Featured Paper
Title: Area between Peaks Feature in the Derivative Reflectance Curve as a Sensitive Indicator of Change in Chlorophyll Concentration
Eric Ariel L. Salas, Geoffrey M. Henebry
Abstract: Vegetation spectral features can detect chlorophyll concentrations. Two key spectral features evident in the first derivative (FD) of reflectance constitute the two main peaks: one located around 685-705 nm and the other near 710-725 nm. We propose that the area between peaks (ABP) can be used as a sensitive indicator of changes in the photosynthetic pigments at leaf level and demonstrate it using a high-spectral-resolution dataset of maize leaves collected by Gitelson and coworkers (2005). We find significant high positive correlations (r2 > 0.90) between chlorophyll concentrations and both the ABP and its continuum length feature. Read more here.
Effects of climate change on nutrient pollution: nitrogen and phosphorus loadings 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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