Harnessing Remote Sensing for Regional Planning: A Narrative Review
Keywords:
Remote Sensing, Regional Planning, GIS, Land Use, Sustainable Development, Lidar, Spatial AnalysisAbstract
Remote sensing technologies have become increasingly essential in modern regional planning, enabling data-driven approaches to land use, environmental monitoring, and urban management. This narrative review aims to explore the application, challenges, and policy implications of remote sensing within regional planning frameworks. Literature was systematically gathered from Scopus, Google Scholar, and PubMed using keywords such as "remote sensing," "land use," "regional planning," and "GIS." Inclusion criteria emphasized studies directly integrating remote sensing with land use analysis, while excluding non-empirical editorials and micro-scale studies. The results show that social factors such as education levels and stakeholder participation significantly affect technology adoption, while economic investment and infrastructure improve data application efficiency. Developed countries benefit from robust data policies and technological resources, whereas developing nations face systemic barriers including limited access, inadequate training, and bureaucratic hurdles. The discussion highlights the importance of institutional capacity and international collaboration in enhancing remote sensing applications. Innovative technologies like LiDAR and multi-sensor data integration offer improved spatial analysis but require substantial technical expertise. In conclusion, remote sensing stands as a critical tool in advancing sustainable regional development. However, to fully harness its potential, targeted investments, capacity building, and inclusive policy reforms are urgently needed.
References
Beijma, S., Comber, A., & Lamb, A. (2014). Use of airborne polarimetric SAR, optical and elevation data for mapping and monitoring of salt marsh vegetation habitats. https://doi.org/10.1117/12.2066487
Bhayunagiri, I., & Saifulloh, M. (2023). Urban footprint extraction derived from worldview-2 satellite imagery by random forest and k-nearest neighbours algorithm. IOP Conference Series: Earth and Environmental Science, 1200(1), 12043. https://doi.org/10.1088/1755-1315/1200/1/012043
Bruno, M., Molfetta, M., Pratola, L., Mossa, M., Nutricato, R., Morea, A., & Chiaradia, M. (2019). A combined approach of field data and earth observation for coastal risk assessment. Sensors, 19(6), 1399. https://doi.org/10.3390/s19061399
Bühler, M., Sebald, C., Rechid, D., Baier, E., Michalski, A., Rothstein, B., & Buziek, G. (2021). Application of Copernicus data for climate-relevant urban planning using the example of water, heat, and vegetation. Remote Sensing, 13(18), 3634. https://doi.org/10.3390/rs13183634
Chitsiko, R., Mutanga, O., Dube, T., & Kutywayo, D. (2022). Review of current models and approaches used for maize crop yield forecasting in Sub-Saharan Africa and their potential use in early warning systems. Physics and Chemistry of the Earth, Parts A/B/C, 127, 103199. https://doi.org/10.1016/j.pce.2022.103199
Dahy, B., Al-Memari, M., Al-Gergawi, A., & Burt, J. (2024). Remote sensing of 50 years of coastal urbanization and environmental change in the Arabian Gulf: A systematic review. Frontiers in Remote Sensing, 5. https://doi.org/10.3389/frsen.2024.1422910
Ekmen, O., & Kocaman, S. (2023). From pixels to sustainability: Trends and collaborations in remote sensing for advancing sustainable cities and communities (SDG 11. Sustainability, 15(22), 16094. https://doi.org/10.3390/su152216094
Espinoza, M., Araya‐Arce, T., Chaves-Zamora, I., Chinchilla, I., & Cambra, M. (2020). Monitoring elasmobranch assemblages in a data-poor country from the Eastern Tropical Pacific using baited remote underwater video stations. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-74282-8
Fernández, M., & Gil, A. (2022). Resource communication: FORESTAZ - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal. Forest Systems, 31(2), 1. https://doi.org/10.5424/fs/2022312-18929
Karimi, P., Pareeth, S., & Fraiture, C. (2018). Remote sensing for agricultural water management (RS4AWM) educational course within postgraduate program for water professionals. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII–5, 21–27. https://doi.org/10.5194/isprs-archives-xlii-5-21-2018
Kelesakis, D., Marthoglou, K., Grammalidis, N., Daras, P., Tsiros, E., Karteris, A., & Stergiadou, A. (2023). A holistic framework for forestry and rural road detection based on satellite imagery and deep semantic segmentation. https://doi.org/10.1117/12.2681850
