23 research outputs found
A Novel Data Fusion Technique for Snow Cover Retrieval
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where two information sources disagree. The presented methodological innovation consists in the integration of remote-sensing data products and the numerical simulation results by means of a machine learning classifier (support vector machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the moderate-resolution imaging spectroradiometer (MODIS) data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well-known underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season
Application of the winter and early-spring satellite images for assessment of the birch forest coverage on the abandoned agricultural lands
На примере зарастающих сельскохозяйственных угодий, расположенных в зоне широколиственных лесов Республики Башкортостан, рассмотрена возможность оценки проективного покрытия формирующихся на залежах березняков по значениям спектральной яркости каналов зимних и ранневесенних космоснимков, а также по значениям нормализованного разностного индекса лесного снега NDFSI. В качестве исходных данных использованы проективное покрытие древесного яруса на 189 модельных участках березняков, описанных в июле 2021 г., и космоснимки Sentinel-2, Landsat 7 и Landsat 8. Наилучшие результаты получены при использовании красного канала ранневесенних снимков в период сохранения снежного покрова (с середины марта до первой половины апреля). Корреляция между проективным покрытием и спектральной яркостью красного канала составила –0,90. Модель позволяет достаточно точно определять проективное покрытие березняков возрастом от 18 до 20 лет, которые преобладают на залежах в зоне распространения широколиственных лесов в Республике Башкортостан. Установлена возможность использования полученных моделей для оценки проективного покрытия березняков на более ранних стадиях зарастания сельскохозяйственных угодий
Earth Observations for Addressing Global Challenges
"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph
Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques
Satellite remote sensing observations of snow cover extent during the melt-out season in the Thompson-Okanagan Region, British Columbia from 2003-2019
Snow is a critical component of the earth’s overall energy budget and it contributes significantly to water resources especially in mountainous regions, coining the term the “water towers” for downstream communities (Viviroli et al., 2006). Studies have shown an increase in snow cover variability due in part by climate change. Most evident throughout the research is an earlier freshet period throughout the northern hemisphere, elevation-dependent warming in mountainous regions and regional climate models indicating transitions from snow to rain dominated basins (Pepin et al., 2015; Rangwala & Miller, 2012). Studies throughout British Columbia have shown evidence of earlier peak runoff from river gauges, a decrease in snow duration and increases in temperature by 1.4ᵒ (Shrestha et al., 2012; Kang et al., 2014; Islam et al., 2017). The Thompson Okanagan region is a semi-arid snow dominated region located in the southern portion of British Columbia (Kang et al., 2014). The spring freshet in Thompson Okanagan is affected by large atmospheric systems as well, including the Pacific North American Pattern (PNA), the Pacific Decadal Oscillation (PDO) and the Oceanic Nino Index (ONI).
This research focuses on identifying variations in snow cover during the spring freshet (April 1st-June 30th) in Thompson Okanagan with remote sensing observations from 2003-2019. Snow cover mapping is achieved using visible-infrared observations of snow. High albedo is easily distinguishable in the visible spectrum; however, cloud contamination impedes analysis using visible infrared observations. Steps to mitigate the impact of cloud cover adopted a multi-step methodology. This improved the ability to characterize snow cover extent variability during the spring freshet. The methodology includes: i) a daily combination of Terra/Aqua (from 2003-2012) and VIIRS (from 2012-2019) observations; ii) an adjacent temporal deduction (ATD) technique which replaces cloud pixels with non-cloudy pixels from +/-2 adjacent days; iii) a spatial filter to interpolate snow in cloudy pixels; iv) and the identification of a regional snowline elevation above which cloud-labelled pixels are classified as snow, and cloud pixels below the elevation for no-snow are classified as no-snow. This methodology significantly reduced cloud cover from an average of 71.5% to 1.6% annually.
Using stratified random sampling approach, reference points were gathered for a range of elevation bands for four watersheds within the region to test the snow mapping accuracy. The last day of snow (LDS) was extracted for each point from 2003-2019. Large scale atmospheric patterns (Pacific Decadal Oscillation (PDO), Pacific-North American (PNA) teleconnection pattern and Oceanic Nino Index (ONI)) were analyzed using simple and multiple linear regression to assess the variability within the LDS dataset that could be explained by these patterns. This analysis showed that the PNA did not significantly account the variability, but the PDO did with an R2 value reaching 64%, with a significance level of >95%. The simple linear regression models showed that the ONI explained 78% of the LDS variation during the March-April-May (MAM) months, with p>95%; this was more than any other 3-month interval studied. Also, the ONI R2 value decreased as elevation increased. Overall, El Nino years showed snow disappearance of ~23 days earlier than La Nina years at low elevation, ~18 days sooner at mid elevation and ~13 days sooner at high elevations. Earlier snow melt-out during El Nino phases have implications for water resources in the region, for residential and crop use as well as economic impacts for tourism (Westering, 2016; Winkler et al., 2017). This also contributes to area burned in forest fires and rapid melting snow can cause flooding in surrounding urban areas within Thompson Okanagan. Extending the study period into the future could allow further insights on potential effects of climate change within the region
Remote Sensing of Natural Hazards
Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches
LA TELEDETECCI 3N COMO INSTRUMENTO PARA EL AN\uc1LISIS GLACIAR EN AMBIENTES DE ALTA MONTA 1A TROPICAL ECUATORIANA
El calentamiento global es un fen\uf3meno clim\ue1tico con afectaciones de distinta naturaleza a escala mundial. Uno de sus efectos es el aumento de la temperatura atmosf\ue9rica, el cual incide de manera directa en el derretimiento de las masas de hielo que conforman los glaciares, especialmente en la alta monta\uf1a tropical. En ese contexto, el presente estudio tiene como objetivo identificar y analizar los cambios temporales que han presentado tres glaciares ubicados en los Andes Ecuatorianos. Para desarrollar el estudio se utiliz\uf3 un software de Sistemas de Informaci\uf3n Geogr\ue1fica y un conjunto de im\ue1genes satelitales de diferentes fechas que permitieron obtener datos a trav\ue9s de la aplicaci\uf3n del \ucdndice de Nieve de Diferencia Normalizada (NDSI). Como resultado se encontr\uf3 que los glaciares presentaron un importante retroceso y disminuci\uf3n en t\ue9rminos de superficie, contray\ue9ndose de manera significativa hacia sectores de mayor elevaci\uf3n.
