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Vegetation Cover Changes from Satellite Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 10724

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Dr. Vladimir Valerievich Elsakov

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Guest Editor

Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences (IB FRC Komi SC UB RAS), Syktyvkar, Russia
Interests: vegetation cover dynamics; climate change; tundra vegetation; climate and anthropogenic effects; analysis of optical remote sensing data

Special Issue Information

Dear Colleagues,

Vegetation cover is the most important indicator of ecosystem transformations and landscape history on local, regional, or global scales. The vegetation is one of the easily readable objects for optical remote sensing, as an active pigment keeper. The water content and density combinations of plant shoots and tree crowns open the possibility for SAR instruments analysis. The high-accuracy measurements and time-series stability of RS data are the basis for statistically correct estimations of transformations of vegetation cover. It is more important for hard-to-reach regions in Arctic, taiga, or arid zones, and in monitoring low-intensity changes. The species composition dynamics and plant biomass production are visual processes of ecosystem changes. Past and new disasters of vegetation cover (windfalls, fires, pasture grazing) affect biodiversity.

This Special Issue will accumulate original research on Vegetation Cover Changes at different levels of organization: from local communities to transition landscape zones of biomes. Careful research carried out at the local level can serve as a basis for other researchers to compare and validate their global conclusions. New algorithms for processing and measurement sensors for vegetation cover changes are also welcomed.

Dr. Vladimir Valerievich Elsakov
Guest Editor

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Keywords

Vegetation dynamics as an indicator of global ecosystem transformations
Climate effects in transition vegetation zones
Ecological gradients in vegetation cover formation
Anthropogenic and native disturbances of vegetation cover
New algorithms and sensors for vegetation cover analysis
Vegetation cover mapping

Published Papers (3 papers)

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26 pages, 10145 KiB

Open AccessArticle

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

by Dmitry V. Ershov, Egor A. Gavrilyuk, Natalia V. Koroleva, Elena I. Belova, Elena V. Tikhonova, Olga V. Shopina, Anastasia V. Titovets and Gleb N. Tikhonov

Remote Sens. 2022, 14(2), 322; https://doi.org/10.3390/rs14020322 - 11 Jan 2022

Cited by 9 | Viewed by 3053

Abstract

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels. Full article

(This article belongs to the Special Issue Vegetation Cover Changes from Satellite Data)

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24 pages, 4252 KiB

Open AccessArticle

Modelling of Vegetation Dynamics from Satellite Time Series to Determine Proglacial Primary Succession in the Course of Global Warming—A Case Study in the Upper Martell Valley (Eastern Italian Alps)

by Bettina Knoflach, Katharina Ramskogler, Matthew Talluto, Florentin Hofmeister, Florian Haas, Tobias Heckmann, Madlene Pfeiffer, Livia Piermattei, Camillo Ressl, Michael H. Wimmer, Clemens Geitner, Brigitta Erschbamer and Johann Stötter

Remote Sens. 2021, 13(21), 4450; https://doi.org/10.3390/rs13214450 - 5 Nov 2021

Cited by 10 | Viewed by 3556

Abstract

Satellite-based long-term observations of vegetation cover development in combination with recent in-situ observations provide a basis to better understand the spatio-temporal changes of vegetation patterns, their sensitivity to climate drivers and thus climatic impact on proglacial landscape development. In this study we combined field investigations in the glacier forelands of Fürkele-, Zufall- and Langenferner (Ortles-Cevedale group/Eastern Italian Alps) with four different Vegetation Indices (VI) from Landsat scenes in order to test the suitability for modelling an area-wide vegetation cover map by using a Bayesian beta regression model (RStan). Since the model with the Normalized Difference Vegetation Index (NDVI) as predictor showed the best results, it was used to calculate a vegetation cover time series (1986–2019). The alteration of the proglacial areas since the end of the Little Ice Age (LIA) was analyzed from digital elevation models based on Airborne Laser Scanning (ALS) data and areal images, orthophotos, historical maps and field mapping campaigns. Our results show that a massive glacier retreat with an area loss of 8.1 km² (56.9%; LIA–2019) resulted in a constant enlargement of the glacier forelands, which has a statistically significant impact on the degree of vegetation cover. The area covered by vegetation increased from 0.25 km² (5.6%) in 1986 to 0.90 km² (11.2%) in 2019 with a significant acceleration of the mean annual changing rate. As patterns of both densification processes and plant colonization at higher elevations can be reflected by the model results, we consider in-situ observations combined with NDVI time series to be powerful tools for monitoring vegetation cover changes in alpine proglacial areas. Full article

(This article belongs to the Special Issue Vegetation Cover Changes from Satellite Data)

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15 pages, 5597 KiB

Open AccessArticle

Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

by Risu Na, Li Na, Haibo Du, Hong S. He, Yin Shan, Shengwei Zong, Lirong Huang, Yue Yang and Zhengfang Wu

Remote Sens. 2021, 13(20), 4066; https://doi.org/10.3390/rs13204066 - 12 Oct 2021

Cited by 17 | Viewed by 2342

Abstract

Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions. Full article

(This article belongs to the Special Issue Vegetation Cover Changes from Satellite Data)

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