scholarly journals Spatio-Temporal Variability in Remotely Sensed Vegetation Greenness Across Yellowstone National Park

2019 ◽  
Vol 11 (7) ◽  
pp. 798 ◽  
Author(s):  
Michael Notaro ◽  
Kristen Emmett ◽  
Donal O’Leary
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Yellowstone National Park ◽  
National Park ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Climate Change Signal ◽  
Summer Drought ◽  
Remotely Sensed Data ◽  
Vegetation Greenness

The study’s objective was to quantify the responses of vegetation greenness and productivity to climate variability and change across complex topographic, climatic, and ecological gradients in Yellowstone National Park through the use of remotely sensed data. The climate change signal in Yellowstone was pronounced, including substantial warming, an abrupt decline in snowpack, and more frequent droughts. While phenological studies are increasing in Yellowstone, the near absence of long-term and continuous ground-based phenological measurements motivated the study’s application of remotely sensed data to aid in identifying ecological vulnerabilities and guide resource management in light of on ongoing environmental change. Correlation, time-series, and empirical orthogonal function analyses for 1982–2015 focused on Daymet data and vegetation indices (VIs) from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The study’s key questions address unique time scales. First, what are the dominant meteorological drivers of variability in vegetation greenness on seasonal to interannual time scales? Key results include: (1) Green-up is the most elevation- and climate-sensitive phenological stage, with La Niña-induced cool, wet conditions or an anomalously deep snowpack delaying the green-up wave. (2) Drought measures were the dominant contributors towards phenological variability, as winter–spring drought corresponded to enhanced April–June greening and spring–summer drought corresponded to reduced August–September greening. Second, how have patterns of productivity changed in response to climate change and disturbances? Key results include: (1) The park predominantly exhibited positive productivity trends, associated with lodgepole pine re-establishment and growth following the 1988 fires. (2) Landscapes which were undisturbed by the 1988 fires showed no apparent sign of warming-induced greening. This study motivates a systematic investigation of remote-sensing data across western parks to identify ecological vulnerabilities and support the development of climate change vulnerability assessments and adaptation strategies.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

2021 ◽  
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Full Range ◽  
Time Lapse ◽  
Test Site ◽  
Remotely Sensed ◽  
Optical Behaviour ◽  
Remotely Sensed Data ◽  
Snow Cover Area ◽  
Significant Information

<p><span>Polar areas are the most sensitive targets of </span><span>the </span><span>climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in a</span><span>n </span><span>Ar</span><span>c</span><span>tic </span><span>test-site </span><span>(Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the </span><span>Amundsen - Nobile</span><span> Climate Change Tower (Ny </span><span>Å</span><span>lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, </span><span>therefore,</span><span> to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.</span></p>

scholarly journals Remote Sensing Approaches and Related Techniques to Map and Study Landslides

2020 ◽  
Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin
Keyword(s):  
Remote Sensing ◽  
Hazard Assessment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Landslide Monitoring ◽  
In Situ Observation ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

scholarly journals Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night

2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Important Indicator ◽  
Artificial Lighting ◽  
Sensing Data ◽  
Dragon Fruit ◽  
Positive Correlations

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.

Earth’s Energy Imbalance

2014 ◽  
Vol 27 (9) ◽  
pp. 3129-3144 ◽  
Author(s):  
Kevin E. Trenberth ◽  
John T. Fasullo ◽  
Magdalena A. Balmaseda
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Radiative Forcing ◽  
Heat Content ◽  
Volcanic Eruptions ◽  
Climate Change Signal ◽  
Ocean Reanalysis ◽  
Climate System Model ◽  
Energy Imbalance ◽  
Rates Of Change

Abstract Climate change from increased greenhouse gases arises from a global energy imbalance at the top of the atmosphere (TOA). TOA measurements of radiation from space can track changes over time but lack absolute accuracy. An inventory of energy storage changes shows that over 90% of the imbalance is manifested as a rise in ocean heat content (OHC). Data from the Ocean Reanalysis System, version 4 (ORAS4), and other OHC-estimated rates of change are used to compare with model-based estimates of TOA energy imbalance [from the Community Climate System Model, version 4 (CCSM4)] and with TOA satellite measurements for the year 2000 onward. Most ocean-only OHC analyses extend to only 700-m depth, have large discrepancies among the rates of change of OHC, and do not resolve interannual variability adequately to capture ENSO and volcanic eruption effects, all aspects that are improved with assimilation of multivariate data. ORAS4 rates of change of OHC quantitatively agree with the radiative forcing estimates of impacts of the three major volcanic eruptions since 1960 (Mt. Agung, 1963; El Chichón, 1982; and Mt. Pinatubo, 1991). The natural variability of the energy imbalance is substantial from month to month, associated with cloud and weather variations, and interannually mainly associated with ENSO, while the sun affects 15% of the climate change signal on decadal time scales. All estimates (OHC and TOA) show that over the past decade the energy imbalance ranges between about 0.5 and 1 W m−2. By using the full-depth ocean, there is a better overall accounting for energy, but discrepancies remain at interannual time scales between OHC- and TOA-based estimates, notably in 2008/09.

