Background Vegetation Research Articles

Classification of vegetation of the Barents Sea coast at the mouth of the Indiga River

Classification of coastal vegetation from the standpoint of the ecological-phytocoenotic approach at the estuary of the Indiga River, flowing into the southeastern part of the Barents Sea, is presented. On the base of 131 relevés of coastal vegetation, 23 associations in 18 formations were identified. The coastal vegetation in different biotopes differs in the structure and features of the phytocoenoses floristic composition. Based on the signs of vegetation cover, mesorelief, and hydrological features of the shores, the following biotopes were identified: saline and brackish marshes, beaches and dunes, tidal flats at the mouths of estuaries, saline and brackish lagoon lakes on marshes. The marches with 14 described associations have the highest phytocoenotic diversity. A phytocoenotic diversity of the beaches is noticeably less, 4 associations are described there. The vegetation background of the beaches is formed by the communities of the Leymeta arenarii formation, the vegetation background of the salty marshes is formed by the communities of the Cariceta subspathaceae, Cariceta glareosae, and Cariceta mackenziei formations. In saline lakes, large areas are occupied by communities of the Hippurideta tetraphyllae formation, and the communities of the Triglochineta maritimi and Plantagineta subpolaris formations are rare, the northern limit of their range runs approximately along the Indiga mouth. The vegetation of brackish marshes is mainly formed by the Cariceta salinae herbosum, Junceta gerardii, and Cariceta rariflorae communities. At the mouth of Indiga estuary, large areas are occupied by communities of the Magnocariceta formation. In the mouth of the Indiga, a little north of the range limit, communities of Eleocharis uniglumis, a rare species in the Nenets Autonomous Okrug, are described for the first time at the rank of association Eleocharitetum uniglumis. The aquatic vegetation of desalinated lagoon lakes is represented by the Myriophylleta sibirici formation. The halophyte vegetation of the Indiga River estuary is compared with the estuaries of the rivers of the Kanin Peninsula and the Pechora Bay. The ordination of geobotanical relevés shows a greater similarity of the coastal vegetation of the Indiga estuary with the estuaries of the rivers flowing on the Kanin Peninsula of the White and Barents Seas basins and with the vegetation of the rivers estuaries of the Pechora Bay coast. The article has an electronic appendix (https://journals.rcsi.science/0006-8136/issue/archive).

Remains of cave hyenas Crocuta spelaea (Hyaenidae) from Late Pleistocene cave and alluvial localities in southern Western Siberia

The results of studying 562 bone remains of Crocuta spelaea (Goldfuss, 1823) from 12 cave sites in Khakassia and Altai and 4 alluvial sites in the upper Ob River are presented. In the Fanatics cave in Khakassia, 3 almost complete skeletons of cave hyenas and one partially preserved were found. In this trap cave, 37–42 thousand years ago, hyenas died falling into a deep karst well. Morphometric analysis of the skulls and bones of the postcranial skeleton showed that the cave hyenas of the Late Pleistocene of southern Western Siberia did not differ in body size from the hyenas of Western Europe. In the alluvial localities of the Upper Ob region, the remains of C. spelaea are extremely rare, averaging 0.07% of the number of megafauna remains, which is several times less than that of the cave lion. In the Altai and Khakassia mountains, the relative abundance of cave hyenas may not have been inferior to that of Panthera leo spelaea (Goldfuss, 1810). During the glaciation periods, when cold and arid mountain-steppe landscapes dominated here, inhabited by diverse and numerous megafauna, the abundance of C. spelaea was the highest. During the interglacial period, against the background of widespread forest vegetation, the abundance of herd-hoofed animals decreased, which led to a several-fold decrease in the number of cave hyena populations. The most recent remains of C. spelaea from the Chik River are dated to the age of 33.4 thousand years ago.

Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images

Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data. This study leverages AI-assisted techniques to enhance the identification and mapping of epiphytes using UAV imagery. The primary objective of this research is to evaluate the effectiveness of AI-assisted methods compared to traditional approaches in segmenting/identifying epiphytes from UAV images collected in a reserve forest in Costa Rica. Specifically, the study investigates whether Deep Learning (DL) models can accurately identify epiphytes during complex backgrounds, even with a limited dataset of varying image quality. Systematically, this study compares three traditional image segmentation methods Auto Cluster, Watershed, and Level Set with two DL-based segmentation networks: the UNet and the Vision Transformer-based TransUNet. Results obtained from this study indicate that traditional methods struggle with the complexity of vegetation backgrounds and variability in target characteristics. Epiphyte identification results were quantitatively evaluated using the Jaccard score. Among traditional methods, Watershed scored 0.10, Auto Cluster 0.13, and Level Set failed to identify the target. In contrast, AI-assisted models performed better, with UNet scoring 0.60 and TransUNet 0.65. These results highlight the potential of DL approaches to improve the accuracy and efficiency of epiphyte identification and mapping, advancing ecological research and conservation.

Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery

Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.

Bionic metamaterial for multispectral-compatible camouflage of solar spectrum and infrared in the background of vegetation

Bionic metamaterial for multispectral-compatible camouflage of solar spectrum and infrared in the background of vegetation

Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm

Given the importance and interest of buildings in the urban environment, numerous studies have focused on automatically extracting building outlines by exploiting different datasets and techniques. Recent advancements in unmanned aerial vehicles (UAVs) and their associated sensors have made it possible to obtain high-resolution data to update building information. These detailed, up-to-date geographic data on the built environment are essential and present a practical approach to comprehending how assets and people are exposed to hazards. This paper presents an effective method for extracting building outlines from UAV-derived orthomosaics using a semantic segmentation approach based on a U-Net architecture with a ResNet-34 backbone (UResNet-34). The novelty of this work lies in integrating a grey wolf optimiser (GWO) to fine-tune the hyperparameters of the UResNet-34 model, significantly enhancing building extraction accuracy across various localities. The experimental results, based on testing data from four different localities, demonstrate the robustness and generalisability of the approach. In this study, Locality-1 is well-laid buildings with roads, Locality-2 is dominated by slum buildings in proximity, Locality-3 has few buildings with background vegetation and Locality-4 is a conglomeration of Locality-1 and Locality-2. The proposed GWO-UResNet-34 model produced superior performance, surpassing the U-Net and UResNet-34. Thus, for Locality-1, the GWO-UResNet-34 achieved 94.74% accuracy, 98.11% precision, 84.85% recall, 91.00% F1-score, and 88.16% MIoU. For Locality-2, 90.88% accuracy, 73.23% precision, 75.65% recall, 74.42% F1-score, and 74.06% MioU was obtained.The GWO-UResNet-34 had 99.37% accuracy, 90.97% precision, 88.42% recall, 89.68% F1-score, and 90.21% MIoU for Locality-3, and 95.30% accuracy, 93.03% precision, 89.75% recall, 91.36% F1-score, and 88.92% MIoU for Locality-4.

Asymmetric response of vegetation GPP to impervious surface expansion: Case studies in the Yellow and Yangtze River Basins

Terrestrial gross primary production (GPP) changes due to impervious surfaces significantly impact ecosystem services in watersheds. Understanding the asymmetric response of vegetation GPP to impervious surface expansion is essential for regional development planning and ecosystem management. However, the asymmetric response of vegetation GPP to the impacts of impervious surface expansion is unknown in different watersheds. This paper selected the Yellow River and Yangtze River basins as case studies. We characterized the overall change in GPP based on changes in impervious surface ratio (ISR), determined impervious surface expansion's direct and indirect impacts on GPP in the two watersheds, and further analyzed the asymmetric response of the compensatory effects of indirect influences on the impervious surface expansion in different watersheds. The results showed that: (1) The vegetation GPP decreased with increasing ISR in the Yangtze River Basin, while that in the Yellow River Basin first increased and then reduced. (2) The direct impacts of increased ISR reduced vegetation GPP, while the indirect impacts both had a growth-compensating effect. Growth compensation stabilized at approximately 0.40 and 0.30 in the Yellow and Yangtze River Basins. (3) When the ISR was 0.34–0.56, the growth compensation could offset the reduction of GPP due to direct impact and ensure that the background vegetation GPP was not damaged in the Yellow River Basin. In contrast, the background vegetation GPP was inevitably impaired with increased ISR in the Yangtze River Basin. Therefore, this study suggests that the ISR should be ensured to be between 0.34 and 0.56 to maximize the impervious surface of the Yellow River Basin without compromising the background vegetation GPP. While pursuing impervious surface expansion in the Yangtze River Basin, other programs should be sought to compensate for the loss to GPP.

