Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 872
Filtrar
1.
J Environ Manage ; 281: 111888, 2021 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-33388712

RESUMO

Most studies about particulate matter (PM) estimation have been done based on satellite-derived optical depth aerosol (AOD) products. But, the use of AOD products having coarse resolution is not possible for PM map generation in small spatial coverage such as local cities. To solve this issue, a PM estimation framework is proposed in this work which accepts the original calibrated radiance of MODIS-Level 1 images as input. There are no intermediate computations for atmospheric reflectance or aerosol thickness calculation. A deep neural network consisting of recurrent layers is proposed to extract the relationship between the grey level values of the satellite image bands and the PM measurements in different days and locations. Two individual networks are trained for PM2.5 and PM10 concentrations. The PM2.5 map and PM10 map of Tehran city are generated. The performance of the proposed method is compared with several recently published air pollution studies. The results show that the proposed method is a simple, low cost and efficient approach for PM generation of small-scaled coverage using free available Moderate Resolution Imaging Spectroradiometer (MODIS) images.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Aerossóis/análise , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Cidades , Monitoramento Ambiental , Características da Família , Irã (Geográfico) , Redes Neurais de Computação , Material Particulado/análise , Imagens de Satélites
2.
Ying Yong Sheng Tai Xue Bao ; 32(1): 252-260, 2021 Jan.
Artigo em Chinês | MEDLINE | ID: mdl-33477233

RESUMO

It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.


Assuntos
Tecnologia de Sensoriamento Remoto , Água , Imagens de Satélites , Estações do Ano , Solo
3.
Nat Commun ; 12(1): 684, 2021 01 29.
Artigo em Inglês | MEDLINE | ID: mdl-33514721

RESUMO

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15 min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.


Assuntos
Monitorização de Parâmetros Ecológicos/métodos , Folhas de Planta/fisiologia , Floresta Úmida , Imagens de Satélites , Brasil , Cor , Fotossíntese , Estações do Ano , Análise Espaço-Temporal
4.
Artigo em Inglês | MEDLINE | ID: mdl-33317103

RESUMO

BACKGROUND: Recently, the importance of light physical activity (LPA) for health has been emphasized, and residential greenness has been positively linked to the level of LPA and a variety of positive health outcomes. However, people spend less time in green environments because of urbanization and modern sedentary leisure activities. AIMS: In this population-based study, we investigated the association between objectively measured residential greenness and accelerometry measured physical activity (PA), with a special interest in LPA and gender differences. METHODS: The study was based on the Northern Finland Birth Cohort 1966 (5433 members). Participants filled in a postal questionnaire and underwent clinical examinations and wore a continuous measurement of PA with wrist-worn Polar Active Activity Monitor accelerometers for two weeks. The volume of PA (metabolic equivalent of task or MET) was used to describe the participant's total daily activity (light: 2-3.49 MET; moderate: 3.5-4.99 MET; vigorous: 5-7.99 MET; very vigorous: ≥8 MET). A geographic information system (GIS) was used to assess the features of each individual's residential environment. The normalized difference vegetation index (NDVI) was used for the objective quantification of residential greenness. Multiple linear regression and a generalized additive model (GAM) were used to analyze the association between residential greenness and the amount of PA at different intensity levels. RESULTS: Residential greenness (NDVI) was independently associated with LPA (unadjusted ß = 174; CI = 140, 209) and moderate physical activity (MPA) (unadjusted ß = 75; CI = 48, 101). In the adjusted model, residential greenness was positively and significantly associated with LPA (adjusted ß = 70; CI = 26, 114). In men, residential greenness was positively and significantly associated with LPA (unadjusted ß = 224; CI = 173, 275), MPA (unadjusted ß = 75; CI = 48, 101), and moderate to vigorous physical activity (MVPA) (unadjusted ß = 89; CI = 25, 152). In women, residential greenness was positively related to LPA (unadjusted ß = 142; CI = 96, 188) and inversely associated with MPA (unadjusted ß = -22; CI = -36, -8), vigorous/very vigorous physical activity (VPA/VVPA) (unadjusted ß = -49; CI = -84, -14), and MVPA (unadjusted ß = -71; CI = -113, -29). In the final adjusted models, residential greenness was significantly associated only with the amount of LPA in men (adjusted ß = 140; CI = 75, 204). CONCLUSIONS: Residential greenness was positively associated with LPA in both genders, but the association remained significant after adjustments only in men. Residential greenness may provide a supportive environment for promoting LPA.


