A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Remote Sensing ◽

10.3390/rs12244114 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4114

Author(s):

Shaobo Sun ◽

Yonggen Zhang ◽

Zhaoliang Song ◽

Baozhang Chen ◽

Yangjian Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Open Access ◽

Spatial Resolution ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Regional Scale ◽

Google Earth ◽

Human Welfare ◽

Bohai Rim ◽

Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

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Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

Remote Sensing ◽

10.3390/rs12010187 ◽

2020 ◽

Vol 12 (1) ◽

pp. 187 ◽

Cited By ~ 3

Author(s):

Viktor Myroniuk ◽

Mykola Kutia ◽

Arbi J. Sarkissian ◽

Andrii Bilous ◽

Shuguang Liu

Keyword(s):

Time Series ◽

Spatial Resolution ◽

Forest Cover ◽

Regional Scale ◽

Forest Products ◽

Google Earth ◽

Tree Cover ◽

Landsat 8 ◽

Fragmented Landscapes ◽

Forest Mapping

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

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Influence of the North Atlantic oscillation on the atmospheric levels of benzo[a]pyrene over Europe

Climate Dynamics ◽

10.1007/s00382-021-05766-0 ◽

2021 ◽

Author(s):

Pedro Jiménez-Guerrero ◽

Nuno Ratola

Keyword(s):

Spatial Pattern ◽

North Atlantic Oscillation ◽

North Atlantic ◽

Transport Model ◽

Regional Scale ◽

The South ◽

Climatic Fluctuations ◽

The North ◽

The North Atlantic ◽

The North Atlantic Oscillation

AbstractThe atmospheric concentration of persistent organic pollutants (and of polycyclic aromatic hydrocarbons, PAHs, in particular) is closely related to climate change and climatic fluctuations, which are likely to influence contaminant’s transport pathways and transfer processes. Predicting how climate variability alters PAHs concentrations in the atmosphere still poses an exceptional challenge. In this sense, the main objective of this contribution is to assess the relationship between the North Atlantic Oscillation (NAO) index and the mean concentration of benzo[a]pyrene (BaP, the most studied PAH congener) in a domain covering Europe, with an emphasis on the effect of regional-scale processes. A numerical simulation for a present climate period of 30 years was performed using a regional chemistry transport model with a 25 km spatial resolution (horizontal), higher than those commonly applied. The results show an important seasonal behaviour, with a remarkable spatial pattern of difference between the north and the south of the domain. In winter, higher BaP ground levels are found during the NAO+ phase for the Mediterranean basin, while the spatial pattern of this feature (higher BaP levels during NAO+ phases) moves northwards in summer. These results show deviations up to and sometimes over 100% in the BaP mean concentrations, but statistically significant signals (p<0.1) of lower changes (20–40% variations in the signal) are found for the north of the domain in winter and for the south in summer.

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Biogeography of acoustic biodiversity of NW Mediterranean coralligenous reefs

Scientific Reports ◽

10.1038/s41598-021-96378-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lucia Di Iorio ◽

Manon Audax ◽

Julie Deter ◽

Florian Holon ◽

Julie Lossent ◽

...

