Traditional Field Sampling Research Articles

Involving degradation products provides a new perspective of diffuse pollution assessment of atrazine with a modified mass balance approach.

Atrazine (ATR) is an endocrine disruptor known for its persistence and mobility. While the diffuse characteristics and potential risks of ATR have been extensively studied, its transregional migration and degradation characteristics have received less attention. In this study, a modified mass balance approach considering the diffuse source (DS), tributaries, water resource usage, degradation, adsorption, and evaporation was developed based on the traditional mass balance framework and field sampling to estimate the DS fluxes of ATR in a large river basin. Field sampling revealed that the ATR concentration in the surface water increased downstream, whereas the ATR levels in suspended particulate matter decreased. The ATR degradation ratio also decreased downstream, suggesting increased input along the river. The modified mass balance approach identified DS as the primary ATR source in the river, followed by tributaries. Together, the DS input and tributary inflow accounted for 95.6 % ± 2.1 % of the total ATR flux, with DS alone contributing 73.8 % ± 10.2 %. Finally, the ATR parent and its hazardous materials (ATR, desethylatrazine, and desisopropylatrazine) accounted for 6 % and 27 %, respectively, of the total ATR content that reached the estuary. This integrated consideration of ATR and its degradation products offer a new perspective on their transregional migration and degradation patterns resulting from diffuse pollution.

Rice Counting and Localization in Unmanned Aerial Vehicle Imagery Using Enhanced Feature Fusion

In rice cultivation and breeding, obtaining accurate information on the quantity and spatial distribution of rice plants is crucial. However, traditional field sampling methods can only provide rough estimates of the plant count and fail to capture precise plant locations. To address these problems, this paper proposes P2PNet-EFF for the counting and localization of rice plants. Firstly, through the introduction of the enhanced feature fusion (EFF), the model improves its ability to integrate deep semantic information while preserving shallow spatial details. This allows the model to holistically analyze the morphology of plants rather than focusing solely on their central points, substantially reducing errors caused by leaf overlap. Secondly, by integrating efficient multi-scale attention (EMA) into the backbone, the model enhances its feature extraction capabilities and suppresses interference from similar backgrounds. Finally, to evaluate the effectiveness of the P2PNet-EFF method, we introduce the URCAL dataset for rice counting and localization, gathered using UAV. This dataset consists of 365 high-resolution images and 173,352 point annotations. Experimental results on the URCAL demonstrate that the proposed method achieves a 34.87% reduction in MAE and a 28.19% reduction in RMSE compared to the original P2PNet while increasing R2 by 3.03%. Furthermore, we conducted extensive experiments on three frequently used plant counting datasets. The results demonstrate the excellent performance of the proposed method.

Measuring Tree Diameter with Photogrammetry Using Mobile Phone Cameras

Tree inventories are a cornerstone of forest science and management. Inventories are essential for quantifying forest growth rates, determining biomass and carbon stock variation, assessing species diversity, and evaluating the impacts of both forest management and climate change. Recent advances in digital sensing technologies on mobile phones have the potential to improve traditional forest inventories through increased efficiency in measurement and transcription and potentially through increasing participation in data collection by non-experts. However, the degree to which digital sensing tools (e.g., camera-enabled smartphone applications) can accurately determine the tree parameters measured during forest inventories remains unclear. In this study, we assess the ability of a smartphone application to perform a user-assisted tree inventory and compare digital estimates of tree diameter to measurements made using traditional forestry field sampling approaches. The results suggest that digital sensing tools on mobile phones can accurately measure tree diameter (R2 = 0.95; RMSE = 2.71 cm compared to manual measurements) while saving time during both the data-collection stage and data-entry stage of field sampling. Importantly, we compare measurements of the same tree across users of the phone application in order to determine the per-user, per-tree, and per-species uncertainty associated with each form of measurement. Strong agreement between manual and digital measurements suggests that digital sensing technologies have the potential to facilitate the efficient collection of high-quality and auditable data collected by non-experts but with some important limitations compared to traditional tree measurement approaches. Most people in the world own a smartphone. Enabling accurate tree inventory data collection through mobile phones at scale can improve our understanding of tree growth and biomass accumulation and the key factors (e.g., climate change or management practices) that affect these processes, ultimately advancing forest science and management.