scholarly journals OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 185-189
Author(s):  
J. Murray ◽  
I. Sargent ◽  
D. Holland ◽  
A. Gardiner ◽  
K. Dionysopoulou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Feature Extraction ◽  
Remote Sensing Data ◽  
Field Work ◽  
Remotely Sensed ◽  
Network Architectures ◽  
Remotely Sensed Data ◽  
Ordnance Survey ◽  
Extensive Field

Abstract. National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows.

Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1389
Author(s):  
Kamini Yadav ◽  
Hatim M. E. Geli
Keyword(s):  
Climate Change ◽  
New Mexico ◽  
Crop Yield ◽  
Crop Yields ◽  
Production Systems ◽  
Remotely Sensed ◽  
Adaptation Strategies ◽  
Yield Prediction ◽  
Climate Variables ◽  
Remotely Sensed Data

Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.

Volumetric Determination of Marine Oil Spills Using Coordinated Airborne and Surface Sampling Data

1973 ◽  
Vol 1973 (1) ◽  
pp. 117-125
Author(s):  
J. E. Estes ◽  
P. G. Mikolaj ◽  
R. R. Thaman ◽  
L. W. Senger
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Oil Pollution ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Coast Guard ◽  
Surveillance Systems ◽  
Surface Sampling ◽  
Remotely Sensed Data ◽  
Marine Oil

ABSTRACT The detection, measurement, and monitoring of oil pollution in the marine environment are receiving increased attention owing to: I) the growing incidence of oil spills; 2) the associated need for improved cleanup procedures; and, 3) the need for more effective surveillance systems, capable of gathering legal evidence for the prosecution of violators. The Geography Remote Sensing Unit and the Department of Chemical and Nuclear Engineering at the University of California, Santa Barbara for 2 1/2 years has been conducting experiments related to the application of remotely sensed data to these problem areas. As part of a United States Coast Guard test of a high seas oil containment device, a system for estimating the volume of oil loss resulting from oil pollution incidents was developed. This system involved the coordination of remote sensing data acquisition with simultaneous collection of surface sampling data. Results indicate that remotely sensed data, when effectively correlated with surface sampling data, can provide a base for volumetric estimations of a given oil slick. Refinements of these techniques can lead to more efficient, real-time day/night, operational monitoring of marine oil pollution incidents.

scholarly journals Multidecadal ENSO Amplitude Variability in a 1000-yr Simulation of a Coupled Global Climate Model: Implications for Observed ENSO Variability

2013 ◽  
Vol 26 (23) ◽  
pp. 9399-9407 ◽  
Author(s):  
Simon Borlace ◽  
Wenju Cai ◽  
Agus Santoso
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Climate Model ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Warming Trend ◽  
Recent Change ◽  
Climate Change Signal ◽  
Coupled Global Climate Model

The amplitude of the El Niño–Southern Oscillation (ENSO) can vary naturally over multidecadal time scales and can be influenced by climate change. However, determining the mechanism for this variation is difficult because of the paucity of observations over such long time scales. Using a 1000-yr integration of a coupled global climate model and a linear stability analysis, it is demonstrated that multidecadal modulation of ENSO amplitude can be driven by variations in the governing dynamics. In this model, the modulation is controlled by the underlying thermocline feedback mechanism, which in turn is governed by the response of the oceanic thermocline slope across the equatorial Pacific to changes in the overlying basinwide zonal winds. Furthermore, the episodic strengthening and weakening of this coupled interaction is shown to be linked to the slowly varying background climate. In comparison with the model statistics, the recent change of ENSO amplitude in observations appears to be still within the range of natural variability. This is despite the apparent warming trend in the mean climate. Hence, this study suggests that it may be difficult to infer a climate change signal from changes in ENSO amplitude alone, particularly given the presently limited observational data.

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