scholarly journals Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

2016 ◽  
Vol 8 (9) ◽  
pp. 726 ◽  
Author(s):  
Margaret Kosmala ◽  
Alycia Crall ◽  
Rebecca Cheng ◽  
Koen Hufkens ◽  
Sandra Henderson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Citizen Science ◽  
Plant Phenology ◽  
Near Surface

PhenoCar: Assessment of phenology of thousands of trees along an environmental gradient using car mounted cameras and deep-learning based image segmentation

2020 ◽  
Author(s):  
David Basler ◽  
Andrew D. Richardson
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Plant Phenology ◽  
Fine Tuning ◽  
Human Observer ◽  
Near Surface ◽  
Tree Phenology ◽  
Vegetation Activity ◽  
The Impact

<p>The length of the period of vegetation activity is a significant driver of the global carbon cycle. Thus, the observation of plant phenology and seasonal vegetation dynamics has become an essential tool to quantify the impact of climate change on ecosystems. However, the accurate prediction of potential shifts of plant phenology in a warmer future requires a detailed spatio-temporal quantification of phenological patterns observed today. While phenological data derived from satellite-based remote sensing platforms often lack the spatial and/or temporal resolution to resolve the responses on the species level or to even reveal intraspecific patterns across the landscape, accurate visual observations by a human observer for thousands of trees are often not feasible due to time constraints. Therefore, we here present a novel near-surface remote sensing method that allowed the accurate tracking of tree phenology along an elevation- and urbanization gradient using a car-mounted camera. Using deep-learning-based image segmentation, we were able to track distinct patterns in the timing of leaf phenology of tens of thousands of trees along a nearly 100 km transect in New England throughout two growing seasons. The efficient collection of such high-resolution, multi-species, spatiotemporal data provides an excellent opportunity to quantify variation in tree phenology down to the level of individual organisms, across landscape and regional scales and for the fine-tuning of phenological models.</p>

scholarly journals Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 2001
Author(s):  
Antonella Boselli ◽  
Alessia Sannino ◽  
Mariagrazia D’Emilio ◽  
Xuan Wang ◽  
Salvatore Amoruso
Keyword(s):  
Remote Sensing ◽  
Biomass Burning ◽  
Ground Level ◽  
Large Area ◽  
Fire Event ◽  
Near Surface ◽  
Mean Values ◽  
Microphysical Properties ◽  
Observational Site ◽  
Biomass Burning Aerosol

During the summer of 2017, multiple huge fires occurred on Mount Vesuvius (Italy), dispersing a large quantity of ash in the surrounding area ensuing the burning of tens of hectares of Mediterranean scrub. The fires affected a very large area of the Vesuvius National Park and the smoke was driven by winds towards the city of Naples, causing daily peak values of particulate matter (PM) concentrations at ground level higher than the limit of the EU air quality directive. The smoke plume spreading over the area of Naples in this period was characterized by active (lidar) and passive (sun photometer) remote sensing as well as near-surface (optical particle counter) observational techniques. The measurements allowed us to follow both the PM variation at ground level and the vertical profile of fresh biomass burning aerosol as well as to analyze the optical and microphysical properties. The results evidenced the presence of a layer of fine mode aerosol with large mean values of optical depth (AOD > 0.25) and Ångstrom exponent (γ > 1.5) above the observational site. Moreover, the lidar ratio and aerosol linear depolarization obtained from the lidar observations were about 40 sr and 4%, respectively, consistent with the presence of biomass burning aerosol in the atmosphere.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

Regional characterisation of soil properties by combining soil science and hyperspectral and thermal remote sensing: A technical overview of lab and field remote sensing methods

2021 ◽  
Author(s):  
Richard Mommertz ◽  
Lars Konen ◽  
Martin Schodlok
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Regional Scale ◽  
Arable Land ◽  
Soil Mapping ◽  
Soil Science ◽  
Near Surface ◽  
Soil Parameter ◽  
The Impact ◽  
Parameter Retrieval

