scholarly journals Remote Sensing of Ecosystem Structure: Fusing Passive and Active Remotely Sensed Data to Characterize a Deltaic Wetland Landscape

2020 ◽  
Vol 12 (22) ◽  
pp. 3819 ◽  
Author(s):  
Daniel Peters ◽  
K. Olaf Niemann ◽  
Robert Skelly
Keyword(s):  
Remotely Sensed ◽  
Ecosystem Structure ◽  
Remotely Sensed Data ◽  
Horizontal Structure ◽  
Multiple Sensors ◽  
Data Mining Approach ◽  
Input Variables ◽  
Future Work ◽  
The Impact ◽  
Northern Alberta

A project was constructed to integrate remotely sensed data from multiple sensors and platforms to characterize range of ecosystem characteristics in the Peace–Athabasca Delta in Northern Alberta, Canada. The objective of this project was to provide a framework for the processing of multisensor data to extract ecosystem information describing complex deltaic wetland environments. The data used in this study was based on a passive satellite-based earth observation multispectral sensor (Sentinel-2) and airborne discrete light detection and ranging (LiDAR). The data processing strategy adopted here allowed us to employ a data mining approach to grouping of the input variables into ecologically meaningful clusters. Using this approach, we described not only the reflective characteristics of the cover, but also ascribe vertical and horizontal structure, thereby differentiating spectrally similar, but ecologically distinct, ground features. This methodology provides a framework for assessing the impact of ecosystems on radiance, as measured by Earth observing systems, where it forms the basis for sampling and analysis. This final point will be the focus of future work.

scholarly journals Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ai-Ling Jiang ◽  
Ming-Chieh Lee ◽  
Guofa Zhou ◽  
Daibin Zhong ◽  
Dawit Hawaria ◽  
...  
Keyword(s):  
Malaria Vector ◽  
Malaria Control ◽  
Remotely Sensed ◽  
Larval Habitat ◽  
Validation Dataset ◽  
Significant Shift ◽  
Remotely Sensed Data ◽  
Larval Habitats ◽  
Common Land Model ◽  
The Impact

AbstractLarval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data

2007 ◽  
Vol 109 (3) ◽  
pp. 314-327 ◽  
Author(s):  
Izaya Numata ◽  
Dar A. Roberts ◽  
Oliver A. Chadwick ◽  
Josh Schimel ◽  
Fernando R. Sampaio ◽  
...  
Keyword(s):  
Grazing Intensity ◽  
Remotely Sensed ◽  
Biophysical Properties ◽  
Remotely Sensed Data ◽  
The Impact

scholarly journals Exceptional retreat of Novaya Zemlya's marine-terminating outlet glaciers between 2000 and 2013

2017 ◽  
Author(s):  
J. Rachel Carr ◽  
Heather Bell ◽  
Rebecca Killick ◽  
Tom Holt
Keyword(s):  
Sea Ice ◽  
Sea Level Rise ◽  
Sea Level ◽  
Remotely Sensed ◽  
Glacier Retreat ◽  
Remotely Sensed Data ◽  
Novaya Zemlya ◽  
The Past ◽  
Air Temperatures ◽  
The Impact

Abstract. Novaya Zemlya (NVZ) has experienced rapid ice loss and accelerated marine-terminating glacier retreat during the past two decades. However, it is unknown whether this retreat is exceptional longer-term and/or whether it has persisted since 2010. Investigating this is vital, as dynamic thinning may contribute substantially to ice loss from NVZ, but is not currently included in sea level rise predictions. Here, we use remotely sensed data to assess controls on NVZ glacier retreat between the 1973/6 and 2015. Glaciers that terminate into lakes or the ocean receded 3.5 times faster than those that terminate on land. Between 2000 and 2013, retreat rates were significantly higher on marine-terminating outlet glaciers than during the previous 27 years, and we observe widespread slow-down in retreat, and even advance, between 2013 and 2015. There were some common patterns in the timing of glacier retreat, but the magnitude varied between individual glaciers. Rapid retreat between 2000–2013 corresponds to a period of significantly warmer air temperatures and reduced sea ice concentrations, and to changes in the NAO and AMO. We need to assess the impact of this accelerated retreat on dynamic ice losses from NVZ, to accurately quantify its future sea level rise contribution.

scholarly journals Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1837 ◽  
Author(s):  
James Brinkhoff ◽  
Brian W. Dunn ◽  
Andrew J. Robson ◽  
Tina S. Dunn ◽  
Remy L. Dehaan
Keyword(s):  
Vegetation Index ◽  
Field Experiments ◽  
N Uptake ◽  
Satellite Image ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Multispectral Data ◽  
Input Variables ◽  
Better Than

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren’t used to train the models.

