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Feature Paper Special Issue: Large Spatial Scale Analysis and Intensive Data Use in Urban Social, Ecological, Economic, or Environmental System

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 49605

Special Issue Editor

Meadows Center for Water and the Environment, Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666, USA
Interests: remote sensing; GIS; geospatial statistics; land use land cover change and prediction; assessment and monitoring of drought, land degradation, and desertification; landscape fragmentation; urban environmental modeling including urban water use and climate analysis; forest characterization including coastal environments; disaster assessment, recovery, and monitoring; agriculture water use, evapotranspiration, and surface energy analysis; spatial modeling; and classification algorithm development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Urban remote sensing serves as a platform to provide evidence-based theoretical and practical knowledge that contributes to solving or better understanding the problems of urban issues and formulating relevant policies for sustainable urban systems.

This important feature paper Special Issue aims to collect and highlight recent work in urban science at global, regional, and national level using geospatial data and analytical approaches. We are inviting original work related but not limited to the use of remote sensing and geospatial technics in understanding and quantifying large-scale biophysical, social, economic, demographic, and cultural, climatic phenomena in urban areas, and the development of algorithms and datasets to facilitate urban science. We will also consider urban remote sensing studies that consider geospatial data over multiple cities, even though they do not focus on macrolevel analysis.

Topics may include but are not limited to the following:

  • Assessment and monitoring of urban emissions
  • Urban spatial configuration and pattern analysis
  • Data mining and machine learning in urban applications
  • 3D and 4D urban modeling from satellite, airborne, and terrestrial sensors
  • Space–time analysis of urban environmental parameters
  • Urban disaster-monitoring and change analysis
  • Multiscale sensors and systems: specific urban challenges
  • Radar and LiDAR applications in urban settings
  • Scale issues related to urbanscapes
  • The urban heat island effect and thermal sensing
  • Urban climate change, variation, and extreme
  • Urban data synthesis and analysis
  • Urban applications of high-resolution optical sensors
  • Geospatial cyberinfrastructure for cities

Recognition of the feature papers

  1. The selected manuscripts will be published as award-winning papers at no cost.
  2. The published papers will be honored with a title “Selected as Featured Paper” on the front page of the published papers.
  3. A certificate of recognition will be sent to the lead author of each award-winning paper (5–10 papers).

Procedure

  1. All submissions will be rigorously reviewed according to the Remote Sensing journal guidelines.
  2. Manuscripts that are not suitable for this Special Issue will be notified as soon as after consultation with the editorial board members. Authors of these manuscripts may still consider submitting in the urban remote sensing section or any other section of Remote Sensing as a regular paper. Other manuscripts will be forwarded for review.
  3. Manuscripts that are not selected as feature papers will be notified after the first round of reviews. The selection will be based on the review. Authors of those manuscripts that are not selected for the Special Issue may decide to revise and submit as a regular paper in the urban remote sensing section of the Remote Sensing journal. Please note that authors of these manuscripts need to shoulder the publication fees.
  4. Other manuscripts will be sent for a second round of reviews. However, this does not necessarily mean that a manuscript under the second round of reviews will be published as a feature paper. We will still seek comments and suggestions from reviewers.

Please contact Cris Wang ([email protected]), the managing editor, if you have any questions.

Prof. Soe Myint

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

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19 pages, 16081 KiB  
Article
Mapping 20 Years of Urban Expansion in 45 Urban Areas of Sub-Saharan Africa
by Yann Forget, Michal Shimoni, Marius Gilbert and Catherine Linard
Remote Sens. 2021, 13(3), 525; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030525 - 02 Feb 2021
Cited by 19 | Viewed by 4516
Abstract
By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the [...] Read more.
By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class. Full article
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28 pages, 12691 KiB  
Article
Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions
by Jairo Alejandro Gómez, ChengHe Guan, Pratyush Tripathy, Juan Carlos Duque, Santiago Passos, Michael Keith and Jialin Liu
Remote Sens. 2021, 13(3), 512; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030512 - 01 Feb 2021
Cited by 11 | Viewed by 5744
Abstract
With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and [...] Read more.
With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide. Full article
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22 pages, 1505 KiB  
Article
Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China
by Liang Chen, Xuelei Wang, Xiaobin Cai, Chao Yang and Xiaorong Lu
Remote Sens. 2021, 13(2), 323; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020323 - 19 Jan 2021
Cited by 45 | Viewed by 4550
Abstract
Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, [...] Read more.
Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city. Full article
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21 pages, 14354 KiB  
Article
Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis
by Joshua Hrisko, Prathap Ramamurthy, David Melecio-Vázquez and Jorge E. Gonzalez
Remote Sens. 2021, 13(1), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010059 - 25 Dec 2020
Cited by 3 | Viewed by 2464
Abstract
Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is [...] Read more.
Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance. Full article
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18 pages, 13912 KiB  
Article
A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features
by Tianyou Chu, Yumin Chen, Liheng Huang, Zhiqiang Xu and Huangyuan Tan
Remote Sens. 2020, 12(23), 3978; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233978 - 04 Dec 2020
Cited by 8 | Viewed by 2538
Abstract
Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The [...] Read more.
Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods. Full article
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16 pages, 17006 KiB  
Article
Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments
by David Malmgren-Hansen, Thomas Sohnesen, Peter Fisker and Javier Baez
Remote Sens. 2020, 12(15), 2409; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152409 - 27 Jul 2020
Cited by 13 | Viewed by 4575
Abstract
For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access [...] Read more.
For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access to cloud-free images is unlikely for days both before and after the main impact. In this situation, Synthetic Aperture Radar (SAR) data is a unique first source of information, as it works irrespective of weather and sunlight conditions. This paper shows, in the context of the cyclone Idai that hit Mozambique in March 2019, that Change Detection between pairs of SAR data is a perfect match with weather data, and therefore captures impact from the severe cyclone. For emergency operations, the filtering of Change Detections by external data on the location of houses prior to an event allows assessment of the impact on houses as opposed to impact on the surrounding natural environment. The free availability of SAR data from Sentinel-1, with further automated processing of it, means that this analysis is a cost-effective and quick potential first indication of cyclone destruction. Full article
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24 pages, 38240 KiB  
Article
Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning
by Emmanouil Panagiotou, Georgios Chochlakis, Lazaros Grammatikopoulos and Eleni Charou
Remote Sens. 2020, 12(12), 2002; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122002 - 22 Jun 2020
Cited by 13 | Viewed by 9064
Abstract
Generating Digital Elevation Models (DEM) from satellite imagery or other data sources constitutes an essential tool for a plethora of applications and disciplines, ranging from 3D flight planning and simulation, autonomous driving and satellite navigation, such as GPS, to modeling water flow, precision [...] Read more.
Generating Digital Elevation Models (DEM) from satellite imagery or other data sources constitutes an essential tool for a plethora of applications and disciplines, ranging from 3D flight planning and simulation, autonomous driving and satellite navigation, such as GPS, to modeling water flow, precision farming and forestry. The task of extracting this 3D geometry from a given surface hitherto requires a combination of appropriately collected corresponding samples and/or specialized equipment, as inferring the elevation from single image data is out of reach for contemporary approaches. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have experienced unprecedented growth in recent years as they can extrapolate rules in a data-driven manner and retrieve convoluted, nonlinear one-to-one mappings, such as an approximate mapping from satellite imagery to DEMs. Therefore, we propose an end-to-end Deep Learning (DL) approach to construct this mapping and to generate an absolute or relative point cloud estimation of a DEM given a single RGB satellite (Sentinel-2 imagery in this work) or drone image. The model has been readily extended to incorporate available information from the non-visible electromagnetic spectrum. Unlike existing methods, we only exploit one image for the production of the elevation data, rendering our approach less restrictive and constrained, but suboptimal compared to them at the same time. Moreover, recent advances in software and hardware allow us to make the inference and the generation extremely fast, even on moderate hardware. We deploy Conditional Generative Adversarial networks (CGAN), which are the state-of-the-art approach to image-to-image translation. We expect our work to serve as a springboard for further development in this field and to foster the integration of such methods in the process of generating, updating and analyzing DEMs. Full article
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21 pages, 3722 KiB  
Article
Using Earth Observation for Monitoring SDG 11.3.1-Ratio of Land Consumption Rate to Population Growth Rate in Mainland China
by Yunchen Wang, Chunlin Huang, Yaya Feng, Minyan Zhao and Juan Gu
Remote Sens. 2020, 12(3), 357; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030357 - 22 Jan 2020
Cited by 67 | Viewed by 5849
Abstract
Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this [...] Read more.
Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China. Full article
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20 pages, 2733 KiB  
Article
Building Footprint Extraction from Multispectral, Spaceborne Earth Observation Datasets Using a Structurally Optimized U-Net Convolutional Neural Network
by Giorgio Pasquali, Gianni Cristian Iannelli and Fabio Dell’Acqua
Remote Sens. 2019, 11(23), 2803; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11232803 - 27 Nov 2019
Cited by 21 | Viewed by 5921
Abstract
Building footprint detection and outlining from satellite imagery represents a very useful tool in many types of applications, ranging from population mapping to the monitoring of illegal development, from urban expansion monitoring to organizing prompter and more effective rescuer response in the case [...] Read more.
Building footprint detection and outlining from satellite imagery represents a very useful tool in many types of applications, ranging from population mapping to the monitoring of illegal development, from urban expansion monitoring to organizing prompter and more effective rescuer response in the case of catastrophic events. The problem of detecting building footprints in optical, multispectral satellite data is not easy to solve in a general way due to the extreme variability of material, shape, spatial, and spectral patterns that may come with disparate environmental conditions and construction practices rooted in different places across the globe. This difficult problem has been tackled in many different ways since multispectral satellite data at a sufficient spatial resolution started making its appearance on the public scene at the turn of the century. Whereas a typical approach, until recently, hinged on various combinations of spectral–spatial analysis and image processing techniques, in more recent times, the role of machine learning has undergone a progressive expansion. This is also testified by the appearance of online challenges like SpaceNet, which invite scholars to submit their own artificial intelligence (AI)-based, tailored solutions for building footprint detection in satellite data, and automatically compare and rank by accuracy the proposed maps. In this framework, after reviewing the state-of-the-art on this subject, we came to the conclusion that some improvement could be contributed to the so-called U-Net architecture, which has shown to be promising in this respect. In this work, we focused on the architecture of the U-Net to develop a suitable version for this task, capable of competing with the accuracy levels of past SpaceNet competition winners using only one model and one type of data. This achievement could pave the way for achieving better performances than the current state-of-the-art. All these results, indeed, have yet to be augmented through the integration of techniques that in the past have demonstrated a capability of improving the detection accuracy of U-net-based footprint detectors. The most notable cases are represented by an ensemble of different U-Net architectures, the integration of distance transform to improve boundary detection accuracy, and the incorporation of ancillary geospatial data on buildings. Our future work will incorporate those enhancements. Full article
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Review

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23 pages, 1019 KiB  
Review
Supporting Urban Weed Biosecurity Programs with Remote Sensing
by Kathryn Sheffield and Tony Dugdale
Remote Sens. 2020, 12(12), 2007; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122007 - 22 Jun 2020
Cited by 8 | Viewed by 3030
Abstract
Weeds can impact many ecosystems, including natural, urban and agricultural environments. This paper discusses core weed biosecurity program concepts and considerations for urban and peri-urban areas from a remote sensing perspective and reviews the contribution of remote sensing to weed detection and management [...] Read more.
Weeds can impact many ecosystems, including natural, urban and agricultural environments. This paper discusses core weed biosecurity program concepts and considerations for urban and peri-urban areas from a remote sensing perspective and reviews the contribution of remote sensing to weed detection and management in these environments. Urban and peri-urban landscapes are typically heterogenous ecosystems with a variety of vectors for invasive weed species introduction and dispersal. This diversity requires agile systems to support landscape-scale detection and monitoring, while accommodating more site-specific management and eradication goals. The integration of remote sensing technologies within biosecurity programs presents an opportunity to improve weed detection rates, the timeliness of surveillance, distribution and monitoring data availability, and the cost-effectiveness of surveillance and eradication efforts. A framework (the Weed Aerial Surveillance Program) is presented to support a structured approach to integrating multiple remote sensing technologies into urban and peri-urban weed biosecurity and invasive species management efforts. It is designed to support the translation of remote sensing science into operational management outcomes and promote more effective use of remote sensing technologies within biosecurity programs. Full article
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