PeerJ Preprints: Spatial and Geographic Information Sciencehttps://peerj.com/preprints/index.atom?journal=peerj&subject=1445Spatial and Geographic Information Science articles published in PeerJ PreprintsOoctonus vulgatus (Hymenoptera, Mymaridae), a potential biocontrol agent to reduce populations of Philaenus spumarius (Hemiptera, Aphrophoridae) the main vector of Xylella fastidiosa in Europehttps://peerj.com/preprints/279412019-09-072019-09-07Xavier MesminMarguerite ChartoisGuenaelle GensonJean-Pierre RossiAstrid CruaudJean-Yves Rasplus
As vector of Xylella fastidiosa (Wells, 1987) in Europe, the meadow spittlebug, Philaenus spumarius (Linnaeus, 1758) (Hemiptera: Aphrophoridae) is a species of major concern. Therefore, tools and agents to control this ubiquitous insect that develops and feeds on hundreds of plant species are wanted. We conducted a field survey of P. spumarius eggs in Corsica and provide a first report of Ooctonus vulgatus Haliday, 1833 (Hymenoptera, Mymaridae) as a potential biocontrol agent of P. spumarius in Europe. To allow species identification, we summarized the main characters distinguishing O. vulgatus from other European species of Ooctonus and generated COI DNA barcodes. We also assessed parasitism rates in several sampling sites, highlighting the top-down impact of O. vulgatus on populations of P. spumarius. Based on the geographic occurrences of O. vulgatus mined in the literature, we calibrated an ecological niche model to assess its potential distribution in the Holarctic. Our results showed that O. vulgatus potential distribution overlaps that of P. spumarius. Hence, O. vulgatus appears to be a promising biocontrol agent of the meadow spittlebug in Europe and it seems advisable to conduct research on this small parasitoid wasp to assess whether it could contribute to reduce the spread and impact of X. fastidiosa in Europe.
As vector of Xylella fastidiosa (Wells, 1987) in Europe, the meadow spittlebug, Philaenus spumarius (Linnaeus, 1758) (Hemiptera: Aphrophoridae) is a species of major concern. Therefore, tools and agents to control this ubiquitous insect that develops and feeds on hundreds of plant species are wanted. We conducted a field survey of P. spumarius eggs in Corsica and provide a first report of Ooctonus vulgatus Haliday, 1833 (Hymenoptera, Mymaridae) as a potential biocontrol agent of P. spumarius in Europe. To allow species identification, we summarized the main characters distinguishing O. vulgatus from other European species of Ooctonus and generated COI DNA barcodes. We also assessed parasitism rates in several sampling sites, highlighting the top-down impact of O. vulgatus on populations of P. spumarius. Based on the geographic occurrences of O. vulgatus mined in the literature, we calibrated an ecological niche model to assess its potential distribution in the Holarctic. Our results showed that O. vulgatus potential distribution overlaps that of P. spumarius. Hence, O. vulgatus appears to be a promising biocontrol agent of the meadow spittlebugin Europe and it seems advisable to conduct research on this small parasitoid wasp to assess whether it could contribute to reduce the spread and impact of X. fastidiosa in Europe.Natural history of the critically endangered salamander Pseudoeurycea robertsihttps://peerj.com/preprints/279112019-08-202019-08-20Armando SunnyCarmen Caballero-ViñasLuis Duarte-deJesusFabiola Ramírez-CoronaJavier ManjarrezGiovanny González-DesalesXareni P. PachecoOctavio Monroy-VilchisAndrea González-Fernández
Mexico is one of the most diverse countries that is losing a large amount of forest due to land use change, these data put Mexico in fourth place for global deforestation rate, therefore, Mexico occupies the first place in number of endangered species in the world with 665 endangered species. It is important to study amphibians because they are among the most threatened vertebrates on Earth and their populations are rapidly declining worldwide due primarily to the loss and degradation of their natural habitats. Pseudoeurycea robertsi is a micro-endemic and critically endangered Plethodontid salamander from the Nevado de Toluca Volcano and to date almost nothing is known about its natural history therefore, we survey fourteen sites of the Nevado de Toluca Volcano a mountain that is part of the Trans-Mexican Volcanic Belt, Mexico. We carry out the most exhaustive sampling scheme of this species throughout the Nevado de Toluca Volcano to know the number of individuals and the microhabitat features associated with the presence of P. robertsi. Likewise, we carry out a morphometric study and coloration measurements of P. robertsi individuals and we determine the potential distribution of P. robertsi and the other 3 species of pletodontids present in the NTV using ecological niche modeling and to determine the most important habitat features associated with the presence of salamander species, as well as to know the niche overlap among salamander species. This information will help raise conservation strategies for this micro-endemic and critically endangered salamander.