Measuring river planform changes from remotely-sensed data: A Monte-Carlo approach to assess the impact of spatially-variable geometric error.

2020 ◽  
Author(s):  
Timothée Jautzy ◽  
Pierre-Alexis Herrault ◽  
Valentin Chardon ◽  
Laurent Schmitt ◽  
Gilles Rixhon
Keyword(s):  
Monte Carlo ◽  
Confidence Level ◽  
Anthropogenic Activities ◽  
Independent Set ◽  
Remotely Sensed ◽  
Geometric Error ◽  
Remotely Sensed Data ◽  
River Planform ◽  
Active Channels ◽  
The Impact

&lt;p&gt;A majority of European rivers have been extensively affected by diverse anthropogenic activities, including e.g. channelization, regulation and sediment mining. Against this background, the planimetric analysis based on remotely-sensed data is frequently used to evaluate historical planform changes, eventually leading to quantification of migration rates. However, geometric spatially-variable (SV) error inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially on mid-sized rivers. We therefore address the following issue: What is the impact of spatially-variable error on the quantification of surfacic river planform changes?&lt;/p&gt;&lt;p&gt;Our test river corresponds to a 20 m wide meandering sub-tributary of the Upper Rhine, the Lower Bruche. Within four, geomorphologically-diverse sub-reaches, the active channel is digitised using diachronic orthophotos (1950; 1964) and the SV-error affecting the data is interpolated with an Inverse Distance Weighting technique based on an independent set of ground control points. As a second step, the main novelty of our approach consists in running Monte-Carlo (MC) simulations to randomly translate active channels according to the interpolated SV-error. This eventually allows to display the relative margin of error (RME) associated with measured eroded and/or deposited surfaces for each sub-reach through MC simulations, illustrating the confidence level in the respective measurements of our river planform changes.&lt;/p&gt;&lt;p&gt;Our results suggest that (i) SV-error strongly affects the significance of measured changes and (ii) the confidence level might be dependent not only on magnitude of changes but also on their shapes. Taking SV-error into account is strongly recommended, regardless of the remotely-sensed data used. This is particularly true for mid-sized rivers and/or low amplitude river planform changes, especially in the aim of their sustainable management and/or restoration. Finally, our methodology is transferrable to different fluvial styles.&lt;/p&gt;

Estimation of vegetative fuel loads using Landsat TM imagery in New South Wales, Australia

2003 ◽  
Vol 12 (2) ◽  
pp. 185 ◽  
Author(s):  
Kate Brandis ◽  
Carol Jacobson
Keyword(s):  
Fire History ◽  
Spatial Information ◽  
New South ◽  
New South Wales ◽  
Remotely Sensed ◽  
Landsat Tm ◽  
Remotely Sensed Data ◽  
Fuel Loads ◽  
South Wales ◽  
The Impact

Fuel loads in forest areas are dependent on vegetation type and the time since the last fire. This paper reports a study on the feasibility of using remotely sensed data to estimate vegetative fuel loads. It describes two methods for estimating fuel loads using Landsat TM data based on equations describing litter accumulation and decomposition. The first method uses classification techniques to predict vegetation types coupled with fire history data to derive current fuel loads. The second method applies a canopy turnover rate to estimate litterfall and subsequently accumulated litter from biomass, thus utilising the dominant influence of canopy on remotely sensed data. Both methods are compared with data collected from Popran National Park in coastal New South Wales. The amounts of litter calculated with the biomass method were similar to field results, but the classification method was found to overestimate fuel loads. A sensitivity analysis investigated the impact of varying the vegetation constants and rates used in the fuel estimates to simulate uncertainty or error in their values. The biomass method was less subject to uncertainties and has potential for estimating fuel quantities to provide useful spatial information for fire managers.

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