Vegetation Monitoring Research Articles

Decadal vegetation changes in a subarctic‐alpine ecosystem: Can effects of Iceland's largest hydropower reservoir, climate change, and herbivory be detected?

AbstractAimsWhile plot‐based vegetation surveys provide means to precisely track changes in plant species abundance and distribution, ecosystems are continuously influenced by numerous drivers, confounding interpretation of monitoring results. Following the making of Iceland's largest hydropower reservoir in 2006, a decadal vegetation monitoring was set up. Relating our results to those of different types of environmental monitoring associated with the hydropower project, we aimed to gain insights into the forces driving the spatio‐temporal vegetation pattern.LocationSubarctic‐alpine area near Hálslón reservoir, East Iceland.MethodsAt the time of its making, we conducted a baseline plant survey in a total of 72 plots, spread around Hálslón reservoir's vicinity (each containing ten 0.25 m2 subplots). Plots were located in three different habitat types (heathlands, wetlands, poorly vegetated land), at three different sites regarding direction and distance from the reservoir. A decade later, we resurveyed the plots and reviewed the results of other environmental monitoring projects in the area, mostly presented in the gray literature.ResultsTemporal changes in vegetation were mostly inconsistent between and within habitat types, but some general trends were noticed, that is, decreased lichen and fern cover in heathlands. Distance from the reservoir did not significantly affect changes in vegetation cover. The greatest overall changes were detected where great changes in herbivore land usage had been recorded during the study.ConclusionsDirect effects of Hálslón reservoir on vegetation in our study plots were not noticed. Most likely, the reservoir indirectly impacted vegetation in our study through loss of grazing area, affecting herbivore land usage, coupled with an unrelated population growth of two of the area's main wild herbivores before and during the study period. Response to climate change was not detected, potentially concealed by substantial land‐use change and a short study period.

Satellite-Derived Bathymetry in Support of Maritime Archaeological Research—VENμS Imagery of Caesarea Maritima, Israel, as a Case Study

Deriving bathymetry by means of multispectral satellite imagery proves to be a replicable method, offering high-resolution coverage over large areas while keeping costs low. Maritime archaeologists often require bathymetric mapping at a high resolution and with a large spatial coverage. In this paper, we demonstrate the implementation of SDB in maritime archaeology using high-resolution (5 m/pixel) data from Vegetation and Environment monitoring on a New Micro-Satellite (VENμS) imagery. We focus on the area of the Roman harbour of Sebastos, located at Caesarea Maritima along the Israeli coast of the Eastern Mediterranean. For extracting SDB, we take an empirical approach, which is based on the integration of satellite imagery and sonar depth measurements, resulting in a blue-green band ratio algorithm that provides reliable results up to a water depth of 17 m. Comparison with in situ depth measurements yielded an RMSE of 0.688 m. The SDB mapping is complemented by satellite-based identification of above- and below-water rocks. The presented approach can readily be replicated in other regions using various types of multispectral satellite imagery, particularly when only coarse bathymetric sonar data are available, thus substantially contributing to our ability to perform maritime archaeological research.

The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.

Monitoring of chlorophyll content in local saltwort species Suaeda salsa under water and salt stress based on the PROSAIL-D model in coastal wetland

As the invasion of alien species intensifies, the native salt marsh vegetation, especially the ecosystem of Suaeda salsa (S. salsa), in Chinese coastal wetlands has been severely disrupted, significantly impeding its functionality within coastal wetland ecosystems. Chlorophyll content (Cab) is an important parameter for monitoring the growth and health status of vegetation. However, the remote sensing mechanism of Cab for different phenotypes of green and red vegetation influenced by betacyanin under different water and salt conditions is not clear. Therefore, to further clarify the remote sensing mechanism of S. salsa and improve the generality of Cab prediction for different phenotypes under different water and salt conditions, we designed a growth experiment under different groundwater levels and salt concentrations. This experiment was used to simulate the growth of S. salsa in different habitats in nature and investigate the relationship between their physicochemical parameters, spectra, and environmental stress. We propose using the specific absorption coefficient of betacyanin (Beta) to adjust PROSAIL-D to better fit the spectral characteristics of S. salsa. The relationship between Beta and Cab was utilized to further optimize the simulation accuracy of the PROSAIL-D model in different environments. Feature extraction algorithms, including differential enhancement of two-dimensional correlation spectroscopy (FD-2DCOS) and three-band models, were introduced to construct multiple spectral features for establishing a Cab prediction model for S. salsa. The research results showed that: (1) S. salsa had the best response of Cab and spectra to salinity under a higher groundwater level and showed a gradual reddening trend. (2) The spectral curves simulated by the adjusted PROSAIL-D model were more consistent with the spectral characteristics of S. salsa under different water-salt environments, and the simulation effect could be further optimized by using the relationship between Beta and Cab. (3) The FD-2DCOS analysis method we proposed could highlight the useful spectral information related to Cab better than the original 2DCOS algorithm. The final eight spectral features of (1/R701–1/R540)R540, Mean(D590–600), Mean(D660–670), Mean(D677–685), FDVI_NRG, FDVI_NIR, Skewness (D677–750) and Position(D677–750) had the best response to Cab of S. salsa, and had the least influence on LAI and soil background. (4) The particle swarm optimization random forest regression (PSO-RFR) model based on eight spectral features and adding 70% measured spectral data performed the best in predicting the actual Cab of S. salsa, with an RMSE of 2.326 μg·cm−2. Although further optimization and calibration of the adjusted PROSAIL-D model and Cab prediction model of S. salsa are needed in the future, this study extended its application to S. salsa, a special local salt marsh vegetation, and promoted the development of Cab monitoring of salt marsh vegetation in coastal wetlands.