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Western Mediterranean ◽

Ecosystem Functions ◽

Marine Biodiversity ◽

Predator Species ◽

The North ◽

Mediterranean Habitats ◽

Geographical Regions ◽

Nw Mediterranean

AbstractMonitoring the biodiversity of key habitats and understanding the drivers across spatial scales is essential for preserving ecosystem functions and associated services. Coralligenous reefs are threatened marine biodiversity hotspots that are challenging to monitor. As fish sounds reflect biodiversity in other habitats, we unveiled the biogeography of coralligenous reef sounds across the north-western Mediterranean using data from 27 sites covering 2000 km and 3 regions over a 3-year period. We assessed how acoustic biodiversity is related to habitat parameters and environmental status. We identified 28 putative fish sound types, which is up to four times as many as recorded in other Mediterranean habitats. 40% of these sounds are not found in other coastal habitats, thus strongly related to coralligenous reefs. Acoustic diversity differed between geographical regions. Ubiquitous sound types were identified, including sounds from top-predator species and others that were more specifically related to the presence of ecosystem engineers (red coral, gorgonians), which are key players in maintaining habitat function. The main determinants of acoustic community composition were depth and percentage coverage of coralligenous outcrops, suggesting that fish-related acoustic communities exhibit bathymetric stratification and are related to benthic reef assemblages. Multivariate analysis also revealed that acoustic communities can reflect different environmental states. This study presents the first large-scale map of acoustic fish biodiversity providing insights into the ichthyofauna that is otherwise difficult to assess because of reduced diving times. It also highlights the potential of passive acoustics in providing new aspects of the correlates of biogeographical patterns of this emblematic habitat relevant for monitoring and conservation.

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Elevational Movement of Vegetation Greenness on the Tibetan Plateau: Evidence from the Landsat Satellite Observations during the Last Three Decades

Atmosphere ◽

10.3390/atmos12020161 ◽

2021 ◽

Vol 12 (2) ◽

pp. 161

Author(s):

Liheng Lu ◽

Xiaoqian Shen ◽

Ruyin Cao

Keyword(s):

Tibetan Plateau ◽

Spatial Resolution ◽

Climate Warming ◽

Vertical Movement ◽

The Tibetan Plateau ◽

Upward Movement ◽

Vegetation Growth ◽

Vegetation Greenness ◽

Mountainous Regions ◽

Vegetation Activities

The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.

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Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

ENVIRONMENTAL SYSTEMS RESEARCH ◽

10.1186/s40068-019-0159-8 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Gizachew Kabite Wedajo ◽

Misgana K. Muleta ◽

Berhan Gessesse ◽

Sifan A. Koriche

Keyword(s):

Climate Change ◽

Trend Analysis ◽

River Basin ◽

Climate Change Adaptation ◽

Spatial Relationship ◽

Local Scale ◽

Climate Variables ◽

Climatic Zones ◽

Vegetation Greenness ◽

Climate Change Adaptation Strategies

Abstract Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale.

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Regional-scale analysis of dune-beach systems using Google Earth Engine

10.5194/egusphere-egu21-12923 ◽

2021 ◽

Author(s):

Melissa Latella ◽

Arjen Luijendijk ◽

Carlo Camporeale

Keyword(s):

Ecosystem Services ◽

Satellite Images ◽

Regional Scale ◽

Google Earth ◽

Classification Algorithm ◽

Mitigation Strategies ◽

Training Data ◽

Machine Learning Techniques ◽

Beach Width ◽

Google Earth Engine

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only.&#160;According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities.&#160;

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Metamorphic Gradient: A Regional-Scale Area Selection Criterion for Gold in the Northeastern Superior Province, Eastern Canadian Shield

SEG Discovery ◽

10.5382/segnews.2007-69.fea ◽

2007 ◽

pp. 1-15

Author(s):

Michel Gauthier ◽

Sylvain Trépanier ◽

Stephen Gardoll

Keyword(s):

Regional Scale ◽

Selection Criterion ◽

Superior Province ◽

James Bay ◽

Area Selection ◽

The North ◽

First Pass ◽

Abitibi Subprovince ◽

Frontier Region ◽

Metamorphic Gradient

ABSTRACT One hundred years after the first gold discoveries in the Abitibi subprovince, the Archean James Bay region to the north is experiencing a major exploration boom. Poor geologic coverage in this part of the northeastern Superior province has hindered the application of traditional Abitibi exploration criteria such as crustal-scale faults and “Timiskaming-type” sedimentary rocks. New area selection criteria are needed for successful greenfield exploration in this frontier region, and the use of steep metamorphic gradients is presented as a possible alternative. The statistical robustness of the metamorphic gradient area selection criterion was confirmed by using the curve of the receiver operating characteristic (ROC) to estimate the correlation between metamorphic fronts and the distribution of known Abitibi orogenic gold producers. The criterion was then applied to the James Bay region during a first-pass craton-scale exploration program. This was part of the strategy that led to the discovery of the Eleonore multimillion-ounce gold deposit in 2004.

