Planning for compound hazards during the COVID-19 pandemic: The role of climate information systems

Roundtable on Compound Hazards and COVID-19 What: An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. When: 30 June 2021 Where: Online, convened by the World Meteorological Organization and hosted by the American Geophysical UnionPeer Reviewed"Article signat per 12 autors/es: Benjamin F. Zaitchik, Judy Omumbo, Rachel Lowe, Maarten van Aalst, Liana O. Anderson, Erich Fischer, Charlotte Norman, Joanne Robbins, Rosa Barciela, Juli Trtanj, Rosa von Borries, and Jürg Luterbacher"Postprint (published version

Sensor modis : características gerais e aplicações

O presente trabalho tem como propósito realizar uma abordagem geral sobre um dos principais sensores, denominado MODIS (Moderate Resolution ImagingSpectroradiometer), desenvolvido pela NASA, com o objetivo de determinar como aTerra está mudando e quais as conseqüências para a vida neste planeta, desenvolvendo um entendimento de seu funcionamento como um sistema único e interligado. Seusprodutos permitirão um monitoramento de longa duração da superfície, necessários para o entendimento de mudanças globais. Neste propósito, realiza-se uma descrição sobre este sensor e por fim, uma síntese dos principais produtos gerados por ele e suas aplicações. _________________________________________________________________________________ ABSTRACTThe present work has as purpose to accomplish a general descriptionon one of main sensor, denominated MODIS (Moderate Resolution ImagingSpectroradiometer), developed by NASA, with the objective of determining how theEarth is changing and which the consequences for the life in this planet, developing an understanding of its functioning as an only and interlinked system. Its products willallow a long term monitoring of long duration of the surface, necessary for theunderstanding of global changes. In this purpose, it is presented a description of its systems and finally, a synthesis of the main products generated by this sensor and its applications

Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90 and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the total area of deforestation and identify small clearings

Key Elements in Service Innovation:Insights for the Hospitality Industry

For the hospitality industry, innovation is the oxygen that keeps concepts fresh and which attracts new customers, as well as encourages repeat customers. Although innovation requires creative thinking, coming up with a new idea is only the first step in ensuring successful service innovations. To detail the elements of service innovation and to determine ways to support successful innovations, the Cornell Center for Hospitality Research invited service industry leaders and Cornell faculty members to examine the issues surrounding service innovation. The resulting Service Innovation Roundtable brought in not only hospitality industry leaders, but also representatives from other service industries that have incorporated service innovations into their business models

Modeling abundance using N-mixture models: the importance of considering ecological mechanisms

Predicting abundance across a species' distribution is useful for studies of ecology and biodiversity management. Modeling of survey data in relation to environmental variables can be a powerful method for extrapolating abundances across a species' distribution and, consequently, calculating total abundances and ultimately trends. Research in this area has demonstrated that models of abundance are often unstable and produce spurious estimates, and until recently our ability to remove detection error limited the development of accurate models. The N-mixture model accounts for detection and abundance simultaneously and has been a significant advance in abundance modeling. Case studies that have tested these new models have demonstrated success for some species, but doubt remains over the appropriateness of standard N-mixture models for many species. Here we develop the N-mixture model to accommodate zero-inflated data, a common occurrence in ecology, by employing zero-inflated count models. To our knowledge, this is the first application of this method to modeling count data. We use four variants of the N-mixture model (Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial) to model abundance, occupancy (zero-inflated models only) and detection probability of six birds in South Australia. We assess models by their statistical fit and the ecological realism of the parameter estimates. Specifically, we assess the statistical fit with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the “best model” according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates. The zero-inflated Poisson variant is preferable to the negative binomial variants of the N-mixture, as it models an ecological mechanism rather than a statistical phenomenon and generates reasonable parameter estimates. Our results emphasize the need to include ecological reasoning when choosing appropriate models and highlight the dangers of modeling statistical properties of the data. We demonstrate that, to obtain ecologically realistic estimates of abundance, occupancy and detection probability, it is essential to understand the sources of variation in the data and then use this information to choose appropriate error distributions. Copyright ESA. All rights reserved