Intelligent vegetable freshness monitoring system developed by integrating eco-friendly fluorescent sensor arrays with deep convolutional neural networks

Given the perishable and seasonal nature of vegetables, monitoring their freshness is essential to ensure food safety and reduce waste. Currently, there are limited packaging systems for fresh vegetables that incorporate intelligent freshness monitoring labels. Herein, we report on the development and application of a 3 × 6 fluorescent sensor array that exhibits pH-sensitive properties, utilizing curcumin, puerarin, and fisetin. During spoilage, yardlong beans and spinach, which had high protein content, produced alkaline volatile organic compounds (VOCs), whereas sweet corn, rich in sugar, emitted acidic VOCs. The fluorescent sensor array, integrated with deep convolutional neural network (DCNN), enabled non-destructive, real-time, and accurate classification of the freshness of the aforementioned three vegetables by detecting the acidity or alkalinity of their VOCs. The trained ResNet50 DCNN model achieved an overall accuracy of 96.21 % in classifying the freshness of the aforementioned vegetables in the testing set, with specific accuracies of 98.58 % for yardlong beans, 97.15 % for spinach, and 92.89 % for sweet corn, respectively. This intelligent freshness monitoring platform is adaptable for monitoring and classifying the freshness of a wide range of agricultural and food products.

Nature-Based Solutions vs. Human-Induced Approaches for Alpine Grassland Ecosystem: “Climate-Help” Overwhelms “Human Act” to Promote Ecological Restoration in the Three-River-Source Region of Qinghai–Tibet Plateau

How climate change and human activities drive the evolution of the regional environment and where the quality of ecosystems improve or decline over time have become widespread concerns. In this study, we took the Three-River-Source (TRS) region of the Qinghai–Tibet Plateau as a case, aiming to identify and quantify the contribution of the natural and anthropogenic factors to the ecosystem changes over the past years from 1980 to 2018 using the methods of remote sensing and spatial statistical analysis. Based on the land cover map interpreted by reference to satellite remote sensing imagery data, we defined the Ecological Restoration Area Proportion (ERAP) as the bare land patch decrement to indicate the ecologically restored quantity in space. Assembling the restoration project information, we digitalized and vectorized the ecological Restoration Intensity (RI) including the spatial range and temporal duration. Combining the ERAP and the net primary productivity (NPP), which indicates the quantity and quality of ecosystems, respectively, the ecological asset Index (EAI) was developed and calculated. Having integrated the datasets of the vegetation monitoring, climatic factors, geographical factors, and human activities, we performed multi-variable analysis of the attribution of how the change in the EAI, the NPP, and the EAI have been affected by these factors together. The NPP of the middle and eastern parts of the TRS region has improved the most, as the average growth rate of NPP reached approximately 2.5 kg C/m2/10a. Due to such dynamic pattern, we found that human-induced re-vegetation has made limited contributions in our multi-regression model as the variance explained by the RI merely amounts to 4.4% to 8.8%, while the changes were mostly dependent on the regional temperature and the precipitation which contributed over 45% to the ecological restoration on average. It was summarized that “climate-help” overwhelms “human act” in such alpine grassland ecosystem. The regression results for the different aspects of the ERAP and NPP demonstrated that the ecological restoration project helped most in regard to ecosystem quality improvement rather than the restored ecosystem quantity. Our study has developed a comprehensive assessment methodology that can be reused to account for more ecological asset. The case is an example of an alpine ecosystem in which the success of ecological restoration needs favorable climatic conditions as supporting evidence for the nature-based solution.