<p>Soil is one of the world’s most important natural resources for human livelihood as it provides food and clean water. Therefore, its preservation is of huge importance. For this purpose, a proficient regional database on soil properties is needed. The project “ReCharBo” (Regional Characterisation of Soil Properties) has the objective to combine remote sensing, geophysical and pedological methods to determine soil characteristics on a regional scale. Its aim is to characterise soils non-invasive, time and cost efficient and with a minimal number of soil samples to calibrate the measurements. Konen et al. (2021) give detailed information on the research concept and first field results in a presentation in the session “SSS10.3 Digital Soil Mapping and Assessment”. Hyperspectral remote sensing is a powerful and well known technique to characterise near surface soil properties. Depending on the sensor technology and the data quality, a wide variety of soil properties can be derived with remotely sensed data (Chabrillat et al. 2019, Stenberg et al. 2010). The project aims to investigate the effects of up and downscaling, namely which detail of information is preserved on a regional scale and how a change in scales affects the analysis algorithms and the possibility to retrieve valid soil parameter information. Thus, e.g. laboratory and field spectroscopy are applied to gain information of samples and fieldspots, respectively. Various UAV-based sensors, e.g. thermal & hyperspectral sensors, are applied to study soil properties of arable land in different study areas at field scale. Finally, airborne (helicopter) hyperspectral data will cover the regional scale. Additionally forthcoming spaceborne hyperspectral satellite data (e.g. Prisma, EnMAP, Sentinel-CHIME) are a promising outlook to gain detailed regional soil information. In this context it will be discussed how the multisensor data acquisition is best managed to optimise soil parameter retrieval. Sensor specific properties regarding time and date of acquisition as well as weather/atmospheric conditions are outlined. The presentation addresses and discusses the impact of a multisensor and multiscale remote sensing data collection regarding the results on soil parameter retrieval.</p><p> </p><p>References</p><p>Chabrillat, S., Ben-Dor, E. Cierniewski, J., Gomez, C., Schmid, T. & van Wesemael, B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 40:361–399. https://doi.org/10.1007/s10712-019-09524-0</p><p>Stenberg, B., Viscarra Rossel, R. A., Mounem Mouazen, A. & Wetterlind, J. (2010): Visible and Near Infrared Spectroscopy in Soil Science. In: Donald L. Sparks (editor): Advances in Agronomy. Vol. 107. Academic Press:163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7</p>

A Review of Unmanned Aerial Vehicles, Citizen Science, and Interferometry Remote Sensing in Landslide Hazards

2017 ◽  
pp. 469-491 ◽  
Author(s):  
Panagiotis Partsinevelos ◽  
Zacharias Agioutantis ◽  
Achilleas Tripolitsiotis ◽  
Nathaniel Schaefer
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Citizen Science ◽  
Aerial Vehicles ◽  
Landslide Hazards

Global carbon and water exchange during drought reflects adaptation of rooting depth to climate and topography

2021 ◽  
Author(s):  
Benjamin Stocker ◽  
Shersingh Tumber-Davila ◽  
Alexandra Konings ◽  
Rob Jackson
Keyword(s):  
Remote Sensing ◽  
Snow Accumulation ◽  
Rooting Depth ◽  
Small Scale ◽  
Observation Data ◽  
Systematic Bias ◽  
Thermal Infrared Remote Sensing ◽  
Near Surface ◽  
Rooting Zone ◽  
Zone Water