Kumar, P., Rao, K., Ravindranath, S., Maithani, S., Siddiqui, A., Bharath, B., & Bhavani, S. (2021). A review of IRS-1C applications in urban and regional studies, and infrastructure planning. Journal of the Indian Society of Remote Sensing, 49(1), 161–177. https://doi.org/10.1007/s12524-020-01283-5
Lanya, I., Subadiyasa, N., Sardiana, K., & Adi, G. (2019). Remote sensing and GIS applications for planning of sustainable food agriculture land and agricultural commodity development in Denpasar City. IOP Conference Series: Earth and Environmental Science, 313(1), 12046. https://doi.org/10.1088/1755-1315/313/1/012046
Nagabhatla, N., & Brahmbhatt, R. (2020). Geospatial assessment of water-migration scenarios in the context of sustainable development goals (SDGs) 6, 11, and 16. Remote Sensing, 12(9), 1376. https://doi.org/10.3390/rs12091376
Nonini, L., Schillaci, C., & Fiala, M. (2020). Assessment of forest biomass and carbon stocks at stand level using site-specific primary data to support forest management. https://doi.org/10.1007/978-3-030-39299-4_56
Paršova, V., Celms, A., Gurskienė, V., & Jürgenson, E. (2018). Application of remote-sensing technologies for determination of types of land use. https://doi.org/10.22616/erdev2018.17.n084
Patiño, J., & Duque, J. (2013). A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems, 37, 1–17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003
Qiao, X., Yang, G., Shi, J., Zuo, Q., Liu, L., Niu, M., & Ben‐Gal, A. (2022). Remote sensing data fusion to evaluate patterns of regional evapotranspiration: A case study for dynamics of film-mulched drip-irrigated cotton in China’s Manas River Basin over 20 years. Remote Sensing, 14(14), 3438. https://doi.org/10.3390/rs14143438
Rechsteiner, C., Zellweger, F., Gerber, A., Breiner, F., & Bollmann, K. (2017). Remotely sensed forest habitat structures improve regional species conservation. Remote Sensing in Ecology and Conservation, 3(4), 247–258. https://doi.org/10.1002/rse2.46
Reis, V., Hermoso, V., Hamilton, S., Bunn, S., Fluet‐Chouinard, E., Venables, B., & Linke, S. (2019). Characterizing seasonal dynamics of Amazonian wetlands for conservation and decision making. Aquatic Conservation: Marine and Freshwater Ecosystems, 29(7), 1073–1082. https://doi.org/10.1002/aqc.3051
Shah, P., & Patel, C. (2020). Policy driven application of remote sensing in mitigating urban sprawl: A case study of developing nation. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 751–758). https://doi.org/10.5194/isprs-archives-xliii-b3-2020-751-2020
Sudmanns, M., Tiede, D., Augustin, H., & Lang, S. (2019). Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth Observation (EO) applications using the EO-Compass. International Journal of Digital Earth, 13(7), 768–784. https://doi.org/10.1080/17538947.2019.1572799
Terrone, M., Piana, P., Paliaga, G., D’Orazi, M., & Faccini, F. (2021). Coupling historical maps and LiDAR data to identify man-made landforms in urban areas. ISPRS International Journal of Geo-Information, 10(5), 349. https://doi.org/10.3390/ijgi10050349
Trukhachev, V., Oliinyk, S., Lesnyak, T., & Zlydnev, N. (2020). Application of different probing methods for remote estimation of pasture fertility. https://doi.org/10.22616/erdev.2020.19.tf120
Waite, I. (2013). Development and application of an agricultural intensity index to invertebrate and algal metrics from streams at two scales. JAWRA Journal of the American Water Resources Association, 49(2), 431–448. https://doi.org/10.1111/jawr.12032
Yang, Y., Yang, Y., Liu, D., Nordblom, T., Wu, B., & Yan, N. (2014). Regional water balance based on remotely sensed evapotranspiration and irrigation: An assessment of the Haihe Plain, China. Remote Sensing, 6(3), 2514–2533. https://doi.org/10.3390/rs6032514
Yuan, Q., & Wang, N. (2022). Buildings change detection using high-resolution remote sensing images with self-attention knowledge distillation and multiscale change-aware module. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 225–231. https://doi.org/10.5194/isprs-archives-xlvi-m-2-2022-225-2022
Zaabar, N., Niculescu, S., & Mihoubi, M. (2022). Application of convolutional neural networks with object-based image analysis for land cover and land use mapping in coastal areas: A case study in Ain Témouchent, Algeria. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5177–5189. https://doi.org/10.1109/jstars.2022.3185185