Мониторинг почв в районе предприятий по утилизации опасных промышленных отходов

The article provides proposals for organizing and conducting soil monitoring in the vicinity of the enterprise for recycling waste of I and II hazard class. Ecotechnopark “Mirny” is organized in the Kirov region on the basis of the former chemical weapons destruction facility. We propose a list of indicators, controlled in soils, based on information on the composition of emissions, requirements of regulatory documents, results of engineering and environmental surveys and environmental impact assessments. A soil monitoring network was recommended, including 14 sites located in different biocenoses in accordance with the wind rose and terrain features, which is coordinated with the air and vegetation monitoring network. The developed monitoring regulations provide for different frequency of soil sampling and analysis for various indicators depending on the technological process, composition of emissions at each stage, and provides for soil survey immediately before the start of operation of the facility. This survey will allow us to assess the natural-anthropogenic background and subsequently assess the level of environmental impact by accumulative trend in the content of pollutants. This approach seems relevant in a high soil cover complexity, remoteness of protected areas, construction of the object on previously developed territories, and contradictory and incomplete information about the regional background. Annually soil sampling from the upper layers of soil seems advisable. If contamination of the upper horizon is detected, soil samples from the underlying layers should be analyzed to determine the depth of contamination. It is especially important when groundwater is close to the ground.

The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series

Monitoring vegetation dynamics (VD) is crucial for environmental protection, climate change research, and understanding carbon and water cycles. Remote sensing is an effective method for large-scale and long-term VD monitoring, but it faces challenges due to changing data uncertainties caused by various factors, including observational conditions. Previous studies have demonstrated the significance of implementing proper quality control (QC) of remote sensing data for accurate vegetation monitoring. However, the impact of different QC methods on VD results (magnitude and trend) has not been thoroughly studied. The fraction of absorbed photosynthetically active radiation (FPAR) characterizes the energy absorption capacity of the vegetation canopy and is widely used in VD monitoring. In this study, we investigated the effect of QC methods on vegetation monitoring using a 20-year MODIS FPAR time series. The results showed several important findings. Firstly, we observed that the Mixed-QC (no QC on the algorithm path) generally produced a lower average FPAR during the growing season compared to Main-QC (only using the main algorithm). Additionally, the Mixed-QC FPAR showed a very consistent interannual trend with the Main-QC FPAR over the period 2002–2021 (p < 0.05). Finally, we found that using only the main algorithm for QC generally reduced the trend magnitude (p < 0.1), particularly in forests. These results reveal differences in FPAR values between the two QC methods. However, the interannual FPAR trends demonstrate greater consistency. In conclusion, this study offers a case study on evaluating the influence of different QC methods on VD monitoring. It suggests that while different QC methods may result in different magnitudes of vegetation dynamics, their impact on the time series trends is limited.

Restoring the threatened Scalesia forest: insights from a decade of invasive plant management in Galapagos

Island forests are becoming increasingly fragmented and colonized by invasive species, which can eventually lead to local species extinctions. In the Galapagos Islands, invasive species pose a serious extinction threat to the endemic daisy tree Scalesia pedunculata, formerly the dominant habitat-forming species of the unique Scalesia forest. This forest has been reduced to fragments due to land use changes in the past and is now increasingly invaded by introduced plants. We conducted a field experiment on Santa Cruz Island to assess the impacts of blackberry (Rubus niveus) and two other invasive plant species, Cestrum auriculatum and Tradescantia fluminensis, as well as the effects of the removal of two of these (R. niveus and C. auriculatum) on cover, composition and diversity of the resident plant communities. Particular attention was paid to effects of the invasive species on the S. pedunculata population. Annual vegetation monitoring was carried out in a total of 34 permanent plots (10 m × 10 m) over 10 years (2014–2023), using the line-intercept method. Seventeen of these plots were established in an invaded area and 17 plots in an area with continuous invasive plant removal since 2014. Results indicated that there were significant changes in both the species composition of the plant communities and average percent cover of species over time, comparing removal plots with invaded plots. Species composition in removal plots changed significantly more than in invaded plots, towards a plant community with greater percent cover of endemic species. A significant negative relationship between the three invasive species and cover of S. pedunculata suggested that multiple invader species may have additive negative impacts. Natural recruitment of S. pedunculata by seeds was observed in the removal but not in the invaded plots. These results, as well as the striking decrease of 71% in cover of adult S. pedunculata in the invaded plots indicate that this threatened species will be driven to local extinction on Santa Cruz Island, Galapagos, in less than 20 years if invasive plant species are not removed on a large scale.

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

ABSTRACT The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.

