New paper on spruce-beetle outbreak and high-severity wildfire

Our new paper, lead by my colleagues at Colorado State University and published in the journal PLOS ONE,  investigates the short-term compounded effects of spruce beetle and wildfire in subalpine forests in southwestern Colorado. We think this work is noteworthy because areas that were more heavily affected by spruce beetle corresponded to reduced post-fire vegetation. These results suggest vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing fire. Much of the current subalpine forest in this region has been impacted by recent beetle outbreaks. In the future, these areas could experience higher burn severity which has important implications for the structure and composition of future forests.

*Banner image: beetle-induced tree mortality photographed on Little Mountain, Wyoming.

Presentation at the Ecological Society of America 2017 Meeting

I will present research of burn severity controls on post-fire Araucaria-Nothofagus regeneration in the Andean Cordillera at the ESA annual meeting in Portland, OR. My talk is part of the session entitled: (COS 153) – Communities: Spatial Patterns and Environmental Gradients on Thursday afternoon (2:10 pm), August 10th, in the Oregon Convention Center, C120-121.

Details:

Burn severity and post-fire regeneration in Chilean AraucariaNothofagus forest (COS 153 – 3)

Araucaria forest mortality in Tolhuaca National Park from the 2002 wildfire. Photo taken in March 2012.

New Paper on Grazing and Sage-grouse

Our new paper, lead by my colleagues at Colorado State University and published in the journal Ecological Applications,  evaluates responses of sage-grouse population trends to the timing and level of grazing at broad spatial and temporal scales. We think this work is noteworthy because it deals with both an important species in the West (Greater Sage-grouse) and an important land-use related to livelihoods (grazing). The work could not have been accomplished without the use of two open data sources: 1) grazing records from the Bureau of Land Management, and 2) productivity data derived from the MODIS archive.

*Banner image: sage-grouse (center of image) photographed on Seedskadee National Wildlife Refuge, Wyoming.

Call for Papers: Remote Sensing of Vegetation Parameters

The open access journal Forests has a call for papers related to remote sensing of leaf area index and other vegetation parameters. The call is open to a wide variety of applications including ecology, agriculture, forestry, food security, etc. Full details can be found here.

Deadline for manuscript submissions: 15 April 2018

Dr. Javier García-Haro
Prof. Dr. Hongliang Fang
Prof. Dr. Juan Manuel Lopez-Sanchez
Guest Editors

*Banner image: aspen canopy on Cold Spring Mountain, Colorado.

Call for Papers: Remote Sensing of Wildfire

The open access journal Remote Sensing has a call for papers related to remote sensing of wildfire. The topics of interest are diverse, ranging from wildfire risk to the ecological effects of fire. Full details can be found here.

Deadline for manuscript submissions: 30 June 2018

Wildfires (which include forest fires, grass fires, brush fires, bush fires, and peat fires, among others) are an integral part of so many ecosystems across the world. In general, these fires are primarily viewed negatively despite their favorable contributions. Here, the purpose is to gather scientists/researchers related to this topic, aiming to highlight ongoing research investigations and new applications in the field. In this framework, the editors of this Special Issue would like to invite both applied and theoretical research contributions; submissions of original works furthering knowledge concerned with any aspect of the use of remote sensing in wildfires. Note that these manuscripts must be, not only unpublished, but also not under consideration for potential publication elsewhere. In addition, the manuscripts must employ one of the following remote sensing data types: Optical, thermal, hyperspectral, active and passive microwave acquired by either airborne or spaceborne remote sensing platforms, dealing with wildfires. The topics of interest include, but not limited to:

  • Comprehending of the pre-fire conditions,
  • Forecasting of wildfire danger/risk,
  • Modelling of wildfire behavior,
  • Fighting the wildfire,
  • Modelling prescribed burning,
  • Relation between vegetation phenology and fire,
  • Monitoring of the vegetation recovery following the fire events,
  • Mapping of burn area and ecological impacts,
  • Modelling of smoke propagation, and
  • Analyzing of historical fire regimes, among others.