Differences in the removal efficiency of heavy metals in soils with different vegetation backgrounds along the China-Russia crude oil pipeline

Differences in the removal efficiency of heavy metals in soils with different vegetation backgrounds along the China-Russia crude oil pipeline

Biomimetic multilayer film simulating solar spectrum reflection characteristics of natural vegetations for optical camouflage.

The camouflage for developed hyperspectral detection technology, which can accurately distinguish the spectrum between object and background, has emerged as an important unsolved challenge. In this study, a biomimetic film (Ge/ZnS multilayer structure) for optical camouflage of hyperspectral and laser with color simulation has been proposed and experimentally demonstrated. By taking advantage of the wavelength selective property of Ge/ZnS multilayer through film interference, the biomimetic film which can simulate the reflection spectral characteristics of vegetation background and eliminate laser signal has been realized based on inverse design. The selective narrowband absorption can manipulate the contrary condition for hyperspectral camouflage (high reflectance in 0.8-1.3 µm) and laser camouflage (low reflectance at 1.06 µm) in the same waveband. The planarized biomimetic multilayer film presents several distinct advantages: (1) elaborate simulation of vegetation reflectance spectrum for hyperspectral camouflage (the spectral similarity coefficient of 92.1%), and efficient absorption at 1.06 µm for laser camouflage (reflectance of 17.8%); (2) tunable color chrominance of various vegetation types for visual camouflage; (3) thermally robust camouflage performance (up to 250 °C) due to temperature endurable property of Ge and ZnS. The hyperspectral-laser camouflage film expands the design strategy of optical camouflage application.

A comparison of annual streamflow sensitivities to vegetation change and climate variability in fourteen large watersheds along climate zones in China

Extensive studies have revealed the impacts of climate variability and vegetation change on annual streamflow. However, the response intensity of annual streamflow varies among watersheds owing to differences in climate and watershed properties, which have rarely been assessed, especially for streamflow sensitivity to forest change. This calls for studies to evaluate annual streamflow sensitivity, defined as the annual streamflow response intensity (%) per unit of external change (%), to better interpret variations in annual streamflow responses among watersheds as well as their associated driving mechanisms. In this study, we estimated and compared annual streamflow sensitivities to vegetation change (Sv) and climate variability (Sc) by applying a five-step framework in 14 large watersheds in China and disentangled the contributions of their associated key drivers. We found that Sv was significantly greater than Sc, suggesting that annual streamflow was more sensitive to vegetation change than to climate variability in 11 watersheds. Alpine watersheds have the lowest Sv because of water plays a minor supportive role in vegetation growth under low effective precipitation and leaf area index, whereas higher topsoil salinity, which largely promotes water movement, contributes to a high Sv in temperate watersheds. Temperate watersheds also have a large Sc because their climate is dry and the resilience of their topography to climate variability is low. The relative contributions of the associated key drivers showed that Sv was determined by topography (36.35%), soil (33.68%), landscape patterns (12.22%), vegetation background (9.26%), and climatic conditions (8.49%). In contrast, Sc was mostly controlled by topography (54.39%) and climatic conditions (33.08%), followed by vegetation background (12.53%). This study highlights that the management paradigm for maintaining sustainable water resources should focus on both climate and vegetation changes considering the differences in watershed properties in a changing environment.

THE EFFECT OF CONTRAST ENHANCEMENT ON EPIPHYTE SEGMENTATION USING GENERATIVE NETWORK

Abstract. The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model. The input image quality and the variety of training samples are highly influencing the features derived by the deep learning filters for segmentation. This study focus on the effect of image quality of a natural dataset of epiphytes captured using Unmanned Aerial Vehicles (UAV), while segmenting the epiphytes from other background vegetation. The dataset used in this work is highly challenging in terms of pixel overlap between target and background to be segmented, the occupancy of target in the image and shadows from nearby vegetation. The proposed study used four different contrast enhancement techniques to improve the image quality of low contrast images from the epiphyte dataset. The enhanced dataset with four different methods were used to train five different segmentation models. The segmentation performances of four different models are reported using structural similarity index (SSIM) and intersection over union (IoU) score. The study shows that the epiphyte segmentation performance is highly influenced by the input image quality and recommendations are given based on four different techniques for experts to work with segmentation with natural datasets like epiphytes. The study also reported that the occupancy of the target epiphyte and vegetation highly influence the performance of the segmentation model.

