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Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems

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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 (20 August 2023) | Viewed by 18888

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Dr. Chung-Te Chang

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Taiwan International Graduate Program (TIGP), Ph.D. Program on Biodiversity, Tunghai University, Taichung, Taiwan
Interests: geoinformatics; land surface phenology; long-term ecological study; biogeochemistry
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junhu Dai

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Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: plant and vegetation phenology; vegetation geography; global change and phenology; global change and plant geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vegetation phenology plays an important role in regulating the water cycle, carbon cycle, productivity, etc., which is largely related to region-specific climatic and non-climatic factors. In the context of the warming climate, the dynamics of local regular climate and large-scale climatic variations, such as El Niño-Southern Oscillation (ENSO), are expected to become more dramatic and subsequently may have substantial effects on vegetation phenology. In addition, climatic extremes such as storms, tropical cyclones, and sporadic events, as well as anthropogenic activities, have abruptly altered the development of vegetative from regional to global scales. With the assistance of long-term in situ observations, PhenoCam monitoring networks, and multisource remotely-sensed datasets, the variations in vegetation phenology and its associations with regular climate, climatic fluctuations, or extremes can be potentially captured and disentangled.

For this Special Issue, we invite scientists applying remote sensing and spatial technology to explore the variations of vegetation phenology in relation to climate. For example, the combination of field observations with remote sensing techniques across scales, relationships between satellite-derived phenology (land surface phenology; LSP), and climate, including regional climate conditions and large-scale atmospheric anomalies, are suitable issues. Studies on the effects of phenological variations in landscape on hydrological processes, water resources, and biogeochemical cycles are also great contributions in this field. The alterations of LSP along land-cover gradient and projections of phenology across scales are all welcome.

Related topics may include, but are not limited to, the following:

The combination of in situ plant phenological observation and remotely-sensed data across scales.
Near-surface remote sensing, PhenoCam, and data analysis in relation to climate and disturbances.
LSP across various climate regions, vegetation types, landscapes, and their controls.
LSP along rural-to-urban gradient.
Variations in LSP on evapotranspiration, storage, runoff, sediments, or nutrients in watershed or large scales.
LSP projections.

Dr. Chung-Te Chang
Prof. Dr. Junhu Dai
Guest Editors

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Keywords

vegetation phenology
regular climate
climatic fluctuation
disturbance
phenocam
multisource remotely sensed data
time-series
water resources
productivity
biogeochemical cycles

Published Papers (11 papers)

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Editorial

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7 pages, 4023 KiB

Open AccessEditorial

Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems

by Chung-Te Chang, Jyh-Min Chiang and Junhu Dai

Remote Sens. 2023, 15(21), 5097; https://doi.org/10.3390/rs15215097 - 25 Oct 2023

Viewed by 1026

Abstract

Vegetation phenology, i [...] Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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Research

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25 pages, 8268 KiB

Open AccessArticle

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

by Nela Jantol, Egor Prikaziuk, Marco Celesti, Itza Hernandez-Sequeira, Enrico Tomelleri, Javier Pacheco-Labrador, Shari Van Wittenberghe, Filiberto Pla, Subhajit Bandopadhyay, Gerbrand Koren, Bastian Siegmann, Tarzan Legović, Hrvoje Kutnjak and M. Pilar Cendrero-Mateo

Remote Sens. 2023, 15(19), 4835; https://doi.org/10.3390/rs15194835 - 05 Oct 2023

Viewed by 1534

Abstract

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF₇₆₀ and SIF₆₈₇, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R² > 0.9 for all predictors with respect to SIF₇₆₀ and SIF₆₈₇). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF₇₆₀ in all applied methods (R² = 0.76 with the standard deviation method; R² = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF₆₈₇ did not perform as well as those for SIF₇₆₀, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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22 pages, 16322 KiB