Dr. Quazi K. Hassan
Dr. George P. Petropoulos
Guest Editors

*Banner image: Landsat 8 image of the Sand Fire in southern California from NASA Earth Observatory.

Stratified random sampling for Landscape Analysis

Random sampling is common in ecology. We often stratify the sample to ensure an equal (or minimum) amount of plots fall within some category (e.g. soils, elevation, cover type, etc.) that is meaningful to the central question of the study. Ecologists often stratify their study area based on landscape facets (Assal et al. 2014). Furthermore, it can be advantageous to use this approach when working with remotely sensed covariates, as segregating the landscape into sub-regions of similar biophysical characteristics can isolate spectral gradients (Homer et al. 2004)

This can be accomplished very efficiently using a raster-based approach in R. In this example, I will randomly sample 5 cells in three strata, then obtain the coordinates of the cell centroid and convert the points to a SpatialPointsDataFrame using the raster library. The code below is very basic, but it can be modified to work with any discrete raster that represents the complex landscape facets of interest.

Plot of a continuous raster layer.

Plot of reclassified raster into three strata (white, yellow, green).

Plot of reclassified raster with sample points.

The sample points can now be projected and/or exported to another format (e.g. shapefile) for further analysis.

Literature Cited

Assal, T.J., Sibold, J., and R. Reich. 2014. Modeling a Historical Mountain Pine Beetle Outbreak Using Landsat MSS and Multiple Lines of Evidence. Remote Sensing of Environment 155:275-288. 

Homer, C., Huang, C., Yang, L., Wylie, B., & Coan, M. 2004. Development of a 2001 Na- tional Land-Cover Database for the United States. Photogrammetric Engineering & Remote Sensing 70:829-840.

Raster Visualization in R: Vegetation productivity in the Yuba River Watershed

Below is a brief example of raster visualization in R. It is a more thorough explanation of an example of rasterVIS that I used in a recent lecture I gave in the Department of Geography and Environment at San Francisco State University. The goal is to gain a brief understanding on vegetation productivity dynamics in the Yuba River Watershed (where the SFSU Sierra Nevada Field Campus is located) and quickly display the data using Oscar Perpiñán’s excellent R package rasterVis.

I used eMODIS (EROS Moderate Resolution Imaging Spectroradiometer) data to characterize the NDVI derived productivity of the Yuba River Basin during the month of August. eMODIS is a standardized product which uses MODIS surface reflectance from a 7-day composite period (Brown et al. 2015). I used all eMODIS images from August during the period 2000 through 2016 to calculate the 17-year August mean. I then calculated August productivity anomalies for a dry year (2014) and an average year (2006) to identify the spatial and temporal variability in productivity within the watershed (based on regional data; Malone et al. 2016).

To execute the example below, please download the data here. I have not included the base data or code to calculate the global mean and anomalies.

Load the required packages in R (note: the Rcolorbrewer library is loaded with the rasterVis library).

Load the required data:

Plot the mean productivity for August (2000-2016):

Note: the NDVI values are scaled by a value of 10000. The gray graphics along the top and left axes represent the mean of the row (right) and column (top) values.

Plot the 2014 and 2006 August productivity anomalies:

Compare dry-year anomalies (top) with average-year anomalies (bottom). The anomalies are analogous to Z-scores and were calculated as the deviation from mean, normalized by the standard deviation.

Literature Cited

Brown, J. F., D. Howard, B. Wylie, A. Frieze, L. Ji, and C. Gacke. 2015. Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition. Remote Sensing 7:16226–16240.

Malone, S. L., M. G. Tulbure, A. J. Pérez-luque, T. J. Assal, L. L. Bremer, D. P. Drucker, V. Hillis, S. Varela, and M. L. Goulden. 2016. Drought resistance across California ecosystems : evaluating changes in carbon dynamics using satellite imagery. Ecosphere 7:1–19.