Assuntos
Acelerometria , Ambiente Construído , Exercício Físico , Imagens de Satélites , Ambiente Construído/normas , Ambiente Construído/estatística & dados numéricos , Feminino , Finlândia , Humanos , Atividades de Lazer , Masculino , Pessoa de Meia-Idade , Fatores Sexuais
5.
PLoS One ; 15(9): e0239746, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32986785

RESUMO

This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's Tobacco production. In order to analyze the performance of the developed classification framework, a pilot sub-region named Yar Hussain is selected for experimentation work. Yar Hussain is a tehsil of district Swabi, within KP province of Pakistan, having highest contribution to the gross production of the KP Tobacco crop. KP generally consists of a diverse crop land with different varieties of vegetation, having similar phenology which makes crop classification a challenging task. In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus on the Tobacco crop. In order to improve the performance of the proposed classification framework, instead of using the prevailing concept of utilizing a single satellite imagery, both Sentinel-2 and Planet-Scope imageries are stacked together to assist in providing more diverse features to the proposed classification framework. Furthermore, instead of using a single date satellite imagery, multiple satellite imageries with respect to the phenological cycle of Tobacco crop are temporally stacked together which resulted in a higher temporal resolution of the employed satellite imagery. The developed framework is trained using the ground truth data. The final output is obtained as an outcome of the SoftMax function of the developed model in the form of probabilistic values, for the classification of the selected classes. The proposed deep learning-based crop classification framework, while utilizing multi-satellite temporally stacked imagery resulted in an overall classification accuracy of 98.15%. Furthermore, as the developed classification framework evolved with specific focus on Tobacco crop, it resulted in best Tobacco crop classification accuracy of 99%.


Assuntos
Agricultura/métodos , Aprendizado Profundo , Imagens de Satélites/métodos , Tabaco/classificação , Verduras/classificação , Confiabilidade dos Dados , Humanos , Paquistão , Triticum/classificação
6.
Water Res ; 186: 116353, 2020 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-32919140

RESUMO

Submerged macrophyte monitoring is a major concern for hydrosystem management, particularly for understanding and preventing the potential impacts of global change on ecological functions and services. Macrophyte distribution assessments in rivers are still primarily realized using field monitoring or manual photo-interpretation of aerial images. Considering the lack of applications in fluvial environments, developing operational, low-cost and less time-consuming tools able to automatically map and monitor submerged macrophyte distribution is therefore crucial to support effective management programs. In this study, the suitability of very fine-scale resolution (50 cm) multispectral Pléiades satellite imagery to estimate submerged macrophyte cover, at the scale of a 1 km river section, was investigated. The performance of nonparametric regression methods (based on two reliable and well-known machine learning algorithms for remote sensing applications, Random Forest and Support Vector Regression) were compared for several spectral datasets, testing the relevance of 4 spectral bands (red, green, blue and near-infrared) and two vegetation indices (the Normalized Difference Vegetation Index, NDVI, and the Green-Red Vegetation Index, GRVI), and for several field sampling configurations. Both machine learning algorithms applied to a Pléiades image were able to reasonably well predict macrophyte cover in river ecosystems with promising performance metrics (R² above 0.7 and RMSE around 20%). The Random Forest algorithm combined to the 4 spectral bands from Pléiades image was the most efficient, particularly for extreme cover values (0% and 100%). Our study also demonstrated that a larger number of fine-scale field sampling entities clearly involved better cover predictions than a smaller number of larger sampling entities.


Assuntos
Ecossistema , Rios , Monitoramento Ambiental , Tecnologia de Sensoriamento Remoto , Imagens de Satélites
7.
Artigo em Inglês | MEDLINE | ID: mdl-32882867

RESUMO

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.


Assuntos
Ambiente Construído , Infecções por Coronavirus , Pandemias , Pneumonia Viral , Imagens de Satélites , Betacoronavirus , Planejamento Ambiental , Humanos , Características de Residência
8.
Nature ; 585(7824): 225-233, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32908268