Open AccessArticle

Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging

by Xiaole Liu, Guangjun Wang, Yu Shi, Sihai Liang and Jinzhang Jia

Remote Sens. 2023, 15(12), 3174; https://doi.org/10.3390/rs15123174 - 18 Jun 2023

Cited by 1 | Viewed by 1173

Abstract

Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km² in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata’s spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km² to 9.02 km² between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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18 pages, 2730 KiB

Open AccessArticle

Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics

by Roger A. Rojas Vásquez, Muditha K. Heenkenda, Reg Nelson and Laura Segura Serrano

Remote Sens. 2023, 15(11), 2888; https://doi.org/10.3390/rs15112888 - 01 Jun 2023

Cited by 1 | Viewed by 1730

Abstract

Remote sensing Vegetation Indices (VIs) are simple, effective, and widely used methods for quantitative and qualitative analysis of vegetation cover, vigor, and growth dynamics. This study developed and assessed a new vegetation index (VI) using Cyan, Orange, and Near Infrared (NIR) bands to assess Soybean growth dynamics. The study was conducted at Lakehead University Agriculture Research Station, Thunder Bay, Canada, over four reproductive stages of Soybean growth (R4–R7). Spectral profiles were created for each stage, and the correlation between each spectral band at different stages was tested. There was no linear correlation between different bands except the correlation between the Cyan and Orange bands at R5 and R6 stages. Existing VIs have also been explored using approximately similar band combinations. Based on this analysis, three VIs were proposed for this new camera, and their behavior at different stages was evaluated using Leaf Area Index (LAI). Cyan and Orange spectral values were relatively high in the first and last growing seasons, while NIR values increased dramatically in the mid-growing seasons and decreased in the last stage. VI_NIR,O,C index showed the best results for mid-growing seasons (correlation with LAI = 0.39 for R5 and R6). VI_C,_O index showed a high level of details visually (leaves and background) for R4 and R7 than the other indices and correlated highly with LAI (0.48 and −0.5, respectively). Overall, the study provided new VIs that can be used to effectively analyze Soybean growth dynamics, with different VIs showing reliability over different growing stages. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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20 pages, 3747 KiB

Open AccessArticle

Vegetation Dynamic in a Large Floodplain Wetland: The Effects of Hydroclimatic Regime

by Lei Jing, Qing Zeng, Ke He, Peizhong Liu, Rong Fan, Weizhi Lu, Guangchun Lei, Cai Lu and Li Wen

Remote Sens. 2023, 15(10), 2614; https://doi.org/10.3390/rs15102614 - 17 May 2023

Cited by 5 | Viewed by 1261

Abstract

Floodplain wetlands are among the most dynamic ecosystems on Earth, featuring high biodiversity and productivity. They are also sensitive to anthropogenic disturbances and are globally threatened. Understanding how flow regime drives the spatiotemporal dynamics of wetland habitats is fundamental to effective conservation practices. In this study, using Landsat imagery and the random forest (RF) machine learning algorithm, we mapped the winter distribution of four wetland habitats (i.e., Carex meadow, reedbed, mudflat, and shallow water) in East Dongting Lake, a Ramsar wetland in the middle to lower Yangtze Basin of China, for 34 years (1988–2021). The dynamics of wetland habitats were explored through pixel-by-pixel comparisons. Further, the response of wetland habitats to flow regime variations was investigated using generalized additive mixed models (GAMM). Our results demonstrated the constant expansion of reedbeds and shrinkage of mudflats, and that there were three processes contributing to the reduction in mudflat: (1) permanent replacement by reedbed; (2) irreversible loss to water; and (3) transitional swapping with Carex meadow. These changes in the relative extent of wetland habitats may degrade the conservation function of the Ramsar wetland. Moreover, the duration of the dry season and the date of water level withdrawal were identified as the key flow regime parameters shaping the size of wetland habitats. However, different wetland vegetation showed distinct responses to variations in flow regime: while Carex meadow increased with earlier water withdrawal and a longer dry season, reedbed continuously expanded independent of the flow regime corresponding to the increase in winter rainfall. Our findings suggested that flow regime acts in concert with other factors, such as climate change and sand mining in river channels, driving wetland habitat transition in a floodplain landscape. Therefore, effective conservation can only be achieved through diverse restoration strategies addressing all drivers. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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21 pages, 9525 KiB