Mexico is one of the most diverse countries that is losing a large amount of forest due to land use change, these data put Mexico in fourth place for global deforestation rate, therefore, Mexico occupies the first place in number of endangered species in the world with 665 endangered species. It is important to study amphibians because they are among the most threatened vertebrates on Earth and their populations are rapidly declining worldwide due primarily to the loss and degradation of their natural habitats. Pseudoeurycea robertsi is a micro-endemic and critically endangered Plethodontid salamander from the Nevado de Toluca Volcano and to date almost nothing is known about its natural history therefore, we survey fourteen sites of the Nevado de Toluca Volcano a mountain that is part of the Trans-Mexican Volcanic Belt, Mexico. We carry out the most exhaustive sampling scheme of this species throughout the Nevado de Toluca Volcano to know the number of individuals and the microhabitat features associated with the presence of P. robertsi. Likewise, we carry out a morphometric study and coloration measurements of P. robertsi individuals and we determine the potential distribution of P. robertsi and the other 3 species of pletodontids present in the NTV using ecological niche modeling and to determine the most important habitat features associated with the presence of salamander species, as well as to know the niche overlap among salamander species. This information will help raise conservation strategies for this micro-endemic and critically endangered salamander.Plant litter estimation and its correlation with sediment concentration in the Loess Plateauhttps://peerj.com/preprints/278912019-08-102019-08-10Qian LiLigang MaSuhong LiuAdilai WufuYinbo LiShengtian YangXiaodong Yang
Background. Sediment concentration in the water of the loess Plateau region has dramatically decreased during the past two decades. Plant litter is considered to be one of the most important factors for this change. Existing remote sensing studies that focus on plant litter mainly use extraction methods based on vegetation indices or changes in the plant litter. Few studies have conducted time series analyses of plant litter or considered the correlation between plant litter and soil erosion. In addition, social factors are not given enough consideration in the remote sensing and soil community. Methods. This study performs time series estimation of plant litter by integrating three-scale remotely sensed data and a random forest (RF) modeling algorithm. Predictive models are used to estimate the spatially explicit plant litter cover for the entire Loess Plateau over the last two decades (2000–2018). Then, the sediment concentration in the water was classified into 9 grades based on environmental and social-economic factors. Results. Our results demonstrate the effectiveness of the proposed predictive models at the regional scale. The areas with increased plant litter cover accounted for 67% of the total area, while the areas with decreased plant litter cover accounted for 33% of the total area. In addition, plant litter is demonstrated to be one of the top three factors contributing to the decrease in the river sediment concentration. Social-economic factors were also important for the decrease of the sediment concentration in the water, for example, the population of the rural area.
Background. Sediment concentration in the water of the loess Plateau region has dramatically decreased during the past two decades. Plant litter is considered to be one of the most important factors for this change. Existing remote sensing studies that focus on plant litter mainly use extraction methods based on vegetation indices or changes in the plant litter. Few studies have conducted time series analyses of plant litter or considered the correlation between plant litter and soil erosion. In addition, social factors are not given enough consideration in the remote sensing and soil community. Methods. This study performs time series estimation of plant litter by integrating three-scale remotely sensed data and a random forest (RF) modeling algorithm. Predictive models are used to estimate the spatially explicit plant litter cover for the entire Loess Plateau over the last two decades (2000–2018). Then, the sediment concentration in the water was classified into 9 grades based on environmental and social-economic factors. Results. Our results demonstrate the effectiveness of the proposed predictive models at the regional scale. The areas with increased plant litter cover accounted for 67% of the total area, while the areas with decreased plant litter cover accounted for 33% of the total area. In addition, plant litter is demonstrated to be one of the top three factors contributing to the decrease in the river sediment concentration. Social-economic factors were also important for the decrease of the sediment concentration in the water, for example, the population of the rural area.The expanding wall and the shrinking beach: Loss of natural coastline in Okinawa Island, Japanhttps://peerj.com/preprints/277792019-06-032019-06-03Giovanni D MasucciJames D Reimer