Evaluation of the Effects of Wastewater Irrigation on Heavy Metal Accumulation in Vegetables and Human Health in the Cauliflower Example : Heavy Metal Accumulation in Cauliflower.

The goals of the present research were to determine the heavy metal contents in the water-soil-cauliflower samples in industrial wastewater irrigated areas and to assess the health risks of these metals to the people. Metal analyses were carried out using the atomic absorption spectrophotometer equipped with a graphite furnace. The metal readings in the cauliflower specimens ranged from 1.153 to 1.389, 0.037 to 0.095, 0.61 to 0.892, 0.625 to 0.921, 1.165 to 2.399, 0.561 to 0.652, 0.565 to 0.585, 0.159 to 0.218 and 1.268 to 1.816mg/kg for Cd, Co, Cr, Cu, Fe, Ni, Pb, Zn and Mn, respectively. Statistics revealed that, with the exception of Pb and Co (p > 0.05), there was no statistically significant variation in the metal concentrations in the cauliflower samples according to the irrigation type. Pb, Ni, and Cr had HRI values below 1.0 and did not seem to be a hazard to human health, in contrast to Cd, Co, Cr, Cu, Fe, Ni, Pb, Zn and Mn, which glanced to constitute a health risk. Regular monitoring of vegetables irrigated with wastewater is strongly advised to reduce health hazards to people.

The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.

Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics

Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991–2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (~4 ha) with low spatial offset (Craigieburn: 6.14 ± 4.03 cm; Mt Faust: 5.11 ± 2.88 cm, mean ± standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for ~6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.

Social learning to promote forest restoration in a semi-arid landscape in North Africa

Forest restoration is a suitable tool to mitigate land degradation and enhance the supply of vital goods and services. Social participation in forest restoration has gained increasing interest. Yet, the impact of participation on people's perception of ecological restoration and the restoration process has barely been addressed despite its relevance for the long-term success of restoration actions. We assessed mutual learning of different stakeholder groups in a ten-year demonstration project, and its potential to foster continued participatory forest restoration in Beni Boufrah valley, a semiarid area located in North Morocco. We performed face-to-face interviews to assess post-restoration individual learning for a subset of 15 highly engaged stakeholders using five components of social learning: reciprocal determinism, self-reflective capability, expectations, self-regulation and locus of control. Furthermore, we organized a workshop to assess collective learning in the study area. The lessons learnt by the project team was described using monitoring vegetation measurements, field visits and meetings, and stakeholder recommendations. Two thirds of the stakeholders showed an increasing awareness of the reciprocal relationship between people and the environment, while recognizing the inappropriateness of current behaviors and practices. Farmers showed continuous mistrust towards the Forestry Agency which justifies the need for more innovative approaches to resolve persistent conflicts, integrate forest and agricultural interventions and establish new mechanisms for economic motivation. Stakeholders acquired theoretical and practical concepts on forest restoration, but self-initiated activities were scarce and reinforcement of environmental awareness is still needed. There was a high impact of drought on seedling survival and growth along with a decrease in stakeholder engagement over the years. We could identify and implement a series of practical corrective measures, namely participatory re-planting, plot fencing, establishment of a local monitoring committee, and raising awareness activities. Such participatory implementation of corrective measures may enhance the credibility of the restoration process and it can be further tested in similar semiarid areas of North Africa even at a larger scale.

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Multisource High-Resolution Remote Sensing Image Vegetation Extraction with Comprehensive Multifeature Perception

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisource high-resolution remote sensing image-based vegetation extraction method that considers the comprehensive perception of multiple features. First, this method utilizes a random forest model to perform feature selection for the vegetation index, selecting an index that enhances the otherness between vegetation and other land features. Based on this, a multifeature synthesis perception convolutional network (MSCIN) is constructed, which enhances the extraction of multiscale feature information, global information interaction, and feature cross-fusion. The MSCIN network simultaneously constructs dual-branch parallel networks for spectral features and vegetation index features, strengthening multiscale feature extraction while reducing the loss of detailed features by simplifying the dense connection module. Furthermore, to facilitate global information interaction between the original spectral information and vegetation index features, a dual-path multihead cross-attention fusion module is designed. This module enhances the differentiation of vegetation from other land features and improves the network’s generalization performance, enabling vegetation extraction from multisource high-resolution remote sensing data. To validate the effectiveness of this method, we randomly selected six test areas within Anhui Province and compared the results with three different data sources and other typical methods (NDVI, RFC, OCBDL, and HRNet). The results demonstrate that the MSCIN method proposed in this paper, under the premise of using only GF2 satellite images as samples, exhibits robust accuracy in extraction results across different sensors. It overcomes the rapid degradation of accuracy observed in other methods with various sensors and addresses issues such as internal fragmentation, false positives, and false negatives caused by sample generalization and image diversity.

Modelling Vegetation Health and Its Relation to Climate Conditions Using Copernicus Data in the City of Constance

Monitoring vegetation health and its response to climate conditions is critical for assessing the impact of climate change on urban environments. While many studies simulate and map the health of vegetation, there seems to be a lack of high-resolution, low-scale data and easy-to-use tools for managers in the municipal administration that they can make use of for decision-making. Data related to climate and vegetation indicators, such as those provided by the C3S Copernicus Data Store (CDS), are mostly available with a coarse resolution but readily available as freely available and open data. This study aims to develop a systematic approach and workflow to provide a simple tool for monitoring vegetation changes and health. We built a toolbox to streamline the geoprocessing workflow. The data derived from CDS included bioclimate indicators such as the annual moisture index and the minimum temperature of the coldest month (BIO06). The biophysical parameters used are leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). We used a linear regression model to derive equations for downscaled biophysical parameters, applying vegetation indices derived from Sentinel-2, to identify the vegetation health status. We also downscaled the bioclimatic indicators using the digital elevation model (DEM) and Landsat surface temperature derived from Landsat 8 through Bayesian kriging regression. The downscaled indicators serve as a critical input for forest-based classification regression to model climate envelopes to address suitable climate conditions for vegetation growth. The results derived contribute to the overall development of a workflow and tool for and within the CoKLIMAx project to gain and deliver new insights that capture vegetation health by explicitly using data from the CDS with a focus on the City of Constance at Lake Constance in southern Germany. The results shall help gain new insights and improve urban resilient, climate-adaptive planning by providing an intuitive tool for monitoring vegetation health and its response to climate conditions.

Monitoring of mangrove forests vegetation based on optical versus microwave data: A case study western coast of Saudi Arabia

Abstract Normalized difference vegetation index (NDVI) is one of the parameters of vegetation that can be studied by remote sensing of land surface with Sentinel-2 (S-2) satellite image. The NDVI is a nondimensional index that depicts the difference in plant cover reflectivity between visible and near-infrared light and can be used to measure the density of green on a piece of land. On the other hand, the dual-pol radar vegetation index (DpRVI) is one of the indices studied using multispectral synthetic aperture radar (SAR) images. Researchers have identified that SAR images are highly sensitive to identify the buildup of biomass from leaf vegetative growth to the flowering stage. Vegetation biophysical characteristics such as the leaf area index (LAI), vegetation water content, and biomass are frequently used as essential system parameters in remote sensing data assimilation for agricultural production models. In the current study, we have used LAI as a system parameter. The findings of the study revealed that the optical data (NDVI) showed a high correlation (up to 0.712) with LAI and a low root-mean-square error (0.0296) compared to microwave data with 0.4523 root-mean-square error. The NDVI, LAI, and DpRVI mean values all decreased between 2019 and 2020. While the DpRVI continued to decline between 2020 and 2021, the NDVI and LAI saw an increase over the same period, which was likely caused by an increase in the study area’s average annual rainfall and the cautious stance of the Red Global (RSG) project on sustainability.

A hyperspectral deep learning attention model for predicting lettuce chlorophyll content

BackgroundThe phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images.ResultsA CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.ConclusionsThis study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

The Application of Remote Sensing Techniques in Ecological Environment Monitoring

As global environmental challenges continue to escalate, remote sensing technology has become an indispensable tool for researching, monitoring, and managing these issues. This article aims to summarize the principles of various remote sensing technologies, their primary application areas, and their crucial role in ecological and environmental conservation for sustainable development. Remote sensing technology offers a diverse array of data from multiple sources and scales, facilitating the monitoring of natural resource changes, studying ecosystem health, and assessing the impact of human activities on the environment. In-depth exploration of the latest advancements and application cases in remote sensing includes: (1) Vegetation: Applications in vegetation monitoring encompass the use of technologies such as unmanned aerial vehicle (UAV) remote sensing and laser radar (LiDAR) for monitoring forest health, vegetation cover, and changes. These applications support forest management and ecological research. (2) Water Bodies: Thermal infrared remote sensing and satellite remote sensing are utilized to monitor water quality, water level fluctuations, and aid in water resource management, addressing water resource challenges. (3) Soil: Remote sensing technology is employed in soil applications, utilizing multispectral and synthetic aperture radar (SAR) techniques for soil property assessment, soil moisture analysis, and soil erosion prediction. These applications contribute to agricultural and land management decisions, and provide suitable remote sensing technologies for the current trends in the ecological field, serving as a reference for monitoring methods.