Palabras clave: Calentamiento global, Cambio clim\ue1tico, Glaciares.
ABSTRACT
Global warming is a climatic phenomenon with affects of different nature on a global scale. One of its effects is the increase in the atmospheric temperature, which directly affects the melting of the ice masses that make up the glaciers, especially in the high tropical mountain. In this context, the present study aims to identify and analyze the temporal changes that have presented three glaciers located in the Ecuadorian Andes. To develop the study, Geographic Information Systems software and a set of satellite images of different dates were used to obtain data through the application of the Normalized Difference Snow Index (NDSI). As a result it was found that the glaciers presented a significant decline and decrease in surface area, contracting significantly towards sectors of higher elevation.
Keywords: Global warming, Climate change, Glaciers. <br
Remote sensing technology applications in forestry and REDD+
Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion
Assimilation of snow information into a cold regions hydrological model
Spring and summer snowmelt runoff from the Canadian Rocky Mountains recharge many rivers and hence provide critical water supplies for a large portion of the population in western Canada. Because of the complex topography and vegetation conditions, the sparse network of observations of climate and snow properties, and the low quality of atmospheric model products, data assimilation (DA) is a potentially useful tool to improve the forecasting and prediction of snow properties and streamflow. To achieve better snowpack and streamflow estimations using DA, this research aims to: 1) evaluate the usefulness of SNODAS SWE data in Canada, and determine the influence of processes missing from the SNODAS model on the accuracy of SNODAS SWE, 2) explore the possibility of using remotely sensed data for detecting snow interception in forest canopies, 3) assimilate in situ measured and remotely sensed snow interception data into CRHM and assess their influence on the simulation of snow interception losses, 4) determine the optimal method to assimilate in situ snow measurements into the CRHM for prediction of basin snowpacks and streamflow.
The results illustrate: 1) missing snow processes (blowing snow transport and canopy snow interception and sublimation) in the SNODAS snow model contribute substantially to its overestimation of SWE, 2) canopy intercepted snow can be detected by optical remote sensing data (NDSI and NDVI), 3) automated snow depth data measured from an adjacent forest and clearing can be used in a mass budget to accurately quantify snow interception loss, and assimilation of in situ measured and remotely sensed snow interception information can all improve simulations of snow interception timing and magnitude, 4) assimilating in situ SWE and snow depth into CRHM generally improves the simulation of snowpack properties and streamflow, but the results varied among different assimilation schemes. A better SWE simulation through DA does not always lead to better prediction of streamflow. The advanced snow interception measurement and DA techniques presented here deepens the understanding of cold regions hydrological DA and improve the capacity to forecast and predict the hydrology of headwater river basins in the Canadian Rockies and other similar regions
La Teledetección como instrumento para el análisis glaciar en ambientes de alta montaña tropical ecuatoriana
Global warming is a climatic phenomenon with affects of different nature on a global scale. One of its effects is the increase in the atmospheric temperature, which directly affects the melting of the ice masses that make up the glaciers, especially in the high tropical mountain. In this context, the present study aims to identify and analyze the temporal changes that have presented three glaciers located in the Ecuadorian Andes. To develop the study, Geographic Information Systems software and a set of satellite images of different dates were used to obtain data through the application of the Normalized Difference Snow Index (NDSI). As a result it was found that the glaciers presented a significant decline and decrease in surface area, contracting significantly towards sectors of higher elevation.El calentamiento global es un fenómeno climático con afectaciones de distinta naturaleza a escala mundial. Uno de sus efectos es el aumento de la temperatura atmosférica, el cual incide de manera directa en el derretimiento de las masas de hielo que conforman los glaciares, especialmente en la alta montaña tropical. En ese contexto, el presente estudio tiene como objetivo identificar y analizar los cambios temporales que han presentado tres glaciares ubicados en los Andes Ecuatorianos. Para desarrollar el estudio se utilizó un software de Sistemas de Información Geográfica y un conjunto de imágenes satelitales de diferentes fechas que permitieron obtener datos a través de la aplicación del Índice de Nieve de Diferencia Normalizada (NDSI). Como resultado se encontró que los glaciares presentaron un importante retroceso y disminución en términos de superficie, contrayéndose de manera significativa hacia sectores de mayor elevación