Okinawa is the largest and most populated island of the Ryukyu Archipelago in southern Japan and is renowned for its natural resources and beauty. Similar as to what has been happening in the rest of the country, Okinawa Island has been affected by an increasing amount of development and construction work. The trend has been particularly acute after reversion to Japanese sovereignty in 1972, following 27 years of post-war American administration. A coastline once characterized by extended sandy beaches surrounded by coral reefs now includes vast portions delimited by seawalls, revetments, and other human-made hardening structures. Additionally, a significant part of coastal Okinawa Island is now constituted by artificially reclaimed land. Nevertheless, the degree of severity of the current situation is unclear, due to the lack of both published studies and easily accessible and updated datasets. The aims of this study were to quantify the extension of coastline alterations in Okinawa Island, including the amount of land-filling performed over the last 51 years, and to describe the coastlines that have been altered the most as well as those that are still relatively pristine. Our analyses were performed using a reference map of Okinawa Island based on GIS vector data extracted from the OpenStreetMap (OSM) coastline dataset, in addition to satellite and aerial photography from multiple providers. We measured 431.8 km of altered coastline, equal to about 63% of the total length of coastline in Okinawa Island. Habitat fragmentation is also an issue as the remaining natural coastline was broken into 239 distinct tracts (mean length = 1.05 km). Finally, 21.03 km2of the island’s surface were of land reclaimed over the last 51 years. The west coast has been altered the most, while the east coast is in relatively more natural condition, particularly the northern part, which has the largest amount of uninterrupted natural coastline. Given the importance of ecosystem services that coastal and marine ecosystems provide to local populations of subtropical islands, including significant economic income from tourism, conservation of remaining natural coastlines should be given high priority.
Okinawa is the largest and most populated island of the Ryukyu Archipelago in southern Japan and is renowned for its natural resources and beauty. Similar as to what has been happening in the rest of the country, Okinawa Island has been affected by an increasing amount of development and construction work. The trend has been particularly acute after reversion to Japanese sovereignty in 1972, following 27 years of post-war American administration. A coastline once characterized by extended sandy beaches surrounded by coral reefs now includes vast portions delimited by seawalls, revetments, and other human-made hardening structures. Additionally, a significant part of coastal Okinawa Island is now constituted by artificially reclaimed land. Nevertheless, the degree of severity of the current situation is unclear, due to the lack of both published studies and easily accessible and updated datasets. The aims of this study were to quantify the extension of coastline alterations in Okinawa Island, including the amount of land-filling performed over the last 51 years, and to describe the coastlines that have been altered the most as well as those that are still relatively pristine. Our analyses were performed using a reference map of Okinawa Island based on GIS vector data extracted from the OpenStreetMap (OSM) coastline dataset, in addition to satellite and aerial photography from multiple providers. We measured 431.8 km of altered coastline, equal to about 63% of the total length of coastline in Okinawa Island. Habitat fragmentation is also an issue as the remaining natural coastline was broken into 239 distinct tracts (mean length = 1.05 km). Finally, 21.03 km2of the island’s surface were of land reclaimed over the last 51 years. The west coast has been altered the most, while the east coast is in relatively more natural condition, particularly the northern part, which has the largest amount of uninterrupted natural coastline. Given the importance of ecosystem services that coastal and marine ecosystems provide to local populations of subtropical islands, including significant economic income from tourism, conservation of remaining natural coastlines should be given high priority.Seismic stratigraphy of the broad, low-gradient continental shelf of the Palaeo-Agulhas Plain, South Africahttps://peerj.com/preprints/277572019-05-242019-05-24Hayley C CawthraPeter FrenzelAnnette HahnJohn ComptonLukas GanderMatthias Zabel
The continental shelf of the Palaeo-Agulhas Plain (PAP) is scattered with Pleistocene deposits with subdued topography. Their exaggerated lateral extension is the expression of a flat underlying substrate and availability of accommodation space, depositional processes and response to glacio-eustatic sea-level change have influenced deposition and distribution of these units. We present new results for the upper ~30 m (up to ~200 ka) of the stratigraphic record in this area and show that this shelf offers the opportunity to examine the response of a stable tectonic setting to the effects of sea-level change. This paper presents the results of extensive sub-bottom profiling surveys and chronostratigraphic investigations from marine sediment vibracores. Radiocarbon and Optically stimulated Luminescence dates are integrated into a seismic stratigraphic model composed of twenty Quaternary units, where two depositional sequences are bounded by shelf-wide unconformities. The upper sequence was cored where Pleistocene deposits were observed to be close to the seafloor and are draped in a thin veneer of marine shelf sediment and allow us to describe the environments of deposition of the PAP. The most pervasive stratigraphic pattern in these shelf deposits is made up of the depositional sequence remnant of the Falling Stage Systems Tract (FSST) forced regression from Marine Isotope Stage 5e–2. The other dominant stratigraphic group is the Transgressive Systems Tract (TST) associated with the Postglacial Marine Transgression. Surprisingly, the TST makes up an almost equal proportion of deposits in both sequences in the sedimentological record as the FSST, despite the shorter temporal span of the TST. The sub-bottom profiles were acquired on regional surveys extending from the Breede River in the west to Plettenberg Bay in the east, and to a maximum depth of 110 m below Mean Sea Level, with the exception of one ~200 m deep shelf-edge profile.