Open AccessArticle

Comparing Different Spatial Resolutions and Indices for Retrieving Land Surface Phenology for Deciduous Broadleaf Forests

by Kailong Cui, Jilin Yang, Jinwei Dong, Guosong Zhao and Yaoping Cui

Remote Sens. 2023, 15(9), 2266; https://doi.org/10.3390/rs15092266 - 25 Apr 2023

Cited by 2 | Viewed by 1350

Abstract

Deciduous broadleaf forests (DBF) are an extremely widespread vegetation type in the global ecosystem and an indicator of global environmental change; thus, they require accurate phenological monitoring. However, there is still a lack of systematic understanding of the sensitivity of phenological retrievals for DBF in terms of different spatial resolution data and proxy indices. In this study, 79 globally distributed DBF PhenoCam Network sites (total 314 site-years, 2013–2018) were used as the reference data (based on green chromaticity coordinates, GCC). Different spatial resolutions (30 m Landsat and Sentinel-2 data, and 500 m MCD43A4 data) and satellite remote sensing vegetation indices (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI; and near-infrared reflectance of vegetation, NIR_V) were compared to find the most suitable data and indices for DBF phenological retrievals. The results showed that: (1) for different spatial resolutions, both 30 m Landsat–Sentinel-2 data and 500 m MODIS data accurately captured (R² > 0.8) DBF phenological metrics (i.e., the start of the growing season, SOS, and the end of the growing season, EOS), which are associated with the comparatively homogeneous landscape pattern of DBF; (2) for SOS, the NIRv index was closer to GCC than EVI and NDVI, and it showed a slight advantage over EVI and a significant advantage over NDVI. However, for EOS, NDVI performed best, outperforming EVI and NIRv; and (3) for different phenological metrics, the 30 m data showed a significant advantage for detecting SOS relative to the 500 m data, while the 500 m MCD43A4 outperformed the 30 m data for EOS. This was because of the differences between the wavebands used for GCC and for the satellite remote sensing vegetation indices calculations, as well as the different sensitivity of spatial resolution data to bare soil. This study provides a reference for preferred data and indices for broad scale accurate monitoring of DBF phenology. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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19 pages, 16174 KiB

Open AccessArticle

Unveiling Temperature Patterns in Tree Canopies across Diverse Heights and Types

by Riyaaz Uddien Shaik, Sriram Babu Jallu and Katarina Doctor

Remote Sens. 2023, 15(8), 2080; https://doi.org/10.3390/rs15082080 - 14 Apr 2023

Cited by 3 | Viewed by 1611

Abstract

Forests are some of the major ecosystems that help in mitigating the effects of climate change. Understanding the relation between the surface temperatures of different vegetation and trees and their heights is very crucial in understanding events such as wildfires. In this work, relationships between tree canopy temperature and canopy height with respect to vegetation types were extracted. The southern part of Sardinia Island, which has dense forests and is often affected by wildfires, was selected as the region of interest. PRISMA hyperspectral imagery has been used to map all the available vegetation types in the region of interest using the support vector machine classifier with an accuracy of >80% for all classes. The Global Ecosystem Dynamics Investigation’s (GEDI) L2A Raster Canopy Top Height product provides canopy height measurements in spatially discrete footprints, and to overcome this issue of discontinuous sampling, Random Forest Regression was used on Sentinel-1 SAR data, Sentinel-2 multispectral data, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to estimate the canopy heights of various vegetation classes, with a root mean squared error (RMSE) value of 2.9176 m and a coefficient of determination (R²) value of 0.791. Finally, the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and emissivity product provides ground surface temperature regardless of land use and land cover (LULC) types. LST measurements over tree canopies are considered as the tree canopy temperature. We estimated the relationship between the canopy temperature of five vegetation types (evergreen oak, olive, juniper, silicicole, riparian trees) and the corresponding canopy heights and vegetation types. The resulting scatter plots showed that lower tree canopy temperatures correspond with higher tree canopies with a correlation coefficient in the range of −0.4 to −0.5 for distinct types of vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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20 pages, 5672 KiB

