple scales.
Among others, salient mapping approaches at multiple scales were applied to investigate land use and land cover changes seeking to provide answers in the spheres of human-dominated landscapes and land-related issues (i.e., to explore, manage, organize, or predict land changes). Examples include automated cropland mapping [1], glacier inventory [2], flood inundation [3], mapping forest harvesting [4], and mapping urban agriculture at high-resolution [5].
This Special Issue contains 12 original papers covering various issues related to land use and land use change in several parts of the world (see references), with the purpose to provide a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, as well as investigations on libraries of legends and classification systems.

2. Key Findings and Insights

Measuring and mapping aboveground biomass is a critical component for carbon stock inventories and quantification (Appendix A). In the first paper of this Special Issue, Amara et al., assessed aboveground biomass distribution in a multi-use savannah landscape in southeastern Kenya using airborne laser scanning data, field surveys, and Sentinel-2 satellite images in the Google Earth Engine. Their study evidenced that fences and conservation areas can lead to reduced biomass stocks, which is a vital role of savannahs. The paper by Žoncová et al. used CORINE land cover data for mapping extent and character of land cover changes in the Low Tatras National Park in Slovakia over the last 30 years (1990–2018). This approach allowed them to exploit the potentials of CORINE data to evaluate the long-term landscape changes in protected areas.
Similarly, the study by Gu et al. analyzed land use and land cover dynamics and their impacts on ecosystem services in central Himalaya using the Google Earth Engine between 2000 and 2005. This study highlighted that the Google Earth Engine is a valuable source of data to evaluate the effects of land use and land cover changes on ecosystem service values.
Monitoring the intensity of land use and urban expansion is of great importance for environmental policies. Kim et al. determined changes in land coverage for 31 satellite cities surrounding Seoul using land cover maps from 1988 and 2018 and employing morphological spatial pattern analysis and cluster analysis. The authors suggested that their results can serve for establishing differentiated environmental policies at the local level. The paper by Nedd et al., performed a literature review to scrutinize and evaluate land and land cover definitions and classification systems at the national, regional, and global scales, highlighting the most important challenges, discrepancies, and knowledge gaps. The methodology proposed by the authors will aid the researcher in analyzing the information required in land us and land cover studies.
Deforestation and forest degradation is one of the main environmental problems in Africa. The paper by Kabuanga et al., evaluated deforestation in the Ituri-Epulu-Aru Landscape (Democratic Republic of the Congo) analyzing historical changes and future trajectories through the diachronic analysis of satellite images (2003–2010–2014–2016) and using the DINAMICA EGO platform. The study shows that observed deforestation rates remain relatively low compared to other regions, but forests are shrinking as a result of the unsustainable land use pattern. In their Perspective article, Jand and Woo reaffirm the importance of native trees and their potential for carbon sequestration and mitigation of greenhouse gas emissions. The study highlighted the importance of native trees for providing vital ecosystem services.
Renewable energies can play an important rule toward carbon neutrality. Nevertheless, they can also impact on landscape integrity. Cole et al. assessed landscape dynamics in the United Kingdom driven by pressures from energy production and forests, analyzing change patterns and land cover transitions using CORINE data (years 2006–2018). The authors reported that there has been an increase in the rate of change attributed to renewable energy infrastructure.
Remote sensing images can be efficiently used for multitemporal analysis of changes in forest ecosystems. De Oliveira et al., used high-resolution Landsat images to carry out a multitemporal analysis of changes in land use and land cover in the municipality of Floresta in Pernambuco State in Brazil. The authors analyzed impact of changes in the study area, showing a reduction in the forest and agricultural classes and an increase for exposed-soil class. In another study, Nicolau and Condessa assessed net land take in Portugal between 2007 and 201 by using the Land and Ecosystem Accounting (LEAC) system developed by the European Environment Agency. The study shows that the land use rate amounted to 7.2 ha/day.
The paper by Mushtaq et al., developed an International online catalogue for land cover legend, named Land Cover Legend Registry. This is an international platform that can contribute to development of harmonized land cover legends and datasets at various levels globally. In the final paper, Allan et al. performed a review on the drivers of land use and land cover change in urban areas (2012 to 2022). The study shows that transportation availability was the most frequent factor impacting land use and land cover change processes.