The continental shelf of the Palaeo-Agulhas Plain (PAP) is scattered with Pleistocene deposits with subdued topography. Their exaggerated lateral extension is the expression of a flat underlying substrate and availability of accommodation space, depositional processes and response to glacio-eustatic sea-level change have influenced deposition and distribution of these units. We present new results for the upper ~30 m (up to ~200 ka) of the stratigraphic record in this area and show that this shelf offers the opportunity to examine the response of a stable tectonic setting to the effects of sea-level change. This paper presents the results of extensive sub-bottom profiling surveys and chronostratigraphic investigations from marine sediment vibracores. Radiocarbon and Optically stimulated Luminescence dates are integrated into a seismic stratigraphic model composed of twenty Quaternary units, where two depositional sequences are bounded by shelf-wide unconformities. The upper sequence was cored where Pleistocene deposits were observed to be close to the seafloor and are draped in a thin veneer of marine shelf sediment and allow us to describe the environments of deposition of the PAP. The most pervasive stratigraphic pattern in these shelf deposits is made up of the depositional sequence remnant of the Falling Stage Systems Tract (FSST) forced regression from Marine Isotope Stage 5e–2. The other dominant stratigraphic group is the Transgressive Systems Tract (TST) associated with the Postglacial Marine Transgression. Surprisingly, the TST makes up an almost equal proportion of deposits in both sequences in the sedimentological record as the FSST, despite the shorter temporal span of the TST. The sub-bottom profiles were acquired on regional surveys extending from the Breede River in the west to Plettenberg Bay in the east, and to a maximum depth of 110 m below Mean Sea Level, with the exception of one ~200 m deep shelf-edge profile.Rapid remote sensing assessment of landscape-scale impacts from the California Camp Firehttps://peerj.com/preprints/276542019-04-152019-04-15Jeffrey ChambersCaralyn GormanYanlei FengMargaret TornJared Stapp
The Camp Fire rapidly spread across a landscape in Butte County, California, toward the city of Paradise in the early morning hours of 8 November 2018. Here we provide a set of initial tools and analyses that are useful to a variety of stakeholders, including: (1) a visualization app for GOES 16 data and the surrounding landscape showing the rapid spread of the fire from 6:37-10:47 a.m. local time; (2) processed Landsat 8 images for before, during, and after the fire, along with a tool for visualizing regional impacts; (3) a timeline of fire spread from ignition over the first four hours; and (4) a description of a potential early warning app that could make use of existing data, visualization, and analysis tools, to provide additional information for effective evacuation of communities threatened by rapidly moving wildfires. Using these tools we estimate that, over the first hour, the Camp Fire was consuming ~200 ha/minute of vegetation with a linear spread rate of 14 km over the fire’s first 25 minutes, or ~33km/hr. We briefly discuss broader use of remote sensing and geospatial analysis for fire research and risk management.