RESUMO

Isoprene is the dominant non-methane organic compound emitted to the atmosphere1-3. It drives ozone and aerosol production, modulates atmospheric oxidation and interacts with the global nitrogen cycle4-8. Isoprene emissions are highly uncertain1,9, as is the nonlinear chemistry coupling isoprene and the hydroxyl radical, OH-its primary sink10-13. Here we present global isoprene measurements taken from space using the Cross-track Infrared Sounder. Together with observations of formaldehyde, an isoprene oxidation product, these measurements provide constraints on isoprene emissions and atmospheric oxidation. We find that the isoprene-formaldehyde relationships measured from space are broadly consistent with the current understanding of isoprene-OH chemistry, with no indication of missing OH recycling at low nitrogen oxide concentrations. We analyse these datasets over four global isoprene hotspots in relation to model predictions, and present a quantification of isoprene emissions based directly on satellite measurements of isoprene itself. A major discrepancy emerges over Amazonia, where current underestimates of natural nitrogen oxide emissions bias modelled OH and hence isoprene. Over southern Africa, we find that a prominent isoprene hotspot is missing from bottom-up predictions. A multi-year analysis sheds light on interannual isoprene variability, and suggests the influence of the El Niño/Southern Oscillation.


Assuntos
Atmosfera/química , Butadienos/análise , Butadienos/química , Mapeamento Geográfico , Hemiterpenos/análise , Hemiterpenos/química , Imagens de Satélites , África , Austrália , Brasil , Conjuntos de Dados como Assunto , El Niño Oscilação Sul , Formaldeído/química , Radical Hidroxila/análise , Radical Hidroxila/química , Ciclo do Nitrogênio , Óxidos de Nitrogênio/análise , Óxidos de Nitrogênio/química , Oxirredução , Estações do Ano , Sudeste dos Estados Unidos
9.
PLoS One ; 15(8): e0237878, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32833966

RESUMO

Land subsidence monitoring provides information required when developing land use plans and allows for proactive management of subsidence issues. However, it has been challenging to accurately detect land subsidence areas, especially those under waterbodies. This study evaluated the applicability of integrated use of the optical Landsat-8 OLI and microwave Sentinel-1A TOPSAR imagery to delineate subsidence areas and quantify subsidence rates in a typical coal mining area of North China Plain. An Enhanced Modified Normalized Difference Water Index (E-MNDWI) was combined with Short BAseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) image to monitor underwater and dry ground subsidence. The results demonstrated that the method could delineate underwater and dry ground subsidence and quantify its rates accurately. The proposed method estimated subsidence area corresponded to 34.8% (16.7 km2) of the study area. The size of underwater subsidence areas was substantial and accounted for 43.7% of the subsidence areas. Seasonal underwater subsidence areas were generally distributed in the vicinity of perennial ones. Dry ground subsidence covered 9.4 km2 of the study area and generally occurred in urban and rural residential areas with the maximum subsidence of up to 80.1 mm/year. This study demonstrates the efficiency and capacity of integrating optical and microwave images to monitor the subsidence progresses, which thus can help develop effective rehabilitation policy and strategy to mitigate the impacts of land subsidence.


Assuntos
Monitoramento Ambiental , Água Subterrânea , China , Geografia , Micro-Ondas , Imagem Óptica , Tecnologia de Sensoriamento Remoto , Imagens de Satélites
10.
PLoS One ; 15(8): e0238165, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32841269

RESUMO

Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas.


Assuntos
Altitude , Ecossistema , Plantas/classificação , Imagens de Satélites , Algoritmos , China , Análise por Conglomerados , Bases de Dados Factuais , Monitoramento Ambiental/estatística & dados numéricos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Imagens de Satélites/estatística & dados numéricos
11.
PLoS One ; 15(8): e0235171, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32797112

RESUMO

Pavement crack analysis, which deals with crack detection and crack growth detection, is a crucial task for modern Pavement Management Systems (PMS). This paper proposed a novel approach that uses historical crack data as reference for automatic pavement crack analysis. At first, a multi-scale localization method, which including GPS based coarse localization, image-level localization, and metric localization has been presented to establish image correspondences between historical and query crack images. Then historical crack pixels can be mapped onto the query crack image, and these mapped crack pixels are seen as high-quality seed points for crack analysis. Finally, crack analysis is accomplished by applying Region Growing Method (RGM) to further detect newly grown cracks. The proposed method has been tested with the actual pavement images collected in different time. The F-measure for crack growth is 88.9%, which demonstrates the proposed method has an ability to greatly simplify and enhances crack analysis result.


