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Environmental Stress and Natural Vegetation Growth

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

Deadline for manuscript submissions: closed (30 October 2022) | Viewed by 16183

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Special Issue Editors

Prof. Dr. Jarosław Chormański

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Guest Editor

Department of Remote Sensing and Environmental Assessment; Institute of Environmental Engineering, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland
Interests: urban and eco-hydrology; ecosystem services; imaging spectroscopy; remote sensing, UAS
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tim Van de Voorde

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Guest Editor

Department of Geography, Ghent University, Krijgslaan 281 S8, 9000 Gent, Belgium
Interests: urban remote sensing, urban green and ecosystem services, deep learning in remote sensing, LULC change, monitoring cultural heritage with remote sensing

Special Issue Information

Dear Colleagues,

Vegetation in optimal conditions provides many ecosystem services in natural, agricultural, as well as urban environments. As vegetation condition depends on a broad range of environmental factors, innovative and robust tools are crucial for generating the data flows needed to produce essential variables and state indicators required by monitoring approaches that aim to inform policy makers and ensure environmental protection and improvement. Notwithstanding the importance of in situ data to monitor ecosystem functioning and biodiversity change, remote sensing provides the opportunity to assess vegetation conditions at different scales (form local to global) more efficiently than traditional field surveys. Pressures and impact on vegetation or ecosystems in general, however, depend on vegetation type, climate zone, and human activities. Hence, tools for assessing stress factors require customized development or parametrization.

This proposed Special Issue addresses research on assessing vegetation stress and/or its impact on ecosystem service provisioning using different remote sensing techniques. We welcome original contributions on vegetation conditions and pressure in all kinds of biomes, in natural as well as in environments strongly influenced by humans (e.g., urban and agricultural areas) relying on all possible types of remotely sensed data: local applications with UAV or airborne flights as well as regional or global studies using active or passive satellite sensors. Research on environmental indicators or essential variables using widely known remote sensing indices (e.g., crop water stress index—CWSI, normalized difference vegetation index—NDVI) as well as newly created ones is welcome in this Special Issue.

Prof. Jarosław Chormański
Prof. Dr. Tim Van de Voorde
Guest Editors

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Keywords

Environmental stress monitoring and modeling
Ecosystem services
Essential variables
Environmental indicators
Thermal indices
Optical indices

Published Papers (7 papers)

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Research

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19 pages, 2829 KiB

Open AccessArticle

Quantifying the Interaction Effects of Climatic Factors on Vegetation Growth in Southwest China

by Meng Wang and Zhengfeng An

Remote Sens. 2023, 15(3), 774; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030774 - 29 Jan 2023

Cited by 1 | Viewed by 1397

Abstract

Due to the complex and variable climate structure in Southwest China (SW), the impacts of climate variables on vegetation change and the interactions between climate factors remain controversial, considering the uncertainty and complexity in the relationships between climate factors and vegetation in this region. In this study, the CRU TS v. 4.02 from 1982 to 2017 and the annual maximum (P₁₀₀), upper quarter quantile (P₇₅), median (P₅₀), lower quarter quantile (P₂₅), minimum (P₅), and mean (Mean) of GIMMS NDVI were utilized to reveal the main and interaction effects of significant climate variables on vegetation development at the level of SW and the core areas (CAs) of typical climate type (including T^{+ *}–P^{+ *}, T^{+ *}–P^–, T^{+ *}–P⁺, and NSC) using the simple moving average method, a multivariate linear model, the slope method, and the Johnson–Neyman method. The obtained regression relationships between NDVI, temperature, and precipitation were verified successfully by constructing multiple linear models with interaction terms. Within the T^{+ *}–P^– CA, precipitation had the main impact; meanwhile, in the SW and other CAs, the temperature had the main effect. In general, most of the significant moderating effects of temperature (precipitation) on vegetation growth predominantly increased with the increase in precipitation (temperature). Nevertheless, the significant moderating effect varied in different regions and directions. In the SW area, when the temperature/precipitation was in the range of [4.73 °C, 5.13 °C]/[730.00 mm, 753.95 mm], the impact of temperature/precipitation on NDVI had a significant positive regulating effect with respect to the precipitation/temperature. Meanwhile, in the NSC/T^{+ *}–P^{+ *} areas, when the temperature/precipitation was in the range of [15.99 °C, 16.03 °C]/[725.17 mm, 752.82 mm], the impact of temperature/precipitation on NDVI has a significant negative moderating role with respect to the precipitation/temperature. Overall, our study provides a modern context for clearly uncovering the complexity of the effect of climate alteration on vegetation development, allowing for clarification of the alterations in vegetation development due to climate change. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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17 pages, 10699 KiB