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.

Correction of UAV LiDAR-derived grassland canopy height based on scan angle.

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25-65cm) and segmented (25-40cm, 40-50cm and 50-65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.

Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

TRANSFORMATION OF THE NORMALIZED DIFFERENCE CHLOROPHYLL INDEX TO RETRIEVE CHLOROPHYLL-A CONCENTRATIONS IN MANILA BAY

Abstract. Manila Bay is one of the most significant harbors in the region, enabling commerce and trade between the Philippines and other countries. Its abundant natural resources have provided for generations of its inhabitants and have driven socio-economic development for centuries. Like other water bodies adjacent to highly urbanized cities, the increased organic and nutrient loading from untreated domestic, industrial, and agricultural wastes resulted to further degradation of its water quality. While frequent water quality monitoring is ideal, data from traditional field sampling methods might not be sufficient to assess the spatial and temporal variations of water quality in Manila Bay. Remote sensing fills the need for a frequent and full overview of the bay’s water quality. Sentinel-3 images were initially processed through the Case 2 Regional CoastColour (C2RCC) model to retrieve the remote sensing reflectance. The Normalized Difference Chlorophyll Index (NDCI) was computed for the images and were modeled against the C2RCC-derived chlorophyll-a estimates. From this, we were able to get a general equation (TNDCI – transformed NDCI) to retrieve chlorophyll-a concentrations from two reflectance bands (Oa8 and Oa11). TNDCI gave an R2 of 0.9 and RMSE = 5.12 µg/L as compared to C2RCC values, and an R2 = 0.85 and RMSE = 2.44 µg/L with field data.

Exploring relationship of soil PTE geochemical and “VIS-NIR spectroscopy” patterns near Cu–Mo mine (Armenia)

PTE contamination of soils remains one of the global environmental concerns. The ways of detecting and monitoring PTE concentrations in soils varies including traditional field sampling accompanied by sample preparation and chemical analysis and state of the art visible and near-infrared (Vis-NIR) spectroscopic approaches. Among the different Machine Learning (ML) to extract soil information from spectra and to explore the relationship between spectral reflectance data and soil PTE content PLSR method is a well-established one to construct a soil PTE estimation model. This study aimed to explore the relationship of soil PTE geochemical and VIS-NIR spectroscopy characteristics in agricultural soils near Cu–Mo mine area in Armenia. PLSR method is applied to identify the links between the spectra and agricultural soil Ti, V, Cr, Mn, Fe, Co, Ba, Pb, Zn, Cu, Sr, Zr and Mo contents to reveal the potential of VIS-NIR spectroscopy in ex-situ monitoring of Kajaran soils. The results show that different portions of VIS-NIR spectra are responsible for Ti (1100–1200 nm, 2350–2500 nm), V (350–500 nm, 700–750 nm, 1000–1100 nm, 1400–2500 nm), Cr (1300–1400 nm, 1900–2100 nm) and Ba (450–500 nm, 600–800 nm, 1050–1700 nm, 2000–2100 nm, 2350–2400 nm) estimations through PLSR correspondingly. However, among the studied PTEs Ti and V, which shows significant negative correlations in VIS-NIR spectra registered at around 400–600 nm and 850–1150 nm regions, are remarkable and promising with the PLSR estimation results using VIS-NIR spectra Ti (R2Test = 0.74), V (R2Test = 0.71). This study shows that VIS-NIR spectroscopy has a high potential for the estimation of at least several PTE in soils and PLSR modelis reliable for deriving information from there.

Modeling the Missing DBHs: Influence of Model Form on UAV DBH Characterization

The reliability of forest management decisions partly depends on the quality and extent of the data needed for the decision. However, the relatively high cost of traditional field sampling limits sampling intensity and data quality. One strategy to increase data quality and extent, while reducing the overall sample effort, is using remote sensing-based data from unmanned aerial vehicles (UAV). While these techniques reliably identify most tree locations and heights in open-canopied forests, their ability to characterize diameter at breast height (DBH) is limited to estimates of a fraction of trees within the area. This study used UAV-derived DBHs and explanatory variables to test five model forms in predicting the missing DBHs. The results showed that filtering UAV DBHs using regionally derived height to DBH allometries significantly improved model performance. The best predicting model was slightly biased, with a 5.6 cm mean error and a mean absolute error of 6.8 cm. When applied across the stand, the number of trees was underestimated by 26.7 (3.9%), while the basal area and quadratic mean diameter were overestimated by 3.3 m2 ha−1 (13.1%) and 1.8 cm (8.3%), respectively. This study proposes a pathway for remotely sensed DBHs to predict missing DBHs; however, challenges are highlighted in ensuring the model training dataset represents the population.