Assuntos
Controle de Qualidade , Imagens de Satélites/métodos , Algoritmos , Materiais de Construção/normas , Ciência dos Materiais/normas , Imagens de Satélites/normas , Transportes/normas
13.
Sci Total Environ ; 746: 140327, 2020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-32768776

RESUMO

The collection of field-based animal data is laborious, risky and costly in some areas, such as various nature reserves. Although multiple studies have used satellite imagery, aerial imagery, and field data individually for some animal species surveys, several technical issues still need to be addressed before full standardization of remote sensing methods for modeling animal population dynamics over large areas. This study is the first to model the population dynamics of livestock in the Longbao Wetland National Nature Reserve, China by utilizing yak estimations from Worldview-2 satellite imagery (0.5 m) collected in 2010 and yaks counted in a ground-based survey conducted in 2011 in combination with the animal population structure precisely extracted from UAS imagery captured in 2016. As a consequence, 5501, 5357, and 5510 yaks were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. In total, 1092, 1062 and 1092 sheep were estimated to appear in the reserve in 2010, 2011 and 2016, respectively. The uncertainty of the presented method is also discussed. Primary experiments show that both the satellite imagery and UAS imagery are promising for use in yak censuses, but no sheep were observed in the satellite imagery because of the low resolution. Compared to the ground-based survey conducted in 2011, the UAS image estimate and satellite imagery count deviated in yak quantity by 2.69% and 2.86%, respectively. UASs are a reliable and low-budget alternative to animal surveys. No discernable changes in animal behaviors and animal distributions were observed as the UAS passed at a height of 700 m, and the accuracy of UAS imagery counts were not significantly affected by the short-distance animal movement and image mosaicking errors. The experimental results illustrate the advantages of the combination of satellite and UAS imagery in modeling animal population dynamics.


Assuntos
Gado , Áreas Alagadas , Aeronaves , Animais , China , Dinâmica Populacional , Imagens de Satélites , Ovinos
14.
PLoS One ; 15(8): e0237063, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32756580

RESUMO

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a "bottom-up"-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population "top-down" from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.


Assuntos
Mapeamento Geográfico , Densidade Demográfica , Censos , Humanos , Imagens de Satélites/métodos , Sri Lanka/etnologia , Inquéritos e Questionários
15.
PLoS One ; 15(8): e0237806, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32813694

RESUMO

Energy infrastructures can have negative impacts on the environment. In remote and / or sparsely populated as well as in conflict-prone regions, these can be difficult to assess, in particular when they are of a large scale. Analyzing land use and land cover changes can be an important initial step towards establishing the quantity and quality of impacts. Drawing from very-high-resolution-multi-temporal-satellite-imagery, this paper reports on a study which employed the Random Forest Classifier and Land Change Modeler to derive detailed information of the spatial patterns and temporal variations of land-use and land-cover changes resulting from the China-Myanmar Oil and Gas Pipelines in Ann township in Myanmar's Rakhine State of Myanmar. Deforestation and afforestation conversion processes during pre- and post-construction periods (2010 to 2012) are compared. Whilst substantial forest areas were lost along the pipelines, this is only part of the story, as afforestation has also happened in parallel. However, afforestation areas can be of a lower value, and in order to be able to take quality of forests into account, it is of crucial importance to accompany satellite-imagery based techniques with field observation. Findings have important implications for future infrastructure development projects in conflict-affected regions in Myanmar and elsewhere.


Assuntos
Conservação dos Recursos Naturais , Monitoramento Ambiental , Campos de Petróleo e Gás , China , Agricultura Florestal , Geografia , Modelos Teóricos , Mianmar , Imagens de Satélites
16.
PLoS One ; 15(8): e0237324, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32813701

RESUMO

Flood inundation maps provide valuable information towards flood risk preparedness, management, communication, response, and mitigation at the time of disaster, and can be developed by harnessing the power of satellite imagery. In the present study, Sentinel-1 Synthetic Aperture RADAR (SAR) data and Otsu method were utilized to map flood inundation areas. Google Earth Engine (GEE) was used for implementing Otsu algorithm and processing Sentinel-1 SAR data. The results were assessed by (i) calculating a confusion matrix; (ii) comparing the submerge water areas of flooded (Aug 2018), non-flooded (Jan 2018) and previous year's flooded season (Aug 2016, Aug 2017), and (iii) analyzing historical rainfall patterns to understand the flood event. The overall accuracy for the Sentinel-1 SAR flood inundation maps of 9th and 21st August 2018 was observed as 94.3% and 94.1% respectively. The submerged area (region under water) classified significant flooding as compared to the non-flooded (January 2018) and previous year's same season (August 2015-2017) classified outputs. Summing up, observations from Sentinel-1 SAR data using Otsu algorithm in GEE can act as a powerful tool for mapping flood inundation areas at the time of disaster, and enhance existing efforts towards saving lives and livelihoods of communities, and safeguarding infrastructure and businesses.


