An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

Snow albedo is highly variable over multiple temporal and spatial scales. This variability is more pronounced in areas that experience seasonal snowpack. Satellite retrievals, physically based models and parameterizations for snow albedo all require ground-based measurements for calibration, initialization, and validation. Ground measurements are generally made using upward and downward-facing pyranometers at opportunistically located weather stations that are sparsely distributed, particularly in mountainous regions. These station-based measurements cannot capture the spatial variability of albedo across the land surface. Uncrewed Aerial Vehicles (UAVs) equipped with upward and downward-facing pyranometers provide near-surface measurements of broadband albedo that are spatially distributed across landscapes, offering improvements over in-situ sensors. At the hillslope to watershed scale albedo measurements from UAVs taken over heterogeneous terrain are a function of the spatial variability in albedo and topography within the downward-facing sensor’s field-of-view (FOV). In this research we propose methods for topographic correction of UAV snow albedo measurements and comparison to gridded satellite albedo products. These methods account for the variability of surface topography and albedo within the sensor FOV, sensor tilt, and the angular response of pyranometers. We applied the proposed methodologies to UAV snow albedo measurements collected over an alpine meadow in southwest Montana, United States (45.23°, −111.28°). Sensitivity analyses were conducted to determine the effect of altering the processing FOV (PFOV) for both topographic corrections and comparison to coincident Landsat 8-derived albedo measurements. Validation from ground-based albedo measurements showed the topographic correction to reduce albedo measurement error considerably over mildly sloping terrain. Our sensitivity analyses demonstrated that outcomes from the topographic correction and satellite comparison are highly dependent on the specified PFOV. Based on field observations and analyses of UAV albedo measurements made at different altitudes, we provide guidelines for strategizing future UAV albedo surveys. This research presents considerable advances in the standardization of UAV-based albedo measurement. We establish the foundation for future research to utilize this platform to collect near-surface validation measurements over heterogeneous terrain with high accuracy and consistency.

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Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

Remote Sensing ◽

10.3390/rs11222619 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2619 ◽

Cited By ~ 1

Author(s):

Enping ◽

Yunlin ◽

Hui ◽

Guangxing ◽

Dengkui

Keyword(s):

Density Estimation ◽

Forest Carbon ◽

Landsat 8 ◽

Moving Window ◽

Mountainous Areas ◽

Carbon Density ◽

Topographic Correction ◽

Mountainous Regions ◽

Better Than

Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

10.5194/acp-2017-799 ◽

2017 ◽

Author(s):

Chenglai Wu ◽

Xiaohong Liu ◽

Zhaohui Lin ◽

Stefan R. Rahimi-Esfarjani ◽

Zheng Lu

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Rocky Mountain ◽

Hydrologic Cycle ◽

Mountain Region ◽

Near Surface ◽

Variable Resolution ◽

Snow Albedo ◽

Mountainous Regions ◽

Rocky Mountain Region

Abstract. Deposition of light-absorbing aerosols (LAAs) such as black carbon (BC) and dust onto snow cover has been suggested to reduce the snow albedo, and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined high-resolution (0.125º) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period of 1981–2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow, and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations. Although the model underestimates near-surface atmospheric BC concentrations, the simulated BC-in-snow concentrations are overall comparable to observations. Regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6–1.7 W m−2 in spring, and dust contributes to about 21–43 % of total SRE. Due to positive snow-albedo feedbacks induced by the LAAs' SRE, snow water equivalent reduces by 2–50 mm and snow cover fraction by 5–20 % in the two regions around the mountains (Eastern Snake River Plain and Southwestern Wyoming), corresponding to an increase of surface air temperature by 0.9–1.1 °C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15–1.00 mm day−1 in April–May and reductions of 0.04–0.18 mm day−1 in June–July in the mountainous regions. Of all the mountainous regions, Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snow melt (i.e., June and July). Our results highlight the potentially important role of LAA interactions with snowpack and subsequent impacts on the hydrologic cycles across the Rocky Mountains.

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CALIBRATION/VALIDATION OF LANDSAT-DERIVED OCEAN COLOUR PRODUCTS IN BOSTON HARBOUR

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1165-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1165-1168

Author(s):

Nima Pahlevan ◽

Patrick Sheldon ◽

Francesco Peri ◽

Jianwei Wei ◽

Zhehai Shang ◽

...

