Relationship between vegetation indices and forest detection based on Landsat 5 images

Podaci prikupljeni na daljinu široko se koriste u svrhu detekcije promjena na površini zemlje, nestanak ili nastanak vodenih površina, šuma, poljoprivrednih površina itd. Ova studija se odnosi na uspostavljanje veze između vegetacijskih indeksa i detekcije šuma na osnovi satelitskih multispektralnih snimki srednje razlučivosti. Odnosno, kako kombinacije pojedinih indeksa utječu na izdvajanje pošumljenih područja i da li je to uopće moguće. Nakon utvrđivanja veze pristupilo se određivanju nestalih i nastalih šumskih područja. Istraživanje je provedeno koristeći Landsatove snimke prostorne razlučivosti 30 metara i softver Erdas Imagine. Kao ulazni podaci korišteni su pojedini kanali snimki (plavi, crveni, zeleni, bliskoinfracrveni i srednje infracrveni kanal). Aritmetičkim kombiniranjem tih kanala u Model Makeru dobiveni su rasteri (slike) s odgovarajućim vrijednostima piksela na osnovu kojih je izvršena klasifikacija šuma. Za svaki vegetacijski indeks postoje granice u koje spadaju vegetacijska područja. Razlog korištenja više vegetacijskih indeksa leži u činjenici da svaki ima određene prednosti i nedostatke. Neki od njih umanjuju utjecaj pozadine (tla, zemlje) korištenjem dodatnih konstanti, dok neki imaju manji utjecaj atmosfere na konačni raster. Uglavnom, svi koriste ključne infracrvene kanale zbog visokog odziva zdrave vegetacije u tom dijelu spektra. Na osnovi kontrolnih točaka, koje predstavljaju stvarna šumska područja, određene su granice vrijednosti piksela za klasificiranje šuma. Na kraju je izvršena vektorizacija podataka (konverzija iz rastera u vektor) kako bi se dobile površine područja pod šumama. Sve je ovo odrađeno za dva ljetna razdoblja, 1986. i 2011. godine.Remotely sensed data are widely used in order to determine changes on the Earth’s surface, disappearance or appearance of water areas, forests, agricultural areas, etc. This study aims to establish a relation between vegetation indices and forest detection based on multispectral satellite images with medium spatial resolution, i.e. how the combination of individual indices affects the extraction of forested areas and is that even possible. After establishing relations, it was possible to determine the deforested and forested areas. The research was conducted using Landsat images with 30 meters spatial resolution and using the Erdas Imagine software. Individual image bands (blue, red, green, near-infrared and mid-infrared) were used as input data. Rasters with certain pixel values, with which forest classification was conducted, were generated using arithmetical combinations of image bands in the Model Maker. For each vegetation index, there are limit within which vegetation areas belong. The reason for using multiple vegetation indices are advantages and disadvantages of individual indices. Some of them reduce background effects (bare soil) by using additional constants, while some have less atmospheric impact on final results. Mainly, all of them use infrared bands because of high vegetation reflection of that spectrum. Limitation for forest areas was established based on Ground Control Points (GCP) which represent true forest areas, and the classification was performed using this limits. Lastly, vectorization (raster to vector conversion) of data was conducted in order to obtain areas. All of this was applied during two summer periods, in 1986 and 2011

GHSL-S2 plugin User Guide

The GHSL-S2 Tool (version 1.0) is a visualization and download tool (QGIS plugin) developed in the frame of the Global Human Settlement Layer (GHSL) project . It facilitates the access to the GHSL S2 products using a free and open-source cross-platform Geographic Information System software (QGIS v.3.8 or higher). It provides a handy way to explore the GHSL datasets, to classify the probabilistic built-up layer derived from Sentinel-2 image composite and to export user-defined subsets, while avoiding the download of large files. The GHS-S2 tool is developed in Python programming language as a QGIS plugin. It bridges the QGIS software to the Google Earth Engine (GEE) cloud-based platform , making use of the GEE plugin , which integrates GEE and QGIS using the EE Python API . It requires an active GEE account and an internet connection. This document contains the description of the GHS-S2 tool usage, the main features and functionalities. The GHSL-S2 plugin is part of the GHSL tools suite and issued with an end-user licence agreement, included in the download package.JRC.E.1-Disaster Risk Managemen