Open AccessArticle

Evaluating Satellite-Observed Ecosystem Function Changes and the Interaction with Drought in Songnen Plain, Northeast China

by Haiyan Li, Fang Huang, Xiuchao Hong and Ping Wang

Remote Sens. 2022, 14(22), 5887; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225887 - 20 Nov 2022

Cited by 4 | Viewed by 1384

Abstract

Drought is considered one of the devastating natural disasters worldwide. In the context of global climate change, the frequency and intensity of drought have increased, thereby affecting terrestrial ecosystems. To date, the interactions between ecosystem change and drought, especially their mutual lag and cumulative effects is unclear. The Songnen Plain in northeastern China is one of the three major black soil areas in the world and is highly sensitive to global change. Herein, to quantify the interaction between drought and ecosystem function changes in the Songnen Plain, integrating with time-series moderate resolution imaging spectroradiometer (MODIS), leaf area Index (LAI), evapotranspiration (ET), and gross primary productivity (GPP) data, we calculated the standardized precipitation and evapotranspiration index (SPEI) based on the meteorological data, diagnosed the causal relationship between SPEI and the ecosystem function indicators i.e., LAI, ET, and GPP, and analyzed the time-lag and cumulative effects between the degree of drought and three ecosystem function indicators using impulse response analysis. The results showed that the trend of SPEI (2000–2020) was positive in the Songnen Plain, indicating that the drought extent had eased towards wetness. LAI showed insignificant changes (taking up 88.34% of the total area), except for the decrease in LAI found in some forestland and grassland, accounting for 9.43%. The pixels showing a positive trend of ET and GPP occupied 24.86% and 54.94%, respectively. The numbers of pixels with Granger causality between LAI and SPEI (32.31%), SPEI and GPP (52.8%) were greater at the significance 0.05 level. Impulse responses between each variable pair were stronger mainly between the 6th and 8th months, but differed significantly between vegetation types. Grassland and cropland were more susceptible to drought than forest. The cumulative impulse response coefficients values indicated that the mutual impacts between all variables were mainly positive. The increased wetness positively contributed to ecosystem function, and in turn enhanced ecosystem function improved regional drought conditions to some extent. However, in the northeastern forest areas, the SPEI showed a significant negative response to increased ET and GPP, suggesting that the improved physiological functions of forest might lead to regional drought. There were regional differences in the interaction between drought conditions and ecosystem function in the Songnen Plain over the past 21 years. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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21 pages, 5892 KiB

Open AccessArticle

Multiscale Detection and Assessment of Vegetation Eco-Environmental Restoration following Ecological Water Compensation in the Lower Reaches of the Tarim River, China

by Changming Zhu, Qian Shen, Kun Zhang, Xin Zhang and Junli Li

Remote Sens. 2022, 14(22), 5855; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225855 - 18 Nov 2022