3. Conclusions

A growing body of literature has shown that land use and land cover change can impact the global ecosystem, shaping the future sustainability of natural resources. Research findings, challenges, and key insights that emerged in the cutting-edge studies in this Special Issue contribute to the literature by exploiting the full potential of land mapping in understanding the complex nexus of dynamics among land ecosystems, use of resources, and anthropogenic interaction with the land. We hope that the readers of the Land journal find these articles of interest and that they may help in the development of further applications of land use and land cover mapping.

Funding

This research received no external funding.

Acknowledgments

The author wish to thank Land for hosting this Special Issue on “Land Use and land Cover Mapping in a Changing World”; those that submitted papers to this issue; and all the anonymous reviewers for their thoughtful insights and comments.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

  • Amara, E.; Adhikari, H.; Heiskanen, J.; Siljander, M.; Munyao, M.; Omondi, P.; Pellikka, P. Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences. Land 2020, 9, 381. https://0-doi-org.brum.beds.ac.uk/10.3390/land9100381.
  • Žoncová, M.; Hronček, P.; Gregorová, B. Mapping of the Land Cover Changes in High Mountains of Western Carpathians between 1990–2018: Case Study of the Low Tatras National Park (Slovakia). Land 2020, 9, 483. https://0-doi-org.brum.beds.ac.uk/10.3390/land9120483.
  • Gu, C.; Zhang, Y.; Liu, L.; Li, L.; Li, S.; Zhang, B.; Cui, B.; Rai, M.K. Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine. Land 2021, 10, 173. https://0-doi-org.brum.beds.ac.uk/10.3390/land10020173.
  • Kim, J.-H.; Kwon, O.-S.; Ra, J.-H. Urban Type Classification and Characteristic Analysis through Time-Series Environmental Changes for Land Use Management for 31 Satellite Cities around Seoul, South Korea. Land 2021, 10, 799. https://0-doi-org.brum.beds.ac.uk/10.3390/land10080799.
  • Nedd, R.; Light, K.; Owens, M.; James, N.; Johnson, E.; Anandhi, A. A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape. Land 2021, 10, 994. https://0-doi-org.brum.beds.ac.uk/10.3390/land10090994.
  • Kabuanga, J.M.; Kankonda, O.M.; Saqalli, M.; Maestripieri, N.; Bilintoh, T.M.; Mweru, J.-P.M.; Liama, A.B.; Nishuli, R.; Mané, L. Historical Changes and Future Trajectories of Deforestation in the Ituri-Epulu-Aru Landscape (Democratic Republic of the Congo). Land 2021, 10, 1042. https://0-doi-org.brum.beds.ac.uk/10.3390/land10101042.
  • Jang, J.; Woo, S.-Y. Native Trees as a Provider of Vital Urban Ecosystem Services in Urbanizing New Zealand: Status Quo, Challenges and Prospects. Land 2022, 11, 92. https://0-doi-org.brum.beds.ac.uk/10.3390/land11010092.
  • Pulighe, G.; Altobelli, F.; Bonati, G.; Lupia, F. Challenges and Opportunities for Growing Bioenergy Crops in the EU: Linking Support Schemes With Sustainability Issues Towards Carbon Neutrality. In Comprehensive Renewable Energy; Elsevier: Amsterdam, The Netherlands, 2022; pp. 22–33.
  • Cole, B.; Smith, G.; de la Barreda-Bautista, B.; Hamer, A.; Payne, M.; Codd, T.; Johnson, S.C.M.; Chan, L.Y.; Balzter, H. Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry—Results of the CORINE Land Cover Map 2018. Land 2022, 11, 192. https://0-doi-org.brum.beds.ac.uk/10.3390/land11020192.
  • de Oliveira, C.P.; de Lima, R.B.; Alves Junior, F.T.; de Lima Pessoa, M.M.; da Silva, A.F.; dos Santos, N.A.T.; Lopes, I.J.C.; de Melo, C.L.S.-M.S.; Silva, E.A.; da Silva, J.A.A.; et al. Dynamic Modeling of Land Use and Coverage Changes in the Dryland Pernambuco, Brazil. Land 2022, 11, 998. https://0-doi-org.brum.beds.ac.uk/10.3390/land11070998.
  • Nicolau, R.; Condessa, B. Monitoring Net Land Take: Is Mainland Portugal on Track to Meet the 2050 Target? Land 2022, 11, 1005. https://doi.org/10.3390/land11071005.
  • Mushtaq, F.; Henry, M.; O’Brien, C.D.; Di Gregorio, A.; Jalal, R.; Latham, J.; Muchoney, D.; Hill, C.T.; Mosca, N.; Tefera, M.G.; et al. An International Library for Land Cover Legends: The Land Cover Legend Registry. Land 2022, 11, 1083. https://0-doi-org.brum.beds.ac.uk/10.3390/land11071083.
  • Allan, A.; Soltani, A.; Abdi, M.H.; Zarei, M. Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review. Land 2022, 11, 1222. https://0-doi-org.brum.beds.ac.uk/10.3390/land11081222.