Mapping and characterizing social-ecological land systems of South America

Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world

MAPEAMENTO DA COBERTURA DA TERRA DO ESTADO DO MATO GROSSO ATRAVÉS DA UTILIZAÇÃO DE DADOS MULTITEMPORAIS DO SENSOR MODIS

On the last decades, the remote sensing became an important source of information to monitor the natural resources of the planet, due to its possibility to acquire data over large regions. The images derived from remote sensing instruments are an excellent source of data to produce land cover and vegetation maps. Recent estimates of changes occurring in the land cover point to the agricultural intensification, deforestation in the tropic, pasturelands expansion, and urbanization as the currently main forces. So, it is unquestionable the importance of developing an accurate map of the different vegetation formations, as base for conservation studies, and studies that involve global change, such as climate change and carbon and hydrological balance. The main objective of this paper is to present a methodological approach to land cover mapping using MODIS multitemporal sensor data. The map generated in this research presents the classification of different vegetation classes, anthropic areas and soybean cultivation areas, over the Mato Grosso State. Keywords: Land cover mapping; Remote Sensing; MODIS sensor; Mato Grosso State.Nas últimas décadas, o sensoriamento remoto tornou-se uma importante fonte de informações para monitorar os recursos naturais da Terra, devido à possibilidade de se adquirir dados sobre grandes extensões geográficas. As imagens derivadas de produtos do sensoriamento remoto são uma excelente fonte de dados para produzir mapas de cobertura vegetal e uso da terra. Estimativas recentes das mudanças que vem ocorrendo na cobertura da terra apontam para a intensificação da agricultura, desmatamentos nos trópicos, expansão das áreas de pastagens e urbanização como as principais forçantes atuais. Desta forma, é inquestionável a importância de se realizar um mapeamento acurado das diferentes formações vegetais, tanto como base para estudos de conservação, quanto para estudos que envolvam questões relacionadas às mudanças globais, como alterações no clima, no ciclo do carbono e no balanço hídrico. O principal objetivo do presente trabalho é apresentar uma proposta metodológica para o mapeamento da cobertura da terra a partir da utilização de dados multitemporais do sensor MODIS. No mapa de cobertura da terra gerado nesta pesquisa, apresenta-se alem da classificação de diferentes classes vegetais, as áreas antropizadas e as áreas onde existe o cultivo de soja, para o Estado do Mato Grosso. Palavras-Chave: Mapeamento da cobertura da terra; Sensoriamento Remoto; Sensor MODIS; Estado do Mato Grosso

Detection of forest degradation caused by fires in Amazonia from time series of MODIS fraction images

A new method is presented to detect and assess the extent of burned forests in a tropical ecosystem. Our study area is located in Mato Grosso state southern flank of the Brazilian Amazon region. MODIS images are used over the dry season of year 2010. The proposed method is based on (i) linear spectral mixing model applied to MODIS imagery to derive soil and shade fraction images and (ii) image segmentation and classification applied to a multi-temporal dataset of MODIS-derived images. In a first step, deforested areas are identified and mapped from the soil fraction images while burned areas are identified and mapped from the shade fraction images. Then, burned forest areas are mapped by combining a forest/non forest mask with the resulting burned area map. Our results show that 14,220 km2 of forests were degraded by fire in Mato Grosso during year 2010. Our approach can be potentially used operationally for detecting forest degradation due to fires. The proposed method can also be applied to time series of medium and high spatial resolution images for regional and local analysis.JRC.H.3-Forest Resources and Climat

SENSOR MODIS: CARACTERÍSTICAS GERAIS E APLICAÇÕES

The present work has as purpose to accomplish a general description on one of main sensor, denominated MODIS (Moderate Resolution Imaging Spectroradiometer), developed by NASA, with the objective of determining how the Earth is changing and which the consequences for the life in this planet, developing an understanding of its functioning as an only and interlinked system. Its products will allow a long term monitoring of long duration of the surface, necessary for the understanding of global changes. In this purpose, it is presented a description of its systems and finally, a synthesis of the main products generated by this sensor and its applications.O presente trabalho tem como propósito realizar uma abordagem geral sobre um dos principais sensores, denominado MODIS (Moderate Resolution Imaging Spectroradiometer), desenvolvido pela NASA, com o objetivo de determinar como a Terra está mudando e quais as conseqüências para a vida neste planeta, desenvolvendo um entendimento de seu funcionamento como um sistema único e interligado. Seus produtos permitirão um monitoramento de longa duração da superfície, necessários para o entendimento de mudanças globais. Neste propósito, realiza-se uma descrição sobre este sensor e por fim, uma síntese dos principais produtos gerados por ele e suas aplicações