Cited by 1 | Viewed by 1262

Abstract

To protect and restore this downstream ecosystem, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Compensation (EWC) project from 2000 to 2018. Revealing the mechanism of vegetation-hydroecological response processes in the lower reaches of the Tarim River before and after EWC work is conducive to water resource planning, utilization and protection. In this paper, the spatiotemporal responses of vegetation and groundwater to EWC were examined at the points, lines, and area (PLA) scale by coupling remote sensing techniques and field station observation data collected between 2000 and 2017. The findings indicated that (1) In general, the regional fractional vegetation coverage (FVC) increased significantly, and the average FVC growth rate was 3.5%/year from 2000 to 2017 (R² > 0.84, p < 0.01, 2-tailed). (2) The regional vegetation restoration process showed obvious fluctuations and stage characteristics, but the spatial scope of the significantly increased vegetation area was limited. Plants grew rapidly within 10 km of the river, while 10 km away from the water channel, no obvious change was observed. (3) Strong coupling relationships were identified among the FVC growth, EWC volume and groundwater depth variations (p < 0.05, 2-tailed). The response times of the regional vegetation and groundwater depth to EWC indicated one-year lags. The above results imply that the regional ecological environment was significantly improved over the study period, thus confirming that the EWC project made remarkable accomplishments. However, the effect of ecological restoration is not sufficiently stable at present. Vegetation restoration has mainly been centralized around the river channel and is greatly dependent on the annual EWC volume. In addition, the local conditions begin to degrade soon after an EWC project is terminated, and vice versa; when EWC commences, the FVC immediately begins to improve. Therefore, the current EWC achievements need to be further consolidated and strengthened in the future. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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15 pages, 6845 KiB

Open AccessArticle

Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)

by Hong Jiang, Maolin Yao, Jia Guo, Zhaoming Zhang, Wenting Wu and Zhengyuan Mao

Remote Sens. 2022, 14(4), 882; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040882 - 12 Feb 2022

Cited by 4 | Viewed by 2750

Abstract

It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of −0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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10 pages, 3252 KiB

Open AccessCommunication

Detection of Ground Materials Using Normalized Difference Indices with a Threshold: Risk and Ways to Improve

by Fen Chen, Tim Van de Voorde, Dar Roberts, Haojie Zhao and Jingbo Chen

Remote Sens. 2021, 13(3), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030450 - 28 Jan 2021

Cited by 7 | Viewed by 2083

Abstract

In this short communication, we describe the shortcomings and pitfalls of a commonly used method to detect ground materials that relies on setting thresholds for normalized difference indices. We analyze this method critically and present some experimental results on the USGS and ECOSTRESS spectral libraries and on real Sentinel-2 and Landsat-8 images. We demonstrate the risk of commission errors and provide some suggestions to reduce it. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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21 pages, 3787 KiB

Open AccessArticle

Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

by Wojciech Ciężkowski, Sylwia Szporak-Wasilewska, Małgorzata Kleniewska, Jacek Jóźwiak, Tomasz Gnatowski, Piotr Dąbrowski, Maciej Góraj, Jan Szatyłowicz, Stefan Ignar and Jarosław Chormański

Remote Sens. 2020, 12(4), 631; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040631 - 14 Feb 2020

Cited by 22 | Viewed by 3348

Abstract

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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Other

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12 pages, 3694 KiB

Open AccessTechnical Note

npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability

by Roberto O. Chávez, Sergio A. Estay, José A. Lastra, Carlos G. Riquelme, Matías Olea, Javiera Aguayo and Mathieu Decuyper

Remote Sens. 2023, 15(1), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010073 - 23 Dec 2022

Cited by 2 | Viewed by 2780

Abstract

Monitoring vegetation disturbances using long remote sensing time series is crucial to support environmental management, biodiversity conservation, and adaptation strategies to climate change from global to local scales. However, it is difficult to assess whether a remotely detected vegetation disturbance is critical or not, since available operational remote sensing methods deliver only maps of the vegetation anomalies but not maps of how “uncommon” or “extreme” the detected anomalies are based on the available records of the reference period. In this technical note, we present a new release of the probabilistic method and its implementation, the npphen R package, designed to detect not only vegetation anomalies from remotely sensed vegetation indices, but also to quantify the position of the anomalous observations within the historical frequency distribution of the phenological annual records. This version of the R package includes two new key functions to detect and map extreme vegetation anomalies: ExtremeAnom and ExtremeAnoMap. The npphen package allows remote sensing users to detect vegetation changes for a wide range of ecosystems, taking advantage of the flexibility of kernel density estimations to account for any shape of annual phenology and its variability through time. It provides a uniform statistical framework to study all types of vegetation dynamics, contributing to global monitoring efforts such as the GEO-BON Essential Biodiversity Variables. Full article

(This article belongs to the Special Issue Environmental Stress and Natural Vegetation Growth)

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