Generative-Model-Based Data Labeling for Deep Network Regression: Application to Seed Maturity Estimation from UAV Multispectral Images

Field seed maturity monitoring is essential to optimize the farming process and guarantee yield quality through high germination. Remote sensing of parsley fields through UAV multispectral imagery allows uniform scanning and better capture of crop information, in comparison to traditional limited field sampling analysis in the laboratory. Moreover, they only represent localized sub-sections of the crop field and are time consuming to process. The limited availability of seed sample maturity data is a drawback for applying deep learning methods, which have shown tremendous potential in estimating agronomic parameters, especially maturity, as they require large labeled datasets. In this paper, we propose a parametric and non-parametric-based weak labeling approach to overcome the lack of maturity labels and render possible maturity estimation by deep network regression to assist growers in harvest decision-making. We present the data acquisition protocol and the performance evaluation of the generative models and neural network architectures. Convolutional and recurrent neural networks were trained on the generated labels and evaluated on maturity ground truth labels to assess the maturity quantification quality. The results showed improvement by the semi-supervised approaches over the generative models, with a root-mean-squared error of 0.0770 for the long-short-term memory network trained on kernel-density-estimation-generated labels. Generative-model-based data labeling can unlock new possibilities for remote sensing fields where data collection is complex, and in our usage, they provide better-performing models for parsley maturity estimation based on UAV multispectral imagery.

Comparative assessment of satellite- and drone-based vegetation indices to predict arthropod biomass in shrub-steppes.

Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority of arthropods are herbivorous, and others depend on these. Quantifying plant productivity with satellite or aerial vehicle imagery is an easy and fast procedure already tested and implemented in agriculture and field ecology. However, the capability of satellite or aerial vehicle imagery for quantifying arthropod biomass and its relationship with plant productivity has been scarcely addressed. Here, we used unmanned aerial vehicle (UAV) and satellite Sentinel-2 (S2) imagery to establish a relationship between plant productivity and arthropod biomass estimated through ground-truth field sampling in shrub steppes. We UAV-sampled seven plots of 47.6-72.3ha at a 4-cm pixel resolution, subsequently downscaling spatial resolution to 50 cm resolution. In parallel, we used S2 imagery from the same and other dates and locations at 10-m spatial resolution. We related several vegetation indices (VIs) with arthropod biomass (epigeous, coprophagous, and four functional consumer groups: predatory, detritivore, phytophagous, and diverse) estimated at 41-48 sampling stations for UAV flying plots and in 67-79 sampling stations for S2. VIs derived from UAV were consistently and positively related to all arthropod biomass groups. Three out of seven and six out of seven S2-derived VIs were positively related to epigeous and coprophagous arthropod biomass, respectively. The blue normalized difference VI (BNDVI) and enhanced normalized difference VI(ENDVI) showed consistent and positive relationships with arthropod biomass, regardless of the arthropod group or spatial resolution. Our results showed that UAV and S2-VI imagery data may be viable and cost-efficient alternatives for quantifying arthropod biomass at large scales in shrub steppes. The relationship between VI and arthropod biomass is probably habitat-dependent, so future research should address this relationship and include several habitats to validate VIs as proxies of arthropod biomass.

Grid-Scale Regional Risk Assessment of Potentially Toxic Metals Using Multi-Source Data

Understanding the risks posed by potentially toxic metals (PTMs) in large regions is important for environmental management. However, regional risk assessment that relies on traditional field sampling or administrative statistical data is labor-intensive, time-consuming, and coarse. Internet data, remote sensing data, and multi-source data, have the advantage of high speed of collection, and can, thereby, overcome time lag challenges and traditional evaluation inefficiencies, although, to date, they are rarely applied. To evaluate their effectiveness, the current study used multi-source data to conduct a 1 km scale assessment of PTMs in Yunnan Province, China. In addition, a novel model to simulate potentially hazardous areas, based on atmospheric deposition, was also proposed. Assessments reveal that risk areas are mainly distributed in the east, which is consistent with the distribution of mineral resources in the province. Approximately 3.6% of the cropland and 1.4% of the sensitive population are threatened. The risk areas were verified against those reported by the government and the existing literature. The verification exercise confirmed the reliability of multi-source data, which are cost-effective, efficient, and generalizable for assessing pollution risks in large areas, particularly when there is little to no site-specific contamination information.