Call for Papers: IJRS Special Issue at Intersection of Remote Sensing & Ecology

I serve as a reviewer for the International Journal of Remote Sensing and thought I’d pass along this call for papers on an interesting topic at the intersection of remote sensing and ecology:

International Journal of Remote Sensing

Special Issue – Call for Papers

Fine Resolution Remote Sensing of Species in Terrestrial and Coastal Ecosystems

The decline and dieback of native species and the invasion of alien species in terrestrial and coastal ecosystems can cause severe environmental changes that harm the functioning of ecosystems and reduce the services they provide. A rapid response and effective management of environmental change requires detailed information on the spatial distribution of individual species over large spatial extents and over multiple time periods. Species-level information is also needed for precision agriculture and urban forestry.

Although remote sensing has been used to map species for decades, the longstanding challenge is that the accuracy of species maps is often too low to meet the requirements of management, or the methods are too complex or location-specific to be used in routine mapping. On the other hand, in the 21st century, we have witnessed a rapid development in both fine resolution remote sensors and statistical theories and techniques, which hold great potential for accurate species mapping.

This special issue calls for cutting-edge research on using fine resolution remotely sensed data for mapping species in terrestrial or coastal ecosystems, in the following areas:

1)      Focus on terrestrial or coastal systems (including natural, urban, agriculture, and other aspects);

2)      Involvement of any single or combination of fine resolution sensors such as UAV, lidar, high spatial resolution satellite imagery, and hyperspectral sensors;

3)      Incorporation of spectral and/or structural information using new sensors (such as multispectral lidar) and/or the combination of several sensors;

4)      Development of novel techniques in pre-processing (e.g., image georeferencing and orthorectification) and/or image processing (such as segmentation) to improve the classification accuracy;

5)      Development or application of new statistical techniques (such as deep learning) for classification, especially in comparison to other parametric and non-parametric methods;

6)      Development of upscaling framework that can map species over large spatial extent;

7)      Other novel research not listed above on species mapping with fine resolution sensors.

Important Dates

Full paper submission deadline:  November 1st, 2017
Online publication:  Immediately after acceptance and final technical review for compliance with the journal style.

Expected print publication date: July 2018

Editorial information

·         Guest Editor: Qi ChenDepartment of Geography, University of Hawaii at Manoa, USA (qichen@hawaii.edu)

·         Guest Editor: Tiit Kutser Estonian Marine Institute, University of Tartu, Estonia (tiit.kutser@sea.ee)

·         Guest Editor: Antoine Collin, Ecole Pratique des Hautes Etudes, PSL Research University, France (antoine.collin@ephe.sorbonne.fr)

·         Guest Editor: Timothy A. WarnerDepartment of Geology and Geography, West Virginia University, USA (tim.warner@mail.wvu.edu)

Research Presentation at the AGU Fall Meeting 2016

I will present research on drought-induced variability of sagebrushecosystem productivity in the Upper Green River Basin at the AGU Fall Meeting in San Francisco, CA at 10:35 am in Moscone West Rm 2008. My presentation is part of the session entitled: (B32C) Remote Sensing to Support Investigations in Plant-Climate Interactions.

Frequency distribution of net productivity across the Upper Green River Basin. Comparison between an average precipitation year (2010; blue), an wet year (2011; red), and a dry year (2012; green).

A cross-scale approach to understand drought-induced variability of sagebrush ecosystem productivity (B32C-02)

Abstract

Sagebrush (Artemisia spp.) mortality has recently been reported in the Upper Green River Basin (Wyoming, USA) of the sagebrush steppe of western North America. Numerous causes have been suggested, but recent drought (2012-13) is the likely mechanism of mortality in this water-limited ecosystem which provides critical habitat for many species of wildlife.

Sagebrush mortality has been reported across the Upper Green River Basin in recent years.