The European Settlement Map 2019 release

The ESM_2015 is the latest release of the European Settlement Map produced in the frame of the GHSL project. It is produced with the Global Human Settlement Layer (GHSL) technology of the Joint Research Centre (JRC) in collaboration with the Directorate General of Regional and Urban Policy. The workflow was executed on the JRC Big Data Analytics platform. It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012. The ESM_2015 product exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016. Unlike the previous ESM versions, the built-up extraction is realized through supervised learning (and not only by means of image filtering and processing techniques) based on textural and morphological features. The workflow is fully automated and it does not include any post-processing. For the first time a new layer containing non-residential buildings was derived by using only remote sensing imagery and training data. The produced built-up map is delivered at 2 m pixel resolution (level 1 layer) while the residential/non-residential layer (level 2) is delivered at 10 m spatial resolution. ESM_2015 offers new opportunities in Earth observation related research by allowing to study urbanisation and related features across Europe in urban and rural areas, from continental to country perspective, from regional to local, until single blocks. ESM_2015 was validated against the LUCAS 2015 survey database both at 2 and 10 meters resolution (including also the non-residential class). The validation has resulted in a Balanced Accuracy of 0.81 for the 2 m resolution built-up layer and of 0.71 for the 10 m non-residential built-up layer.JRC.E.1-Disaster Risk Managemen

Towards a Map of the European Tree Cover based on Sentinel-2

Many areas of science and policy depend on knowledge of the tree cover in Europe. Sentinel-2 is a new (launched in 2015) satellite with a higher spatial resolution compared to previous satellites. In the present study a new algorithm for mapping tree cover from Sentinel-2 is developed, an analysis of which bands should be used for tree cover mapping is made, the accuracy of the mapping is assessed, and the tree cover from the present approach is compared with previous estimates. Firstly, the feasibility of the present algorithm is demonstrated. Secondly, it is shown that only ten band combinations have good performance in four selected Sentinel-2 tiles and that the bands 3, 5, 6, 12 appear in most combinations. Thirdly, the accuracy is assessed to be high, and lastly it is shown that the relative difference between the tree cover of the present study and the tree cover of previous studies is between -14% and 68

The IUCN Red List of Ecosystems: motivations, challenges, and applications

Abstract In response to growing demand for ecosystem-level risk assessment in biodiversity conservation, and rapid proliferation of locally tailored protocols, the IUCN recently endorsed new Red List criteria as a global standard for ecosystem risk assessment. Four qualities were sought in the design of the IUCN criteria: generality; precision; realism; and simplicity. Drawing from extensive global consultation, we explore trade-offs among these qualities when dealing with key challenges, including ecosystem classification, measuring ecosystem dynamics, degradation and collapse, and setting decision thresholds to delimit ordinal categories of threat. Experience from countries with national lists of threatened ecosystems demonstrates well-balanced trade-offs in current and potential applications of Red Lists of Ecosystems in legislation, policy, environmental management and education. The IUCN Red List of Ecosystems should be judged by whether it achieves conservation ends and improves natural resource management, whether its limitations are outweighed by its benefits, and whether it performs better than alternative methods. Future development of the Red List of Ecosystems will benefit from the history of the Red List of Threatened Species which was trialed and adjusted iteratively over 50 years from rudimentary beginnings. We anticipate the Red List of Ecosystems will promote policy focus on conservation outcomes in situ across whole landscapes and seascapes

GHSL Data Package 2019

The Global Human Settlement Layer (GHSL) produces new global spatial information, evidence-based analytics and knowledge describing the human presence on the planet Earth. The GHSL operates in a fully open and free data and methods access policy, building the knowledge supporting the definition, the public discussion and the implementation of European policies and the international frameworks as the 2030 Development Agenda and the related thematic agreements. The GHSL supports the GEO Human Planet Initiative (HPI) that is committed to developing a new generation of measurements and information products providing new scientific evidence and a comprehensive understanding of the human presence on the planet and that can support global policy processes with agreed, actionable and goal-driven metrics. The Human Planet Initiative relies on a core set of partners committed in coordinating the production of the global settlement spatial baseline data. One of the core partners is the European Commission, Directorate General Joint Research Centre, Global Human Settlement Layer project. The Global Human Settlement Layer project produces global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet. This document describes the public release of the GHSL Data Package 2019 (GHS P2019).JRC.E.1-Disaster Risk Managemen

Description of the GHS Urban Centre Database 2015

The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset - GHS STAT UCDB2015MT GLOBE R2019A V1.0. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time. Urban Centres are defined in a consistent way across geographical locations and over time, applying the “Global Definition of Cities and Settlements” developed by the European Union to the Global Human Settlement Layer Built-up (GHS-BUILT) areas and Population (GHS-POP) grids. This report contains the description of the dimensions and the derived attributes that characterise the Urban Centres in the database. The document includes notes about methodology and sources. The GHS-UCDB contains information for more than 10,000 Urban Centres and it is the baseline data of the analytical results presented in the Atlas of the Human Planet 2018.JRC.E.1-Disaster Risk Managemen

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens