Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images

Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over ∼3000 km 2 of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees’ distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity.

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Distribution Analysis of Heavy Metal Contaminants in Soil With Geostatistic Methods; Paper Review

Eduvest - Journal Of Universal Studies ◽

10.36418/edv.v1i7.111 ◽

2021 ◽

Vol 1 (7) ◽

pp. 620-628

Author(s):

Stefan Daniel Maramis ◽

Rika Ernawati ◽

Waterman Sulistyana Bargawa

Keyword(s):

Heavy Metals ◽

Heavy Metal ◽

Spatial Distribution ◽

Salt Spray ◽

Regional Scale ◽

Volcanic Eruptions ◽

Distribution Analysis ◽

Metal Contaminants ◽

Geostatistical Methods ◽

Heavy Metal Contaminants

Heavy metal contaminants in the soil will have a direct effect on human life. The spatial distribution of naturally occurring heavy metals is highly heterogeneous and significantly increased concentrations may be present in the soil at certain locations. Heavy metals in areas of high concentration can be distributed to other areas by surface runoff, groundwater flow, weathering and atmospheric cycles (eg wind, sea salt spray, volcanic eruptions, deposition by rivers). More and more people are now using a combination of geographic information science (GIS) with geostatistical statistical analysis techniques to examine the spatial distribution of heavy metals in soils on a regional scale. The most widely used geostatistical methods are the Inverse Distance Weighted, Kriging, and Spatial Autocorrelation methods as well as other methods. This review paper will explain clearly the source of the presence of heavy metals in soil, geostatistical methods that are often used, as well as case studies on the use of geostatistics for the distribution of heavy metals. The use of geostatistical models allows us to accurately assess the relationship between the spatial distribution of heavy metals and other parameters in a map.

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Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

Scientific Reports ◽

10.1038/s41598-020-80809-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ricardo Dalagnol ◽

Fabien H. Wagner ◽

Lênio S. Galvão ◽

Annia S. Streher ◽

Oliver L. Phillips ◽

...

Keyword(s):

Large Scale ◽

Forest Canopy ◽

Tree Mortality ◽

Structural Changes ◽

Regional Scale ◽

Airborne Lidar ◽

Amazon Forest ◽

Gap Dynamics ◽

Relative Height ◽

Mortality Estimates

AbstractWe report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20–35% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.

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Population dynamics and spatial distribution of Abaris basistriata Chaudoir, 1873 (Coleoptera: Carabidae)

Ciência e Agrotecnologia ◽

10.1590/s1413-70542016000100005 ◽

2016 ◽

Vol 40 (1) ◽

pp. 57-66 ◽

Cited By ~ 2

Author(s):

Ivan Carlos Fernandes Martins ◽

Francisco Jorge Cividanes ◽

José Carlos Barbosa ◽

Joaquim Alves de Lima Junior ◽

Lourival Dias Campos

Keyword(s):

Population Dynamics ◽

Spatial Distribution ◽

Probabilistic Models ◽

Large Population ◽

Linear Interpolation ◽

Distribution Data ◽

Distribution Analysis ◽

Natural Habitats ◽

Refuge Area ◽

Dispersion Indices

ABSTRACT Abaris basistriata, a beetle species dominant in agroecosystems and natural habitats, may benefit from the establishment of nearby refuge areas or crop field centers. To confirm this hypothesis, we analyzed the spatial distribution of the species and verified the population dynamics of this predator in a soybean/corn rotation crop and a central refuge area. The 1-ha experimental area was divided in half by a range of herbaceous plants (2 m in width and 80 m in length). Beetle samples were collected using pitfall traps every fortnight during the in-season and every month during the off-season (a total of 27 sampling occurrences). Population fluctuation was analyzed by correlating the total number of specimens with plant phenology. We used multiple regression analysis with variable (stepwise) selection to examine the influence of meteorological factors on species occurrence. To determine the spatial distribution, data were analyzed using dispersion indices and probabilistic models based on the Coleoptera frequency distribution. Distribution visualization was assessed using a linear interpolation map. A total of 143 A. basistriata specimens were collected, with 83 from the soybean/corn area and 60 from the refuge area. Periods of large population size occurred during a season with high rainfall and high maximum and minimum temperatures. On the basis of the spatial distribution analysis of A. basistriata, it is likely that the beetles occur in an aggregate form, preferably in the refuge area.

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Spatial Distribution Analysis of Attributes of Rock Joints Using Geomathematical Methods.

