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Effects of climate change on nutrient pollution: nitrogen and phosphorus loadings in Ohio

Title: Implications of climate change on nutrient pollution: a look into the nitrogen and phosphorus loadings in the Great Miami and Little Miami watersheds in Ohio

Authors: Eric Ariel L. Salas, Sakthi Kumaran Subburayalu

The Great Miami (GM) and Little Miami (LM) watersheds in Ohio are included in the 51 major river systems in the United States for long-term assessment of conditions of water quality under the USGS National Water-Quality Assessment (NAWQA) Program. Nitrogen (N) and phosphorus (P) loadings have decreased over the years in the GM and LM basins, however, they still remain among the highest concentrations detected in the nation. The latest nutrient loading analysis from Ohio Environmental Protection Agency (OEPA) showed slightly above period of record averages for N and P. Significant amounts of mobilized agricultural N and P from fertilizers in watersheds are transported to the Ohio River and other coastal marine systems such as the Gulf of Mexico, leading to increased growth of harmful algal blooms. In the GM and LM watersheds, changes in flood frequency and intensity are projected to occur in the future as heavy precipitation events are likely to increase as a result of climate change. As climate plays a role for nutrient transformation and transport, studies have shown that N and P inputs to surface waters from agriculture and other sources are projected to continue to increase over the next several decades. This paper reviewed the means of how the GM and LM watersheds are ecologically affected by the N and P nutrient pollution and how climate change impacts the quantity of N and P loadings that go into the river systems. Without better understanding of the nutrient loading processes and their association with various agricultural practices and interactions with climate, there could be additional threats to water quality in both the GM and LM watersheds in decades to come. This literature review has made references to many pertinent new publications that have become available in recent years, as well as to the classic literature.

Keywords:  algal bloom; climate projections; GCM; global warming; nutrient loadings; Ohio watersheds

Citation: Eric Ariel L. Salas, Sakthi Kumaran Subburayalu. Implications of climate change on nutrient pollution: a look into the nitrogen and phosphorus loadings in the Great Miami and Little Miami watersheds in Ohio. AIMS Environmental Science, 2019, 6(3): 186-221. doi: 10.3934/environsci.2019.3.186

Access the full paper here.

The AGU’s Honors Program 2017 is Still Open For Nominations

AGU’s Honors Program is a diverse program for recognizing individuals who have made outstanding contributions to the advancement of the geophysical sciences, to the service of the community, and public outreach. The program aligns with AGU’s strategic goals in scientific leadership, talent pool development, science and society and organizational excellence.

Check this link to nominate.

Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric

Title: Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric

Authors: Eric Ariel L. Salas and Geoffrey M. Henebry

 Abstract: There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes.

Citation: Eric Ariel L. Salas, Geoffrey M. Henebry. Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric. AIMS Geosciences, 2016, 2(4): 366-390. doi: 10.3934/geosci.2016.4.366 

Download the free PDF copy here.

Species Distribution Model SDM Package Tool in R Software

I used the Species Distribution Model SDM Package Tool in R software all the time.

sdm is an object-oriented, reproducible and extensible R platform for species distribution modelling. The sdm package is designed to create a comprehensive modelling and simulation framework that: 

1) provides a standardised and unified structure for handling species distributions data and modelling techniques (e.g. a unified interface is used to fit different models offered by different packages);

2) is able to support markedly different modelling approaches;

3) enables scientists to modify the existing methods, extend the framework by developing new methods or procedures, and share them to be reproduced by the other scientists;

4) handles spatial as well as temporal data for single or multiple species;

5) employs high performance computing solutions to speed up modelling and simulations, and finally;

6) uses flexible and easy-to-use GUI interface. For more information, check the published paper by Naimi and Araujo (2016) in the journal of Ecography.

Download the package here.

MDI: Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs
by Eric Ariel L. Salas, Kenneth G. Boykin and Raul Valdez

Abstract: We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.

Keywords: object-based analysis; Pamir Mountains Tajikistan; Moment Distance; MDI; Marco Polo argali; multispectral application; image texture; arid environment

To cite the paper:
AMA Style
Salas EAL, Boykin KG, Valdez R. Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs. Remote Sensing. 2016; 8(1):78.

Chicago/Turabian Style
Salas, Eric A.L.; Boykin, Kenneth G.; Valdez, Raul. 2016. “Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs.” Remote Sens. 8, no. 1: 78.

Download the paper here (PDF).

Marco Polo Argali Habitat in the Eastern Pamir Mountains of Tajikistan

Download free GIS datasets for the Southeastern Pamir Mountains of Tajikistan

Title: Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan

Download free maps and GIS dataset here.
Download the paper here.

To cite the paper:
Salas, E.A.L.; Valdez, R.; Boykin, K.G. Geographic Layers as Landscape Drivers for the Marco Polo Argali Habitat in the Southeastern Pamir Mountains of Tajikistan. ISPRS Int. J. Geo-Inf. 2015, 4, 2094-2108.
Keywords: Tajikistan Pamirs; remote sensing; Landsat; vegetation; pasture; GIS analysis; Free GIS maps; bighorn sheep; Free sheep GIS dataset

Featured Video

The Penn State Public Broadcasting launched the Geospatial Revolution Project, an integrated public media and outreach initiative about the impact of digital mapping. Watch two of the four high-definition video episodes that have already been released. You can visit the project via: geospatialrevolution.psu.edu.

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