The Camp Fire rapidly spread across a landscape in Butte County, California, toward the city of Paradise in the early morning hours of 8 November 2018. Here we provide a set of initial tools and analyses that are useful to a variety of stakeholders, including: (1) a visualization app for GOES 16 data and the surrounding landscape showing the rapid spread of the fire from 6:37-10:47 a.m. local time; (2) processed Landsat 8 images for before, during, and after the fire, along with a tool for visualizing regional impacts; (3) a timeline of fire spread from ignition over the first four hours; and (4) a description of a potential early warning app that could make use of existing data, visualization, and analysis tools, to provide additional information for effective evacuation of communities threatened by rapidly moving wildfires. Using these tools we estimate that, over the first hour, the Camp Fire was consuming ~200 ha/minute of vegetation with a linear spread rate of 14 km over the fire’s first 25 minutes, or ~33km/hr. We briefly discuss broader use of remote sensing and geospatial analysis for fire research and risk management.Variations in ecosystem service value in response to land use/land cover changes in Central Asia over 1995-2035https://peerj.com/preprints/276452019-04-112019-04-11Jiangyue LiHongxing ChenChi ZhangTao Pan
Acute farmland expansion and rapid urbanization in Central Asia have accelerated land use/land cover changes, which has significant effect onecosystemservice. However, the spatio-temporal changes in ecosystem service values in Central Asia are not well understood. Here, based on land use products with 300-m resolution for the years of 1995, 2005 and 2015 and transfer methodology, we predicted LUCC for 2025 and 2035 using CA-Markov, assessed changes in ecosystem service value in response to LUCC dynamics, and explored the elasticity for the response of ESV to LULC changes. We found significant expansions of cropland and urban and shrinking of water bodies and bare land during 1995-2035. Overall ESVs had an increasing trend from 1995-2035, which was mainly due to the increasing cropland and construction land. The combined valueofecosystemservices of cropland, grassland, water bodies accounted for over 90% of the total ESVs. However, LULC analysis showed that the area of water body reduced by 21.80% from 1995 to 2015 and continued to decrease by 21.14% from 2015 to 2035, indicating that approximately 63.37 billion US$ of ESVs lost in Central Asia. Biodiversity, food production and water regulation were major service functions, accounting for 80.52% of the total ESVs . Our results demonstrated that theeffective land-usepolicies should be made to control farmland expansion and protect water bodies, grassland and forestland for better sustainable ecosystem services.
Acute farmland expansion and rapid urbanization in Central Asia have accelerated land use/land cover changes, which has significant effect onecosystemservice. However, the spatio-temporal changes in ecosystem service values in Central Asia are not well understood. Here, based on land use products with 300-m resolution for the years of 1995, 2005 and 2015 and transfer methodology, we predicted LUCC for 2025 and 2035 using CA-Markov, assessed changes in ecosystem service value in response to LUCC dynamics, and explored the elasticity for the response of ESV to LULC changes. We found significant expansions of cropland and urban and shrinking of water bodies and bare land during 1995-2035. Overall ESVs had an increasing trend from 1995-2035, which was mainly due to the increasing cropland and construction land. The combined valueofecosystemservices of cropland, grassland, water bodies accounted for over 90% of the total ESVs. However, LULC analysis showed that the area of water body reduced by 21.80% from 1995 to 2015 and continued to decrease by 21.14% from 2015 to 2035, indicating that approximately 63.37 billion US$ of ESVs lost in Central Asia. Biodiversity, food production and water regulation were major service functions, accounting for 80.52% of the total ESVs . Our results demonstrated that theeffective land-usepolicies should be made to control farmland expansion and protect water bodies, grassland and forestland for better sustainable ecosystem services.Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoringhttps://peerj.com/preprints/276302019-04-032019-04-03Xiangyu GeJingzhe WangJianli DingXiaoyi CaoZipeng ZhangJie LiuXiaohang Li
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the spaceborne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland ( 2.5 ×104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477 and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the spaceborne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland ( 2.5 ×104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477 and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.Geomorpho90m - Global high-resolution geomorphometry layers: empirical evaluation and accuracy assessmenthttps://peerj.com/preprints/275952019-03-182019-03-18Giuseppe AmatulliDaniel McInerneyTushar SethiPeter StroblSami Domisch
Topographical relief is composed of the vertical and horizontal variations of the Earth's terrain and drives processes in geography, climatology, hydrology, and ecology. Its assessment and characterisation is fundamental for various types of modelling and simulation analyses. In this regard, the Multi-Error-Removed Improved Terrain (MERIT) Digital Elevation Model (DEM) is the best global, high-resolution DEM currently available at a 3 arc-seconds (90 m) resolution. This is an improved product as multiple error components have been corrected from the underlying Shuttle Radar Topography Mission (SRTM3) and ALOS World 3D - 30 m (AW3D30) DEMs. To depict topographical variations worldwide, we developed the Geomorpho90m dataset comprising of different geomorphometry features derived from the MERIT-DEM. The fully standardised geomorphometry variables consist of layers that describe (i) the rate of change using the first and second order derivatives, (ii) the ruggedness, and (iii) the geomorphology landform. To assess how remaining artefacts in the MERIT-DEM could affect the derived topographic variables, we compared our results with the same variables generated using the 3D Elevation Program (3DEP) DEM, which is the highest quality DEM for the United States of America. We compared the two data sources by calculating the first order derivative (i.e., the rate of change through space measured in degrees) of the difference between a MERIT-derived vs. a 3DEP-derived topographic variable. All newly-created topographic variables are readily available at resolutions of 3 and 7.5 arc-seconds under the WGS84 geographic system, and at a spatial resolution of 100 m under the Equi7 projection. The newly-developed Geomorpho90m dataset provides a globally standardised dataset for environmental models and analyses in the field of geography, geology, hydrology, ecology and biogeography.