Assuntos
Monitoramento Ambiental/métodos , Inundações/estatística & dados numéricos , Imagens de Satélites , Navegador , Monitoramento Ambiental/estatística & dados numéricos , Índia , Rios
17.
Environ Monit Assess ; 192(8): 489, 2020 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-32638119

RESUMO

Glaciers and snow cover area (SCA) plays an important role in river runoff in Himalayan region. There is a need to monitor SCA on spatio-temporal basis for better and efficient utilization of water resources. Moderate Resolution Imaging Spectroradiometer (MODIS) provides less cloudy data due to high temporal resolution as compared to other optical sensors for high elevation regions, and its 8-day snow cover product is globally used for snow cover estimation. The main objective of the present paper is to estimate annual and seasonal SCA in Chandra basin, Western Himalaya, and analysis of its variation with elevation, aspect, and slope during 2001 to 2017 using MODIS Terra (MOD10A2) and Aqua (MYD10A2) snow cover product as well as to correlate the same with temperature and precipitation using fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the global climate (ERA5) data. The total average SCA observed is 84.94% of basin area during the study period. The maximum annual average SCA was found as 91.23% in 2009 with minimum being 76.37% in 2016. Strong correlation is observed in annual and seasonal SCA with temperature which indicate that SCA variability is highly sensitive to temperature.


Assuntos
Imagens de Satélites , Neve , Clima , Monitoramento Ambiental , Camada de Gelo , Estações do Ano
18.
Nature ; 583(7814): 72-77, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32612223

RESUMO

Forests provide a series of ecosystem services that are crucial to our society. In the European Union (EU), forests account for approximately 38% of the total land surface1. These forests are important carbon sinks, and their conservation efforts are vital for the EU's vision of achieving climate neutrality by 20502. However, the increasing demand for forest services and products, driven by the bioeconomy, poses challenges for sustainable forest management. Here we use fine-scale satellite data to observe an increase in the harvested forest area (49 per cent) and an increase in biomass loss (69 per cent) over Europe for the period of 2016-2018 relative to 2011-2015, with large losses occurring on the Iberian Peninsula and in the Nordic and Baltic countries. Satellite imagery further reveals that the average patch size of harvested area increased by 34 per cent across Europe, with potential effects on biodiversity, soil erosion and water regulation. The increase in the rate of forest harvest is the result of the recent expansion of wood markets, as suggested by econometric indicators on forestry, wood-based bioenergy and international trade. If such a high rate of forest harvest continues, the post-2020 EU vision of forest-based climate mitigation may be hampered, and the additional carbon losses from forests would require extra emission reductions in other sectors in order to reach climate neutrality by 20503.


Assuntos
Agricultura Florestal/estatística & dados numéricos , Agricultura Florestal/tendências , Florestas , Biodiversidade , Biomassa , Sequestro de Carbono , Monitoramento Ambiental , Política Ambiental/economia , Política Ambiental/legislação & jurisprudência , Europa (Continente) , União Europeia/economia , Agricultura Florestal/economia , Agricultura Florestal/legislação & jurisprudência , Aquecimento Global/prevenção & controle , História do Século XXI , Imagens de Satélites , Madeira/economia
19.
Mar Pollut Bull ; 156: 111169, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32510420

RESUMO

A combination of idealised modelling and satellite imagery has been used to assess the dispersal of estuarine plume water and suspended material throughout the ecologically significant marine area off the west coast of the central North Island, New Zealand. The idealised modelling was used to elucidate the relative importance of oceanographic and meteorological conditions in controlling the horizontal structure of the estuary plumes, and then compared to plumes visible in satellite imagery and environmental monitoring data. Under low to average river flows the plumes can be categorised as either non-rotational or rotational. Rotational plumes are directed southwards under light (<5 m/s SW or <10 m/s E) winds and northwards under stronger (>5 m/s SW) winds. Non-rotational plumes remain close to the estuary mouth. The type, orientation and extent of the plumes have implications for estuarine flushing and for the dispersal of land-derived contaminants into the marine environment.


Assuntos
Estuários , Rios , Monitoramento Ambiental , Nova Zelândia , Imagens de Satélites
20.
PLoS One ; 15(6): e0234158, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32511261

RESUMO

Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41-0.46; Root Mean Square Error (RMSE): 0.60-0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three-five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three-five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery.


Assuntos
Monitoramento Ambiental/métodos , Plantas , Imagens de Satélites/métodos , Biodiversidade , Pradaria , Modelos Teóricos , Estações do Ano , África do Sul
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA