Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas buff.ly/4gH8Mxj #mdpiremotesensing via
@RemoteSens_MDPI
@project_S34i

🌍🛰️ New research!
We benchmarked #PlanetScope’s performance in detecting Fe³⁺-bearing iron oxides vs #Sentinel2, #ASTER, and #Landsat9.
📘 Full open-access article: www.mdpi.com/2072-4292/17...#Antimony #RemoteSensing #bandratio #satellite #mdpiremotesensing #mineralexploration #criticalrawmaterials

🌍🛰️ New research!
We benchmarked #PlanetScope’s performance in detecting Fe³⁺-bearing iron oxides vs #Sentinel2, #ASTER, and #Landsat9.
📘 Full open-access article: www.mdpi.com/2072-4292/17...
#Antimony #RemoteSensing #bandratio #satellite #mdpiremotesensing #mineralexploration #criticalrawmaterials

Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas buff.ly/4gH8Mxj #mdpiremotesensing via
@RemoteSens_MDPI
@project_S34i

🚨New paper alert 🚨
Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas buff.ly/4gH8Mxj #mdpiremotesensing via
@RemoteSens_MDPI
@project_S34i

Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas The demand for Critical Raw Materials (CRM) is increasing due to the need to decarbonize economies and transition to a sustainable low-carbon future achieving climate goals. To address this, the…

🚨New paper alert 🚨
Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas https://buff.ly/4gH8Mxj #mdpiremotesensing

Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas The demand for Critical Raw Materials (CRM) is increasing due to the need to decarbonize economies and transition to a sustainable low-carbon future achieving climate goals. To address this, the…

🚨New paper alert 🚨
Comparative Performance of Sentinel-2 and Landsat-9 Data for Raw Materials’ Exploration Onshore and in Coastal Areas https://buff.ly/4gH8Mxj #mdpiremotesensing via
@RemoteSens_MDPI

Let's start the week with this study showing how extreme droughts can severely affect crop yields & how monitor the impacts of drought on crops with Sentinel-2
Adrià Descals @jpreixach.bsky.social @csic.es @creaf.cat

mdpi.com/3141658 #mdpiremotesensing via
@RemoteSens_MDPI

Discussion Points of the Remote Sensing Study and Integrated Analysis of the Archaeological Landscape of Rujm el-Hiri Remote sensing techniques provide crucial insights into ancient settlement patterns in various regions by uncovering previously unknown archaeological sites and clarifying the topological features of ...

Discussion Points of the Remote Sensing Study and Integrated Analysis of the Archaeological Landscape of Rujm el-Hiri www.mdpi.com/3041292 #mdpiremotesensing

Risk Prediction of Coastal Hazards Induced by Typhoon: A Case Study in the Coastal Region of Shenzhen, China This study presents a risk prediction of coastal hazards induced by typhoons, which are a severe natural hazard that often occur in coastal regions. Taking the coastal hazards happened in Shenzhen as ...

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Risk Prediction of Coastal Hazards Induced by Typhoon: A Case Study in the Coastal Region of Shenzhen, China www.mdpi.com/728184 #mdpiremotesensing @RemoteSens_MDPIより

Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.

"Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development" https://www.mdpi.com/2072-4292/11/15/1797 via @RemoteSens_MDPI #mdpiremotesensing #openaccess cc @oceanpractices

Fire on the Water Towers: Mapping Burn Scars on Mount Ken... Mount Kenya is one of Kenya’s ‘water towers’, the headwat...

Our paper is out! Click through to full paper (PDF or HTML). "Fire on the Water Towers: Mapping Burn Scars on Mount Kenya Using Satellite Data to Reconstruct Recent Fire History" http://www.mdpi.com/392054 @RemoteSens_MDPI #mdpiremotesensing #fire #Kenya #MODIS #Landsat #dNBR

Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas.

#mdpiremotesensing Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle@RemoteSens_MDPI

mdpi.com/2072-4292/9/3/…

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover.

#mdpiremotesensing Collect Earth: Land Use and Land Cover Assessment through Augmented Visual http://www.mdpi.com/2072-4292/8/10/807 @RemoteSens_MDPI

Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection Land cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications.

#mdpiremotesensing Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection@RemoteSens_MDPI

mdpi.com/2072-4292/8/6/…