References

  1. Xiong, J.; Thenkabail, P.S.; Gumma, M.K.; Teluguntla, P.; Poehnelt, J.; Congalton, R.G.; Yadav, K.; Thau, D. Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing. ISPRS J. Photogramm. Remote Sens. 2017, 126, 225–244. [Google Scholar] [CrossRef] [Green Version]
  2. Mölg, N.; Bolch, T.; Rastner, P.; Strozzi, T.; Paul, F. A Consistent Glacier Inventory for Karakoram and Pamir Derived from Landsat Data: Distribution of Debris Cover and Mapping Challenges. Earth Syst. Sci. Data 2018, 10, 1807–1827. [Google Scholar] [CrossRef] [Green Version]
  3. Jiang, X.; Liang, S.; He, X.; Ziegler, A.D.; Lin, P.; Pan, M.; Wang, D.; Zou, J.; Hao, D.; Mao, G.; et al. Rapid and Large-Scale Mapping of Flood Inundation via Integrating Spaceborne Synthetic Aperture Radar Imagery with Unsupervised Deep Learning. ISPRS J. Photogramm. Remote Sens. 2021, 178, 36–50. [Google Scholar] [CrossRef]
  4. Zhao, F.; Sun, R.; Zhong, L.; Meng, R.; Huang, C.; Zeng, X.; Wang, M.; Li, Y.; Wang, Z. Monthly Mapping of Forest Harvesting Using Dense Time Series Sentinel-1 SAR Imagery and Deep Learning. Remote Sens. Environ. 2022, 269, 112822. [Google Scholar] [CrossRef]
  5. Pulighe, G.; Lupia, F. Mapping Spatial Patterns of Urban Agriculture in Rome (Italy) Using Google Earth and Web-Mapping Services. Land Use Policy 2016, 59, 49–58. [Google Scholar] [CrossRef]
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Pulighe, G. Perspectives and Advancements on “Land Use and Land Cover Mapping in a Changing World”. Land 2022, 11, 2108. https://0-doi-org.brum.beds.ac.uk/10.3390/land11122108

AMA Style

Pulighe G. Perspectives and Advancements on “Land Use and Land Cover Mapping in a Changing World”. Land. 2022; 11(12):2108. https://0-doi-org.brum.beds.ac.uk/10.3390/land11122108

Chicago/Turabian Style

Pulighe, Giuseppe. 2022. "Perspectives and Advancements on “Land Use and Land Cover Mapping in a Changing World”" Land 11, no. 12: 2108. https://0-doi-org.brum.beds.ac.uk/10.3390/land11122108

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