Topographical relief is composed of the vertical and horizontal variations of the Earth's terrain and drives processes in geography, climatology, hydrology, and ecology. Its assessment and characterisation is fundamental for various types of modelling and simulation analyses. In this regard, the Multi-Error-Removed Improved Terrain (MERIT) Digital Elevation Model (DEM) is the best global, high-resolution DEM currently available at a 3 arc-seconds (90 m) resolution. This is an improved product as multiple error components have been corrected from the underlying Shuttle Radar Topography Mission (SRTM3) and ALOS World 3D - 30 m (AW3D30) DEMs. To depict topographical variations worldwide, we developed the Geomorpho90m dataset comprising of different geomorphometry features derived from the MERIT-DEM. The fully standardised geomorphometry variables consist of layers that describe (i) the rate of change using the first and second order derivatives, (ii) the ruggedness, and (iii) the geomorphology landform. To assess how remaining artefacts in the MERIT-DEM could affect the derived topographic variables, we compared our results with the same variables generated using the 3D Elevation Program (3DEP) DEM, which is the highest quality DEM for the United States of America. We compared the two data sources by calculating the first order derivative (i.e., the rate of change through space measured in degrees) of the difference between a MERIT-derived vs. a 3DEP-derived topographic variable. All newly-created topographic variables are readily available at resolutions of 3 and 7.5 arc-seconds under the WGS84 geographic system, and at a spatial resolution of 100 m under the Equi7 projection. The newly-developed Geomorpho90m dataset provides a globally standardised dataset for environmental models and analyses in the field of geography, geology, hydrology, ecology and biogeography.Estimating nitrogen and phosphorus concentrations in streams and rivers across the contiguous United States: a machine learning frameworkhttps://peerj.com/preprints/275852019-03-132019-03-13Longzhu ShenGiuseppe AmatulliTushar SethiPeter RaymondSami Domisch
Nitrogen (N) and Phosphorus (P) are essential nutrients for life processes in water bodies but in excessive quantities, they are a significant source of aquatic pollution. Eutrophication has now become widespread due to such an imbalance, and is largely attributed to anthropogenic activity. In view of this phenomenon, we present a new dataset and statistical method for estimating and mapping elemental and compound con- centrations of N and P at a resolution of 30 arc-seconds (∼1 km) for the conterminous US. The model is based on a Random Forest (RF) machine learning algorithm that was fitted with environmental variables and seasonal N and P concentration observations from 230,000 stations spanning across US stream networks. Accounting for spatial and temporal variability offers improved accuracy in the analysis of N and P cycles. The algorithm has been validated with an internal and external validation procedure that is able to explain 70-83% of the variance in the model. The dataset is ready for use as input in a variety of environmental models and analyses, and the methodological framework can be applied to large-scale studies on N and P pollution, which include water quality, species distribution and water ecology research worldwide.
Nitrogen (N) and Phosphorus (P) are essential nutrients for life processes in water bodies but in excessive quantities, they are a significant source of aquatic pollution. Eutrophication has now become widespread due to such an imbalance, and is largely attributed to anthropogenic activity. In view of this phenomenon, we present a new dataset and statistical method for estimating and mapping elemental and compound con- centrations of N and P at a resolution of 30 arc-seconds (∼1 km) for the conterminous US. The model is based on a Random Forest (RF) machine learning algorithm that was fitted with environmental variables and seasonal N and P concentration observations from 230,000 stations spanning across US stream networks. Accounting for spatial and temporal variability offers improved accuracy in the analysis of N and P cycles. The algorithm has been validated with an internal and external validation procedure that is able to explain 70-83% of the variance in the model. The dataset is ready for use as input in a variety of environmental models and analyses, and the methodological framework can be applied to large-scale studies on N and P pollution, which include water quality, species distribution and water ecology research worldwide.