<p>The rooting zone water storage capacity (S) defines the total amount of water available to plants for transpiration during rain-free periods. Thereby, S determines the sensitivity of carbon and water exchanges between the land surface and the atmosphere, controls the sensitivity of ecosystem functioning to progressive drought conditions, and mediates feedbacks between soil moisture and near-surface air temperatures. While being a central quantity for water-carbon-climate coupling, S is inherently difficult to observe. Notwithstanding scarcity of observations, terrestrial biosphere and Earth system models rely on the specification of S either directly or indirectly through assuming plant rooting depth.</p><p>Here, we model S based on the assumption that plants size their rooting depth to maintain function under the expected maximum cumulative water deficit (CWD), occurring with a return period of 40 years (CWD<sub>X40</sub>), following Gao et al. (2014). CWD<sub>X40</sub> is “translated” into a rooting depth by accounting for the soil texture. CWD is defined as the cumulative evapotranspiration (ET) minus precipitation, where ET is estimated based on thermal infrared remote sensing (ALEXI-ET), and precipitation is from WATCH-WFDEI, modified by accounting for snow accumulation and melt. In contrast to other satellite remote sensing-based ET products, ALEXI-ET makes no a priori assumption about S and, as our evaluation shows, exhibits no systematic bias with increasing CWD. It thus provides a robust observation of surface water loss and enables estimation of S with global coverage at 0.05° (~5 km) resolution.</p><p>Modelled S and its variations across biomes is largely consistent with observed rooting depth, provided as ecosystem-level maximum estimates by Schenk et al. (2002), and a recently compiled comprehensive plant-level dataset. In spite of the general agreement of modelled and observed rooting depth across large climatic gradients, comparisons between local observations and global model predictions are mired by a scale mismatch that is particularly relevant for plant rooting depth, for which the small-scale topographical setting and hydrological conditions, in particular the water table depth, pose strong controls.</p><p>To resolve this limitation, we investigate the sensitivity of photosynthesis (estimated by sun-induced fluorescence, SIF), and of the evaporative fraction (EF, defined as ET over net radiation) to CWD. By employing first principles for the constraint of rooting zone water availability on ET and photosynthesis, it can be derived how their sensitivity to the increasing CWD relates to S. We make use of this relationship to provide an alternative and independent estimate of S (S<sub>dSIF</sub> and S<sub>dEF</sub>), informed by Earth observation data, to which S, modelled using CWD<sub>X40</sub>, can be compared. Our comparison reveals a strong correlation (R<sup>2</sup>=0.54) and tight consistency in magnitude between the two approaches for estimating S. </p><p>Our analysis suggests adaptation of plant structure to prevailing climatic conditions and drought regimes across the globe and at catchment scale and demonstrates its implications for land-atmosphere exchange. Our global high-resolution mapping of S reveals contrasts between plant growth forms (grasslands vs. forests) and a discrepant importance across the landscape of plants’ access to water stored at depth, and enables an observation-informed specification of S in global models.</p>

scholarly journals Investigating the Retention of Solar Wind Implanted Helium-3 on the Moon from the Analysis of Multi-Wavelength Remote Sensing Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3350
Author(s):  
Shashwat Shukla ◽  
Valentyn Tolpekin ◽  
Shashi Kumar ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Solar Wind ◽  
Remote Sensing Data ◽  
Physical Nature ◽  
The Moon ◽  
Near Surface ◽  
Energy Demands ◽  
Multi Wavelength ◽  
High Dielectric ◽  
Weathering Effects

The Moon has a large potential for space exploration and mining valuable resources. In particular, 3He provides rich sources of non-radioactive fusion fuel to fulfill cislunar and Earth’s energy demands, if found economically feasible. The present study focuses on developing advanced techniques to prospect 3He resources on the Moon from multi-sensor remote sensing perspectives. It characterizes optical changes in regolith materials due to space weathering as a new retention parameter and introduces a novel machine learning inversion model for retrieving the physical properties of the regolith. Our analysis suggests that the reddening of the soil predominantly governs the retention, along with attenuated mafic band depths. Moreover, semi-variograms show that the spatial variability of 3He is aligned with the episodic weathering events at different timescales. We also observed that pyroclastic regoliths with high dielectric constant and increased surface scattering mechanisms exhibited a 3He abundant region. For ejecta cover, the retention was weakly associated with the dielectric contrast and a circular polarization ratio (CPR), mainly because of the 3He-deficient nature of the regolith. Furthermore, cross-variograms revealed inherent cyclicity attributed to the sequential process of weathering effects. Our study provides new insights into the physical nature and near-surface alterations of lunar regoliths that influence the spatial distribution and retention of solar wind implanted 3He.

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