Open AccessArticle

Automated Recognition of Tree Species Composition of Forest Communities Using Sentinel-2 Satellite Data

by Alika Polyakova, Svetlana Mukharamova, Oleg Yermolaev and Galiya Shaykhutdinova

Remote Sens. 2023, 15(2), 329; https://doi.org/10.3390/rs15020329 - 05 Jan 2023

Cited by 3 | Viewed by 2193

Abstract

Information about the species composition of a forest is necessary for assessing biodiversity in a particular region and making economic decisions on the management of forest resources. Recognition of the species composition, according to the Earth’s remote sensing data, greatly simplifies the work and reduces time and labor costs in comparison with a traditional inventory of the forest, conducted through ground-based observations. This study analyzes the possibilities of tree species discrimination in coniferous–deciduous forests according to Sentinel-2 data using two automated recognition methods: random forest (RF) and generative topographic mapping (GTM). As remote sensing data, Sentinel-2 images of the Raifa section of Volga-Kama State Reserve in the Tatarstan Republic, Russia used: six images for the vegetation period of 2020. The analysis was carried out for the main forest-forming species. The training sample was created based on the cadastral data of the forest fund. The recognition quality was assessed using the F1-score, precision, recall, and accuracy metrics. The RF method showed a higher recognition accuracy. The accuracy of correct recognition by the RF method on the training sample reaches 0.987, F1-score = 0.976, on the control sample, accuracy = 0.764, F1-score = 0.709. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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16 pages, 7904 KiB

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Detection of Geocryological Conditions in Boreal Landscapes of the Southern Cryolithozone Using Thermal Infrared Remote Sensing Data: A Case Study of the Northern Part of the Yenisei Ridge

by Alexey Medvedkov, Anna Vysotskaya and Alexander Olchev

Remote Sens. 2023, 15(2), 291; https://doi.org/10.3390/rs15020291 - 04 Jan 2023

Cited by 4 | Viewed by 1864

Abstract

This paper discusses the potential of using infrared remote sensing data to determine geocryological conditions in the northern part of the Yenisei Ridge in Russia. Landsat-8 thermal infrared images and land surface data were used for our analysis. The obtained thermal characteristics were compared with vegetation indices calculated for the period of active vegetation growth along several surface transects. Surface observations included geobotanical descriptions, phytomass estimations, measurements of thickness of the seasonally thawed layer, and visual identification of different effects of permafrost on the components of the taiga landscape. The obtained surface temperatures differed depending of forest type due to their bio-productivity characteristics on sporadic permafrost as the most important factor of forest growth conditions within the southern part of the cryolithozone. The differences in the thermal characteristics are due to varying degree of permafrost influence on boreal vegetation growth. The surface temperature was used as indicator to quantify the relationship between the latent heat and the sensible heat fluxes for the corresponding landscape. The areas with higher surface temperatures were usually characterized by higher sensible heat flux due to lower evapotranspiration of the plant canopy. The forest types with the highest evapotranspiration had usually the lowest surface temperatures. Such forest types are also the most fire-resistant systems, and have the highest water-discharge potential. This is characteristic of the forests under the lowest impact of permafrost (thawed soils or the presence of the permafrost layer at lower depths). Such types of forests have higher ecosystem service potential (e.g., fire-resistance and stock formation). Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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16 pages, 4816 KiB