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai’i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

Bloodstained flowers and bloodthirsty flies.

This article is a Commentary on Heiduk et al. (2023), 239:1490–1504.

Ways of step-by-step assessments of transformation and dynamics of soils and vegetation functioning in Kazakhstan ecosystems

On the basis of the obtained expeditionary data, the authors performed a comprehensive analysis of the ecosystems’ modern transformation in the studied area. In the course of the analysis, the authors found that at the present stage there have been quantitative changes (depletion of natural resources) in used landscapes, along with them, there are changes in qualitative characteristics (accumulation of resources). Now, against the background of vegetation and soil degradation, ways of their restoration are observed. New combinations of degraded and self-recovering ecosystems have emerged. Based on the analysis of the current state of different ecological systems and their relationships, the authors determined the possibilities of the dynamics of their combinations functioning by stages. This will make it possible to give a more reliable forecast of the ongoing processes in the ecosystems of the Republic of Kazakhstan.

Ecohydrological insight: Solar farms facilitate carbon sink enhancement in drylands

Solar farms are critical to tackling climate change and achieving carbon neutrality. Besides producing renewable energy, a solar farm modifies microclimates and changes water distribution, consequently affecting local carbon sequestration capacity (CSC). Yet, how the CSC of an ecosystem responds to these changes after solar farm construction remains inadequately understood. Herein, the SOFAR model was adopted to reveal the effects of large-scale solar farms (LSFs) on CSC in arid northern China, with a series of numeric experiments along a climate gradient (with precipitation ranging from 70 to 500 mm yr−1). The results show that relative to pristine vegetation background, CSC was non-linearly increased by averages of 3.49–6.68%, 4.43–10.25%, 5.07–9.71% and 5.6% each year after the installation of LSFs in hyper-arid climates (with aridity index or AI = 0.04–0.05), arid climates (AI = 0.14–0.16), semi-arid climates (AI = 0.21–0.3) and semi-humid climates (AI = 0.55), respectively. The increase in available water for plants growing under the drip lines of photovoltaic panels (PVs) in LSFs is confirmed to be the overwhelming factor responsible for CSC enhancement. Although biases remain in the estimation of increased CSC in hyper- and semi-humid regions due to the high variability of climate (e.g., extreme drought events) and serious radiation reduction beneath PVs, it is certain that solar farms facilitate CSC without increasing external land use. These results will deepen our understanding of the feedback between solar farms and ambient environments and be meaningful for vegetation management in solar farms, especially in the context of climate change and carbon neutrality aims.

A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning

Wildfires negatively affect the atmosphere and ecological environment. The rapid identification of smoke is helpful for early fire detection and positioning, which are significant for fire early warning, fire point tracing, and atmospheric environment monitoring. The purpose of this research is the establishment of a smoke detection framework with which to carry out smoke identification, concentration inversion and the extraction of the smoke concentration center to realize fire source identification and positioning. The spectral characteristics and variation pattern of smoke were first studied and analyzed based on a physical correlation model and laboratory experiments. Moreover, the spectral variation of the vegetation background was measured by the Mahalanobis distance (MD), and MD-based smoke identification and concentration inversion were carried out. Then, the extraction of the smoke concentration center and fire source positioning were realized based on the Laplace operator. Finally, the application and verification of the proposed method were carried out on spaceborne data of forest smoke in Daxing’anling, China, and British Columbia, Canada. The results show that: (1) At the significance level α = 0.1%, the overall accuracy of smoke recognition based on satellite images was 91.30%, and the Kappa coefficient was 81.69%. (2) The retrieved smoke concentration was in line with the visual interpretation results. (3) The fire point location error was 23.05 ± 4.14 m (less than 2 pixels). The results indicate that the proposed MD-based smoke detection model can effectively realize smoke pixel identification and concentration inversion. The proposed smoke concentration center identification method can accurately locate the fire source and provide positioning services to trace the source of wildfires in forest fire emergencies.

A novel infrared stealth coating material IR-PAN with excellent infrared radiation feature fusion performance of vegetation background

In this work, to explore the optimal coating process parameters of superior infrared (IR) camouflage performance in vegetation background, the effect of IR unit materials with different substrate surface (Al and Foam) on the surface temperature and IR image at different temperatures were studied. Wherein, the emissivity values of IR unit range from 0 to 0.933, and the scope of the substrate thickness is 0.2–1.2 cm. Besides, in order to further improve the IR camouflage performance of such IR unit materials, an IR/AlN/SiC composite coating (IR-PAN) was prepared and the IR fusion effect was also evaluated by the temperature difference, IR radiation energy difference and the fusion effect of the thermal image between IR-PAN and the traditional coatings. Finally, the experimental results showed that the target background temperature difference, radiation energy difference and the infrared fusion effect of IR-PAN were better than the traditional coatings because of the addition of the underlying coating, which buffered the heat and reduced the difference between the target and background thermal inertia. The temperature difference reduced from 2.58 °C to 1.13 °C, the energy difference of infrared radiation decreased from 1.6 e−3 J to 8.3 e−7 J and more fusion of thermal images was observed. IR-PAN exhibited better corrosion resistance, higher thermal conductivity of 0.463 (about 0.25 higher than traditional epoxy resin coating), hardness of 6H, impact resistance of 50/kg·cm−1, adhesion grade 1 (better mechanical properties) and the optimum AlN content of 20%.

The choice of spectral ranges of registration for the fluorescent method for detecting leaks in oil pipelines at an excitation wavelength of 355 nm

Mathematical modeling was carried out based on the experimentally measured fluorescence spectra of oil, vegetation and water to select the most effective spectral registration ranges for the fluorescent method for detecting oil leaks at an excitation wavelength of 355 nm. The results of mathematical modeling show that the probabilities of correct detection and false alarms for the problem of detecting oil leaks significantly depend on the type of oil and, accordingly, on the spectral channels selected for monitoring. For reliable detection of oil spills against the background of vegetation or water bodies, two or three spectral channels must be used. The highest probabilities of correct detection (>0.999) and small probabilities of false alarms (<0.04) can be achieved for oils with intensity maxima of laser-induced fluorescent radiation at wavelengths of ~ 420 and 550 nm (when using two spectral channels) and for oils with fluorescence intensity maximum at a wavelength of ~510 nm (when using three spectral channels). For oils with a maximum fluorescence intensity at a wavelength of about 475 nm (when using three spectral channels), the results are worse, although they remain acceptable (at a measurement noise of 10%, the probability of correct detection and false alarms, respectively, 0.94 and 0.11).

A light scattering camouflage membrane with similar solar spectrum reflectance to leaves based on a chlorophyll and titanium dioxide composite.

To counter the hyperspectral detection under the background of vegetation, a light scattering camouflage polyvinyl alcohol membrane containing lithium chloride, chlorophyll (Chl) and titanium dioxide (TD) particles was developed according to the bionic principle. Based on the reflectance and transmittance of the membrane, the optical constants of all components of the membrane were inverted via the ray tracing model and four flux Kubelka-Munk model. Using the determined optical constants, the reflectances of the membranes with different component contents were predicted through the model, and the effects of TD, Chl and water contents on the reflectance of the membrane were elucidated, respectively. Besides, a military specification of the USA in the region of 760 to 1200 nm and an Osmanthus fragrans leaf were used as a spectrum requirement and a simulation object of the camouflage membrane, respectively, to determine the appropriate contents of TD, Chl and water. It is found that when the volume fractions of TD, Chl and water are 0.7%, 5% and 50%, respectively, the 0.3 mm thick membrane can not only meet the military specification but also exhibit a reflection spectrum similar to that of the leaf with a similarity of 0.976.

Built-Up Area Detection Based on Degree of Polarization Analysis in Frequency Domain Using Fully PolSAR Data

This work proposes a built-up area detecting method by analyzing the fluctuation of degree of polarization in frequency domain. The averaged detrended amplitude of the two dimensional fast Fourier transform is proposed for describing the fluctuating information, and serving as the new discriminator. The comparison results for ALOS2-PALSAR2 data of Hakodate area, San Francisco area, and Ebetsu area prove that the proposed method has stably higher detecting accuracy, basically higher than 90%, for various types of built-up areas, much fewer error responses, basically lower than 5%, in natural area, and better performance on recognizing small buildings from vegetation background. Two application examples, i.e., urbanization process observation of Weihe area, and collapsed buildings detection of Mashikimachi area after the Kumamoto earthquake further confirm its good performance.