Shigen-to-Sozai ◽

10.2473/shigentosozai.112.907 ◽

1996 ◽

Vol 112 (13) ◽

pp. 907-914 ◽

Cited By ~ 2

Author(s):

Katsuaki KOIKE ◽

Yoshifumi NOGUCHI ◽

Hiroshi IWASAKI ◽

Katsuhiko KANEKO

Keyword(s):

Spatial Distribution ◽

Rock Joints ◽

Distribution Analysis

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Niche Sharing in Intertidal Mollusks and Decapods in Rocky Shore of Easter Island

Vestnik Zoologii ◽

10.2478/vzoo-2019-0037 ◽

2019 ◽

Vol 53 (5) ◽

pp. 417-422

Author(s):

P. De los Ríos ◽

E. Ibáñez Arancibia

Keyword(s):

Spatial Distribution ◽

Negative Binomial Distribution ◽

Binomial Distribution ◽

Rocky Shore ◽

Negative Binomial ◽

Null Model ◽

Easter Island ◽

Published Data ◽

Distribution Analysis ◽

Field Works

Abstract The coastal marine ecosystems in Easter Island have been poorly studied, and the main studies were isolated species records based on scientific expeditions. The aim of the present study is to apply a spatial distribution analysis and niche sharing null model in published data on intertidal marine gastropods and decapods in rocky shore in Easter Island based in field works in 2010, and published information from CIMAR cruiser in 2004. The field data revealed the presence of decapods Planes minutus (Linnaeus, 1758) and Leptograpsus variegatus (Fabricius, 1793), whereas it was observed the gastropods Nodilittorina pyramidalis pascua Rosewater, 1970 and Nerita morio (G. B. Sowerby I., 1833). The available information revealed the presence of more species in data collected in 2004 in comparison to data collected in 2010, with one species markedly dominant in comparison to the other species. The spatial distribution of species reported in field works revealed that P. minutus and N. morio have aggregated pattern and negative binomial distribution, L. variegatus had uniform pattern with binomial distribution, and finally N. pyramidalis pascua, in spite of aggregated distribution pattern, had not negative binomial distribution. Finally, the results of null model revealed that the species reported did not share ecological niche due to competition absence. The results would agree with other similar information about littoral and sub-littoral fauna for Easter Island.

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Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

Remote Sensing ◽

10.3390/rs13020284 ◽

2021 ◽

Vol 13 (2) ◽

pp. 284

Author(s):

Dan Lu ◽

Yahui Wang ◽

Qingyuan Yang ◽

Kangchuan Su ◽

Haozhe Zhang ◽

...

Keyword(s):

Spatial Distribution ◽

Relative Error ◽

Urban Areas ◽

Large Scale ◽

Population Distribution ◽

Spatial Optimization ◽

Distribution Data ◽

Mountainous Areas ◽

Mean Relative Error ◽

Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

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Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework

The International Journal of High Performance Computing Applications ◽

10.1177/10943420211019118 ◽

2021 ◽

pp. 109434202110191

Author(s):

David McCallen ◽

Houjun Tang ◽

Suiwen Wu ◽

Eric Eckert ◽

Junfei Huang ◽

...

Keyword(s):

Soil Structure ◽

Ground Motions ◽

Regional Scale ◽

Historical Earthquakes ◽

Department Of Energy ◽

Simulation Framework ◽

Data Set ◽

Major Barrier ◽

Local Soil ◽

Computational Workflow

Accurate understanding and quantification of the risk to critical infrastructure posed by future large earthquakes continues to be a very challenging problem. Earthquake phenomena are quite complex and traditional approaches to predicting ground motions for future earthquake events have historically been empirically based whereby measured ground motion data from historical earthquakes are homogenized into a common data set and the ground motions for future postulated earthquakes are probabilistically derived based on the historical observations. This procedure has recognized significant limitations, principally due to the fact that earthquake ground motions tend to be dictated by the particular earthquake fault rupture and geologic conditions at a given site and are thus very site-specific. Historical earthquakes recorded at different locations are often only marginally representative. There has been strong and increasing interest in utilizing large-scale, physics-based regional simulations to advance the ability to accurately predict ground motions and associated infrastructure response. However, the computational requirements for simulations at frequencies of engineering interest have proven a major barrier to employing regional scale simulations. In a U.S. Department of Energy Exascale Computing Initiative project, the EQSIM application development is underway to create a framework for fault-to-structure simulations. This framework is being prepared to exploit emerging exascale platforms in order to overcome computational limitations. This article presents the essential methodology and computational workflow employed in EQSIM to couple regional-scale geophysics models with local soil-structure models to achieve a fully integrated, complete fault-to-structure simulation framework. The computational workflow, accuracy and performance of the coupling methodology are illustrated through example fault-to-structure simulations.

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