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Dr. Keely L. Roth

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Guest Editor

Data Science, The Climate Corporation, San Francisco, CA, USA
Interests: hyperspectral; vegetation mapping; agriculture; plant ecology; functional traits

Dr. Erin Wetherley

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Guest Editor

Geography Department, University of California, Santa Barbara, CA, USA
Interests: plant ecology; urban systems; data fusion; climate modeling; hyperspectral

Special Issue Information

Dear Colleagues,

Vegetation is a critical barometer of ecological change, and making maps of plant species and functional types is valuable for monitoring landscapes, tracking climate change impacts, and understanding the effects of land disturbance or management. Increasing availability of imaging spectroscopy data, with its richness in spectral information, can be used to measure and map plant biophysical, phenological, and structural traits. This creates an opportunity for developing new techniques and applications to deliver on critical monitoring needs.

With this Special Issue, we shall collect state-of-the-art research that investigates using imaging spectroscopy to monitor plant species and functional types, with a particular emphasis on developing new techniques, examining cross-ecosystem applications, and exploring new dimensions of plant species and functional type monitoring.

Within these areas of emphasis, we invite authors to submit papers on a range of topics, including but not limited to species detection, classification, and derivation of functional traits, time-series analysis, comparative cross-biome studies, high-value monitoring applications (e.g., wildfire risk, agriculture, urban ecosystems), multiplatform or multiresolution investigations, and assimilation of imaging spectroscopy data for modeling of ecosystems.

Dr. Keely L. Roth
Dr. Erin Wetherley
Guest Editors

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Keywords

Vegetation monitoring and mapping
Terrestrial ecosystems
UAV, airborne, and spaceborne imaging spectroscopy
Plant species and functional types
Ecosystem modeling
Hyperspectral

Published Papers (2 papers)

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Research

21 pages, 3494 KiB

Open AccessArticle

Changes in the Greenness of Mountain Pine (Pinus mugo Turra) in the Subalpine Zone Related to the Winter Climate

by Veronika Lukasová, Tomáš Bucha, Ľubica Mareková, Anna Buchholcerová and Svetlana Bičárová

Remote Sens. 2021, 13(9), 1788; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091788 - 04 May 2021

Cited by 5 | Viewed by 1679

Abstract

In the current alteration of temperature and snow cover regimes, the impacts of winter climate have received considerably less attention than those of the vegetation period. In this study, we present the results demonstrating the influence of the winter climate conditions on the Mountain pine (Pinus mugo Turra) communities in High Tatra Mts (Western Carpathians). The changes in greenness in 2000–2020 were represented by the inter-annual differences of satellite-derived Normalized Difference Vegetation Index (NDVI). The winter climate conditions were characterized by climate indices calculated from the temperature and snow cover data measured at Skalnaté Pleso Observatory (1778 m a.s.l.) over the period between 1941–2020. Areas with P. mugo were classified into two density classes and five altitudinal zones of occurrence. The partial correlation analyses, which controlled the influence of summer climate, indicated that winter warm spells (WWS) caused a significant decrease in the greenness of the P. mugo thickets growing in the dense class D2 (R = −0.47) and in the altitudinal zones A2 (1600–1700 m a.s.l.) and A3 (1700–1800 m a.s.l.) with R = −0.54 for each zone. The changes in greenness were related to the average snow depth (ASD) as well, particularly in the dense class D2 (R = 0.45) and in the altitudinal zone A2 (R = 0.50). Here, in the summers following winters with the incidence of WWS or low ASD, we found decreased greenness following the injury of P. mugo shrubs, but NDVI after winters with higher ASD indicated more greenness. At lower altitudes, injuries may result in the loss of competition capacity of P. mugo near the timberline, where taller mountain tree species can utilize the conditions of warmer climate for expansion. We also found a significant positive effect of warmer winter seasons in the sparse P. mugo thickets (D1) with R = 0.50 and at higher altitudes (R = 0.49 in A4—1800–1900 m a.s.l.; R = 0.53 in A5—1900–2000 m a.s.l.). The increased temperatures in December correlated significantly with the increase of the greenness in all P. mugo pixels (R = 0.47), with the most pronounced effect in the sparse class D1 (R = 0.57) and in altitudinal zones A4 (R = 0.63) and A5 (R = 0.44), creating advantageous conditions for the thermophilisation of the alpine zone by P. mugo. Full article

(This article belongs to the Special Issue Plant Species and Functional Types Monitoring with Imaging Spectroscopy)

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19 pages, 18259 KiB

Open AccessArticle

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

by Andreas Forstmaier, Ankit Shekhar and Jia Chen

Remote Sens. 2020, 12(14), 2176; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142176 - 08 Jul 2020

Cited by 28 | Viewed by 5727

Abstract

Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas. Full article

(This article belongs to the Special Issue Plant Species and Functional Types Monitoring with Imaging Spectroscopy)

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