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Different trends between extreme and median surface aerosol extinction coefficients over China inferred from quality controlled visibility data

10.5194/acp-2017-335 ◽

2017 ◽

Author(s):

Jing Li ◽

Chengcai Li ◽

Chunsheng Zhao

Keyword(s):

Northwest China ◽

Positive Trend ◽

Median Surface ◽

Aerosol Extinction ◽

Significance Level ◽

The North ◽

Synoptic Conditions ◽

The Mean ◽

Mean Trend ◽

Year 2000

Abstract. Although the temporal changes of aerosol properties have been widely investigated, the majority focused on the averaged condition without much emphasis on the extremes. However, the latter can be more important in terms of human health and climate change. This study uses a previously validated, quality-controlled visibility dataset to investigate the long-term trends of extreme surface aerosol extinction coefficient (AEC) over China, and compare them with the median trends. Two methods are used to independently evaluate the trends, which arrive at consistent results. The sign of extreme and median trends are generally coherent, whereas their magnitudes show distinct spatial and temporal differences. In the 1980s, an overall positive trend is found throughout China with the extreme trend exceeding the mean trend, except for Northwest China and the North China Plain. In the 1990s, AEC over Northeast and Northwest China starts to decline while the rest of the country still exhibits an increase. The extreme trends continue to dominate in the south while it yields to the mean trend in the north. After year 2000, the extreme trend becomes weaker than the mean trend overall in terms of both the magnitude and significance level. The annual trend can be primarily attributed to winter and fall trends. The results suggest that the decadal changes of pollution in China may be governed by different mechanisms. Synoptic conditions that often result in extreme air quality changes might dominate in the 1980s, whereas emission increase might be the main factor for the 2000s.

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Climate impact on the development of Pre-Classic Maya civilisation

Climate of the Past ◽

10.5194/cp-14-1253-2018 ◽

2018 ◽

Vol 14 (8) ◽

pp. 1253-1273 ◽

Cited By ~ 2

Author(s):

Kees Nooren ◽

Wim Z. Hoek ◽

Brian J. Dermody ◽

Didier Galop ◽

Sarah Metcalfe ◽

...

Keyword(s):

North Atlantic ◽

Agricultural Intensification ◽

Climatic Variability ◽

Regional Scale ◽

Climate Impact ◽

Classic Period ◽

Beach Ridge ◽

Maya Lowlands ◽

The North ◽

The Impact

Abstract. The impact of climate change on the development and disintegration of Maya civilisation has long been debated. The lack of agreement among existing palaeoclimatic records from the region has prevented a detailed understanding of regional-scale climatic variability, its climatic forcing mechanisms and its impact on the ancient Maya. We present two new palaeo-precipitation records for the central Maya lowlands, spanning the Pre-Classic period (1800 BCE–250 CE), a key epoch in the development of Maya civilisation. A beach ridge elevation record from world's largest late Holocene beach ridge plain provides a regional picture, while Lake Tuspan's diatom record is indicative of precipitation changes at a local scale. We identify centennial-scale variability in palaeo-precipitation that significantly correlates with the North Atlantic δ14C atmospheric record, with a comparable periodicity of approximately 500 years, indicating an important role of North Atlantic atmospheric–oceanic forcing on precipitation in the central Maya lowlands. Our results show that the Early Pre-Classic period was characterised by relatively dry conditions, shifting to wetter conditions during the Middle Pre-Classic period, around the well-known 850 BCE (2.8 ka) event. We propose that this wet period may have been unfavourable for agricultural intensification in the central Maya lowlands, explaining the relatively delayed development of Maya civilisation in this area. A return to relatively drier conditions during the Late Pre-Classic period coincides with rapid agricultural intensification in the region and the establishment of major cities.

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