An understanding of the variability in patterns of productivity with respect to climate is essential to exploit landscape scale remote sensing for detection of subtle changes associated with mortality in this sparse, uniformly vegetated ecosystem. We used the standardized precipitation index to characterize drought conditions and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery (250-m resolution) to characterize broad characteristics of growing season productivity. We calculated per-pixel growing season anomalies over a 16-year period (2000-2015) to identify the spatial and temporal variability in productivity. Metrics derived from Landsat satellite imagery (30-m resolution) were used to further investigate trends within anomalous areas at local scales. We found evidence to support an initial hypothesis that antecedent winter drought was most important in explaining reduced productivity. The results indicate drought effects were inconsistent over space and time. MODIS derived productivity deviated by more than four standard deviations in heavily impacted areas, but was well within the interannual variability in other areas. Growing season anomalies highlighted dramatic declines in productivity during the 2012 and 2013 growing seasons. However, large negative anomalies persisted in other areas during the 2014 growing season, indicating lag effects of drought. We are further investigating if the reduction in productivity is mediated by local biophysical properties. Our analysis identified spatially explicit patterns of ecosystem properties altered by severe drought which are consistent with field observations of sagebrush mortality. The results provide a theoretical framework for future field based investigation at multiple spatiotemporal scales.

Using ‘open data’ to monitor drought resistance in California ecosystems

We recently published a paper in the open access journal Ecosphere, entitled “Drought resistance across California ecosystems: evaluating changes in carbon dynamics using satellite imagery.” The paper is the product of a collaboration cultivated from  Open Science for Synthesis (OSS) training hosted by UC Santa Barbara’s National Center for Ecological Analysis and Synthesis (NCEAS) in 2014. Our scientific synthesis group (Hampton and Parker 2011) chose this topic in part because we were able to utilize readily available data (i.e. open data (Reichman, Jones and Schildhauer 2011)) to address a timely ecological problem and draw upon our diverse backgrounds.  More information on that experience can be found here.

Source: US Drought Monitor (droughtmonitor.unl.edu/)
The US Drought Monitor indicated the majority of the state of California was classified as extreme or exceptional drought during the summer of 2014 (source: droughtmonitor.unl.edu/)

Prompted by the recent drought, we sought to understand differences in drought sensitivity across California ecosystems. Our analysis, spearheaded by my colleague Sparkle Malone, considered deviations in ecosystem functionality (i.e. ecosystem water use efficiency) during drought periods compared to baseline (non-drought) conditions. Water use efficiency makes for a good indicator of ecosystem function because it measures net primary productivity per amount of water lost through evapotranspiration. In other words, the net amount of carbon pushed into an ecosystem compared to the amount of water lost from plants to the atmosphere. We also chose this metric because the input data sets (net primary productivity, evapotranspiration and leaf area index) can be derived from freely available MODIS satellite data (also known as open data). The continuous dataset is an alternative to single point flux tower measurements and allows for analysis of ecosystem function (e.g. ecosystem water use efficiency) at broad scales.

Our results indicate ecosystem resistance during the recent drought is not uniform across large areas (Malone et al. 2016). For example, resistance was lower in high productivity areas of the state (typically found in northern California) compared to more arid areas that are adapted to limited water resources. Climate change projections indicate extreme events, such as drought, will become more common in the future. Therefore, it is important to evaluate how different ecosystems respond to these events so we can gain a better understanding of how climate change may alter ecosystem structure and function in the future.

As of late 2016, drought still has a grip on the southern part of the state.
As of late 2016, drought still has a grip on the southern part of the state. A much more conspicuous indicator of drought than ecosystem water use efficiency, water storage capacity has been greatly reduced throughout much of the state, including Santa Barbara’s water supply shown above (source: NASA Earth Observatory).

Literature Cited

  • Hampton, S. E. & Parker, J. N. Collaboration and Productivity in Scientific Synthesis. 2011. Bioscience 61, 900–910.
  • Malone, S. L., M. G. Tulbure, A. J. Pérez-Luque, T. J. Assal, L. L. Bremer, D. P. Drucker, V. Hillis, S. Varela, and M. L. Goulden. 2016. Drought resistance across California ecosystems: evaluating changes in carbon dynamics using satellite imagery. Ecosphere 7(11): e01561. 10.1002/ecs2.1561
  • Reichman, O.J., M.B. Jones, and M.P. Schildhauer. Challenges and Opportunities of Open Data in Ecology. 2011. Science. 331, 703-705

research in spatial ecology