Open AccessArticle

Assessing Vegetation Phenology across Different Biomes in Temperate China—Comparing GIMMS and MODIS NDVI Datasets

by Jiangtao Xiao, Ke Huang, Yang Lin, Ping Ren and Jiaxing Zu

Remote Sens. 2022, 14(23), 6180; https://doi.org/10.3390/rs14236180 - 06 Dec 2022

Cited by 3 | Viewed by 1854

Abstract

Assessing vegetation phenology is very important for better understanding the impact of climate change on the ecosystem, and many vegetation index datasets from different remote sensors have been used to quantify vegetation phenology from a regional to global perspective. This study mainly analyzes the similarities and differences in phenology derived from GIMMS NDVI3g and MODIS NDVI datasets across different biomes throughout temperate China. We applied three commonly used methods to extract the start and end of the growing season (SOS and EOS) from two datasets between 2000 and 2015, and analyzed the spatio-temporal characteristics and trends of key phenological parameters between these two datasets in temperate China. Results showed that the multi-year mean GIMMS NDVI was higher than MODIS NDVI throughout most of temperate China, and the consistencies between GIMMS NDVI and MODIS NDVI for all biomes in the senescence phase were better than those in the green-up phase. NDVI differences between GIMMS and MODIS resulted in some distinctions between phenology derived from the two datasets. The results of SOS and EOS for three methods also showed wide discrepancies in spatial patterns, especially in SOS. For different biomes, differences of SOS in forests were obviously less than that in shrublands, grasslands-IM, grasslands-QT and meadows, whereas the differences of EOS in forests were relatively greater than that in SOS. Moreover, large differences of phenological trends were found between GIMMS and MODIS datasets from 2000 to 2015 in entire region and different biomes, and it is particularly noteworthy that both SOS and EOS showed a low proportion of the identical significant trends. The results suggested NDVI datasets obtained from GIMMS and MODIS sensors could induce the differences of the inversion of vegetation phenology in some degree due to the differences of instrumental characteristics between these two sensors. These findings highlighted that inter-calibrate datasets derived from different satellite sensors for some biomes (e.g., grasslands) should be needed when analyzing land surface phenology and their trends, and also provided baseline information for choosing different NDVI datasets in subsequent studies on vegetation patterns and dynamics. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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16 pages, 28816 KiB

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Elevation Gradients Limit the Antiphase Trend in Vegetation and Its Climate Response in Arid Central Asia

by Yujie Yang, Wei Huang, Tingting Xie, Chenxi Li, Yajie Deng, Jie Chen, Yan Liu and Shuai Ma

Remote Sens. 2022, 14(23), 5922; https://doi.org/10.3390/rs14235922 - 23 Nov 2022

Cited by 5 | Viewed by 1497

Abstract

Vegetation in arid central Asia (ACA) has been experiencing significant changes due to substantial warming and humidification since the 1980s. These changes are inhomogeneous due to the ecological vulnerability and topographic complexity of ACA. However, the heterogeneity of vegetation changes has received limited attention in the literature, which has focused more on the region’s overall general features. Thus, this paper analyzes the regional heterogeneity of vegetation changes during the growing season in ACA and further explores their underlying drivers. The results reveal an antiphase trend of vegetation, with an increase in eastern ACA and a decrease in western ACA. This antiphase pattern is primarily constrained by the divergent hydrothermal and climatic contexts of different elevation gradients. At elevations higher than 300 m (in the eastern ACA), increased growing season precipitation dominates vegetation greening. Conversely, vegetation at elevations lower than 300 m (in western ACA) is influenced by growing season soil water, which is driven by winter precipitation (pre-growing season precipitation). Additionally, the temperature could indirectly impact vegetation trends by altering precipitation, soil water, glaciers, snow cover, and runoff. Our findings have implications for restoring the ecosystem and sustainable development in ACA. Full article

(This article belongs to the Special Issue Remote Sensing of Climate-Vegetation Dynamics and Their Effects on Ecosystems)

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