Special Issue "Ecophysiological Remote Sensing"
Deadline for manuscript submissions: 30 September 2017
Dr. John S. Kimball
Numerical Terradynamic Simulation Group, College of Forestry & Conservation, The University of Montana, Missoula, United States
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Interests: ecological remote sensing, water and carbon cycle interactions, vegetation phenology, boreal and Arctic ecosystems, remote sensing retrieval algorithms and modeling
Dr. Kaiyu Guan
Department of Natural Resources and Environmental Sciences, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign Urbana, Illinois, United States
Website | E-Mail
Interests: ecohydrology, crop modeling and forecasting, agriculture adaptation to climate change, vegetation dynamic modeling, terrestrial remote sensing (optical/microwave)
Recent developments in satellite remote sensing include new sensors and/or complimentary observations from different sensors that are providing new insight and capabilities for understanding vegetation and ecosystem properties, dynamics and functional processes. Next generation missions and sensors are now underway or scheduled for near-term operations that may provide new capabilities for better understanding and monitoring of vegetation ecophysiology, including photosynthesis and respiration, canopy phenology, structure, water use, and environmental stress behaviour. Global observations from continuing or similar overlapping satellite missions now span multiple decades and also multiple spectral ranges (visible, near-infrared, thermal, microwave, etc.), enabling more precise documentation and new understanding of vegetation changes and their environmental controls.
In this Special issue on “Ecophysiological Remote Sensing”, we invite papers involving one or more of the following topical areas, emphasizing satellite remote sensing of properties and processes pertaining to terrestrial ecosystems and vegetation; investigations using multi-scale satellite, airborne and ground based observations are also encouraged:
- New technological developments, including next generation sensors and missions.
- Fusion of multi-sensor observations, including satellite, and airborne and terrestrial sources.
- Development and analysis of long time series satellite environmental data records for analysing climate related trends, and anomalies.
- New application studies, including vegetation phenology and stress, drought detection and monitoring, agriculture, rangeland, and forestry.
Dr. John S. Kimball
Dr. Kaiyu Guan
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Water stress
- Canopy structure
- Plant traits
- Crop yield
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
In-situ and remote sensing platforms for monitoring prescribed fire effects on fine fuel and vegetation cover in Sonoran semidesert grasslands
Steven E. Sesnie1,3, Holly Eagleston1, Lacrecia Johnson1, and Emily Yurcich2
1US Fish and Wildlife Service, Division of Biological Sciences Albuquerque, NM
2Lab of Landscape Ecology and Conservation Biology, Northern Arizona University, Flagstaff AZ
3Corresponding author email: firstname.lastname@example.org
Prior to Euro-American settlement, fire played a vital role in shaping semidesert plant composition and structure in the southwestern United States. Currently, most semidesert grasslands have undergone significant soil, hydrology, and vegetation alterations from intensive livestock grazing, non-native plant invasion, drought, and near fire exclusion. US Fish and Wildlife Service land management approaches aim to recover historical disturbance regimes and native plant assemblages necessary for maintaining wildlife habitat and other ecosystem values. These activities can benefit from up-to-date fuels and vegetation data for monitoring disturbance impacts and natural resource decisions. The variety of continuously updated satellite remote sensing systems and sensors provide new opportunities for characterizing management outcomes over large landscapes. For this study, we compared photosynthetically active radiation (PAR) ceptometer and leaf area index (LAI) measurements to conventional means for estimating fine-fuel biomass on 20, 50m x 20m plots and 239, 0.5m x 0.5m quadrats on the Buenos Aires National Wildlife Refuge (BANWR) in southern Arizona. Ceptometer LAI explained 75% of the variance in fine fuel biomass using simple linear regression. An additional 8% to 10% of variance was explained from Random Forest regression tree models that included plant height and cover as predictors. Field biomass and vegetation measurements were used to map fine-fuel load and vegetation cover from plots (n = 446) on BANWR comparing outcomes from multi-date Worldview-3 (WV3) and Operational Land Imager (OLI) imagery. Fine-fuel biomass predicted from multi-date WV3 or OLI imagery explained similar variance using regression tree models (54%). Land cover classification from for 11 categories with high spatial resolution WV3 imagery showed 80% overall accuracy and highlighted areas dominated by non-native grasses with 89% class accuracy. Mixed native and non-native grass and shrublands showed 62% accuracy and rare areas dominated by native grasses that were poorly represented on plots showed low class accuracy (20%). A 30-year record of prescribed fire frequency revealed that greater fire frequency since refuge establishment significantly increased non-native grass cover and fuel loads. We recommend that more consistent fire monitoring be used to assess near- and long-term outcomes, and fire be used in conjunction with activities that can help promote desired native plant composition and structure.
Keywords: biomass, disturbance, vegetation