Keyword(s):

Spatial Scales ◽

Atmospheric Correction ◽

Optical Data ◽

First Year ◽

Landsat 8 ◽

Boston Harbor ◽

Massachusetts Bay ◽

Ocean Colour ◽

Near Surface ◽

Optical Images

The Landsat data archive provides a unique opportunity to investigate the long-term evolution of coastal ecosystems at fine spatial scales that cannot be resolved by ocean colour (OC) satellite sensors. Recognizing Landsat’s limitations in applications over coastal waters, we have launched a series of field campaigns in Boston Harbor and Massachusetts Bay (MA, USA) to validate OC products derived from Landsat-8. We will provide a preliminary demonstration on the calibration/validation of the existing OC algorithms (atmospheric correction and in-water optical properties) to enhance monitoring efforts in Boston Harbor. To do so, Landsat optical images were first compared against ocean colour products over high-latitude regions. The in situ cruise data, including optical data (remote sensing reflectance) and water samples were analyzed to obtain insights into the optical and biogeochemical properties of near-surface waters. Along with the cruise data, three buoys were deployed in three locations across the Harbor to complement our database of concentrations of chlorophyll a, total suspended solids (TSS), and absorption of colour dissolved organic matter (CDOM). The data collected during the first year of the project are used to develop and/or tune OC algorithms. The data will be combined with historic field data to map in-water constituents back to the early 1990’s. This paper presents preliminary analysis of some of the data collected under Landsat-8 overpasses.

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Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

Hydrology ◽

10.3390/hydrology7030041 ◽

2020 ◽

Vol 7 (3) ◽

pp. 41

Author(s):

Eric A. Sproles ◽

Andrew Mullen ◽

Jordy Hendrikx ◽

Charles Gatebe ◽

Suzi Taylor

Keyword(s):

Spatial Resolution ◽

Spatial Scales ◽

Satellite Measurements ◽

Snow Albedo ◽

Mountainous Regions ◽

Clear Sky ◽

Dual Sensor ◽

Forested Areas ◽

Technical Advances ◽

Aerial Vehicle

We present technical advances and methods to measure effective broadband physical albedo in snowy mountain headwaters using a prototype dual-sensor pyranometer mounted on an Autonomous Aerial Vehicle (an AAV). Our test flights over snowy meadows and forested areas performed well during both clear sky and snowy/windy conditions at an elevation of ~2650 m above mean sea level (MSL). Our AAV-pyranometer platform provided high spatial (m) and temporal resolution (sec) measurements of effective broadband (310–2700 nm) surface albedo. The AAV-based measurements reveal spatially explicit changes in landscape albedo that are not present in concurrent satellite measurements from Landsat and MODIS due to a higher spatial resolution. This AAV capability is needed for validation of satellite snow albedo products, especially over variable montane landscapes at spatial scales of critical importance to hydrological applications. Effectively measuring albedo is important, as annually the seasonal accumulation and melt of mountain snowpack represent a dramatic transformation of Earth’s albedo, which directly affects headwaters’ water and energy cycles.

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Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-511-2018 ◽

2018 ◽

Vol 18 (2) ◽

pp. 511-533 ◽

Cited By ~ 15

Author(s):

Chenglai Wu ◽

Xiaohong Liu ◽

Zhaohui Lin ◽

Stefan R. Rahimi-Esfarjani ◽

Zheng Lu

Keyword(s):

Snow Cover ◽

Rocky Mountain ◽

Hydrologic Cycle ◽

Mountain Region ◽

Dust Particles ◽

Near Surface ◽

Variable Resolution ◽

Snow Albedo ◽

Mountainous Regions ◽

Rocky Mountain Region

Abstract. The deposition of light-absorbing aerosols (LAAs), such as black carbon (BC) and dust, onto snow cover has been suggested to reduce the snow albedo and modulate the snowpack and consequent hydrologic cycle. In this study we use the variable-resolution Community Earth System Model (VR-CESM) with a regionally refined high-resolution (0.125°) grid to quantify the impacts of LAAs in snow in the Rocky Mountain region during the period 1981–2005. We first evaluate the model simulation of LAA concentrations both near the surface and in snow and then investigate the snowpack and runoff changes induced by LAAs in snow. The model simulates similar magnitudes of near-surface atmospheric dust concentrations as observations in the Rocky Mountain region. Although the model underestimates near-surface atmospheric BC concentrations, the model overestimates BC-in-snow concentrations by 35 % on average. The regional mean surface radiative effect (SRE) due to LAAs in snow reaches up to 0.6–1.7 W m−2 in spring, and dust contributes to about 21–42 % of total SRE. Due to positive snow albedo feedbacks induced by the LAA SRE, snow water equivalent is reduced by 2–50 mm and snow cover fraction by 5–20 % in the two regions around the mountains (eastern Snake River Plain and southwestern Wyoming), corresponding to an increase in surface air temperature by 0.9–1.1 °C. During the snow melting period, LAAs accelerate the hydrologic cycle with monthly runoff increases of 0.15–1.00 mm day−1 in April–May and reductions of 0.04–0.18 mm day−1 in June–July in the mountainous regions. Of all the mountainous regions, the Southern Rockies experience the largest reduction of total runoff by 15 % during the later stage of snowmelt (i.e., June and July). Compared to previous studies based on field observations, our estimation of dust-induced SRE is generally 1 order of magnitude smaller in the Southern Rockies, which is ascribed to the omission of larger dust particles (with the diameter > 10 µm) in the model. This calls for the inclusion of larger dust particles in the model to reduce the discrepancies. Overall these results highlight the potentially important role of LAA interactions with snowpack and the subsequent impacts on the hydrologic cycles across the Rocky Mountains.

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CALIBRATION/VALIDATION OF LANDSAT-DERIVED OCEAN COLOUR PRODUCTS IN BOSTON HARBOUR

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1165-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1165-1168

Author(s):

Nima Pahlevan ◽

Patrick Sheldon ◽

Francesco Peri ◽

Jianwei Wei ◽

Zhehai Shang ◽

...

Keyword(s):

Spatial Scales ◽

Atmospheric Correction ◽

Optical Data ◽

First Year ◽

Landsat 8 ◽

Boston Harbor ◽

Massachusetts Bay ◽

Ocean Colour ◽

Near Surface ◽

Optical Images

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Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

Pakistan Journal of Engineering Technology & Science ◽

10.22555/pjets.v5i2.920 ◽

2016 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Muhammad Danish Siddiqui ◽

Arjumand Z Zaidi

Keyword(s):

Satellite Data ◽

Spatial Scales ◽

Economic Value ◽

Vegetation Indices ◽

Cost Effective ◽

Geographical Information ◽

Landsat 8 ◽

Coverage Area ◽

Breeding Sites ◽

Marine Plant

Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation wasachieved with WV-2 data that shows 15.5Ha (0.155 Km2)of seaweed cover along Karachi coast that is more representative of the field observed data. A much larger area wasestimated with Landsat 8 image (71.28Ha or 0.7128 Km2) that was mainly due to the mixing of seaweed pixels with water pixels. The WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat 8 in mapping seaweed patches

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First record of the occurrence of sea ice in the Cordillera Darwin fjords (54°S), Chile

Annals of Glaciology ◽

10.1017/aog.2021.3 ◽

2021 ◽

pp. 1-11

Author(s):

Charles Salame ◽

Inti Gonzalez ◽

Rodrigo Gomez-Fell ◽

Ricardo Jaña ◽

Jorge Arigony-Neto

Keyword(s):

Time Series ◽

Sea Ice ◽

First Record ◽

Ice Formation ◽

Future Research ◽

Landsat 8 ◽

Surface Salinity ◽

Air Temperatures ◽

Aerial Reconnaissance ◽

High Precipitation

Abstract This paper provides the first evidence for sea-ice formation in the Cordillera Darwin (CD) fjords in southern Chile, which is farther north than sea ice has previously been reported for the Southern Hemisphere. Initially observed from a passenger plane in September 2015, the presence of sea ice was then confirmed by aerial reconnaissance and subsequently identified in satellite imagery. A time series of Sentinel-1 and Landsat-8 images during austral winter 2015 was used to examine the chronology of sea-ice formation in the Cuevas fjord. A longer time series of imagery across the CD was analyzed from 2000 to 2017 and revealed that sea ice had formed in each of the 13 fjords during at least one winter and was present in some fjords during a majority of the years. Sea ice is more common in the northern end of the CD, compared to the south where sea ice is not typically present. Is suggested that surface freshening from melting glaciers and high precipitation reduces surface salinity and promotes sea-ice formation within the semi-enclosed fjord system during prolonged periods of cold air temperatures. This is a unique set of initial observations that identify questions for future research in this remote area.

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Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

Sustainability ◽

10.3390/su11041163 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1163 ◽

Cited By ~ 4

Author(s):

Melissa Bedinger ◽

Lindsay Beevers ◽

Lila Collet ◽

Annie Visser

Keyword(s):

Climate Change ◽

Human Nature ◽

Climate Change Adaptation ◽

Time Scales ◽

Spatial Scales ◽

Local Context ◽

Multiple Time Scales ◽

Future Research ◽

Multiple Time ◽

Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

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Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS

Wildlife Research ◽

10.1071/wr08153 ◽

2009 ◽

Vol 36 (7) ◽

pp. 553 ◽

Cited By ~ 10

Author(s):

Z. Austin ◽

S. Cinderby ◽

J. C. R. Smart ◽

D. Raffaelli ◽

P. C. L. White

Keyword(s):

Community Involvement ◽

Spatial Data ◽

Spatial Scales ◽

Stakeholder Participation ◽

Landscape Scale ◽

Scientific Data ◽

Future Research ◽

Economic Damage ◽

Participatory Gis ◽

Wild Deer

Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale. Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example. Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps. Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region. Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management. Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.

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