Terrestrial Laser Scanning to Assess Eucalyptus Aboveground Biomass: A Surface Reconstruction Approach

Terrestrial laser scanners (TLS) provide an alternative to traditional field sampling for gathering accurate volumetric data about vegetations without destroying the tree samples. However, popular volumetric modeling approaches (e.g., those cylindrical methods) oversimplify vegetation structure by underutilizing surface undulations provided by the point cloud. Thus, this study aimed to test the capability of two specialized surface reconstruction methods against a traditional cylindrical method to obtain the stem volumes and masses from 3D-cloud points generated by terrestrial laser scanners under relatively controlled conditions in a eucalyptus plantation in Muang, Nakornratchasima Province, Thailand. The TLS point data were collected from the test plots, and then three algorithms, the Poisson Surface Reconstruction (PSR), the Screen Poisson Surface Reconstruction (SPSR), and the traditional Quantitative Structure Model (QSM) were applied in order to build volumetric models of the sample eucalyptus trees. It is notable that this is the first study to test the SPSR method on real tree samples (N=40). The results were then compared with the reference values measured by a water replacement method. The root means square errors (RMSE) were estimated between the xylometric referenced aboveground biomass (AGB) and the TLS three methods. The SPSR approach yielded the most accurate results (RMSE of 0.49 kg or 6.91%), while the PSR method resulted in an RMSE of 0.60 kg (8.37%). The QSM method had the worst results, with an RMSE of 1.09 kg (15.31%). Despite the occlusion problem that caused 20% systematic error, this outcome provides evidence that the use of two Poisson reconstruction methods (e.g., PSR and SPSR) provides effective alternatives to accurately quantify aboveground biomass for eucalyptus trees.

Documenting Emerging Insects, Environmental DNA, and Metal Concentrations in a Small Appalachian Stream

Accurately documenting aquatic insects is of the utmost importance given recent proposals for a paradigm shift in conservation to protect less-charismatic species that are necessary for ecosystem functioning. We used both field techniques and molecular methods to assess biodiversity in a stream in southwest Virginia. We used emergence traps to collect organisms that emerged from the stream as reproducing adults over a 4-week period and collected environmental samples (e.g., water, sediment) to sequence the DNA found in the samples. Emerging aquatic insect abundance, richness, and diversity increased over time. More family richness was detected using environmental DNA (eDNA) than traditional field sampling; however, many families detected using field techniques were not recovered using eDNA, furthering support that both protocols are necessary for fully documenting biodiversity. We did not have much success in identifying eDNA sequences to species with high sequence similarity, suggesting that invertebrate biodiversity of southwest Virginia is not well-documented in open-source DNA sequence databases. In addition to documenting insect biodiversity, we measured the levels of heavy metals in the stream sediment. Sediment values for cadmium, chromium, copper, mercury and lead were within regulatory limits and were not significantly correlated with biodiversity measures.

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review.

Recent research advances in wheat have focused not only on increasing grain yields, but also on establishing higher grain quality. Wheat quality is primarily determined by the grain protein content (GPC) and composition, and both of these are affected by nitrogen (N) levels in the plant as it develops during the growing season. Hyperspectral remote sensing is gradually becoming recognized as an economical alternative to traditional destructive field sampling methods and laboratory testing as a means of determining the N status within wheat. Currently, hyperspectral vegetation indices (VIs) and linear nonparametric regression are the primary tools for monitoring the N status of wheat. Machine learning algorithms have been increasingly applied to model the nonlinear relationship between spectral data and wheat N status. This study is a comprehensive review of available N-related hyperspectral VIs and aims to inform the selection of VIs under field conditions. The combination of feature mining and machine learning algorithms is discussed as an application of hyperspectral imaging systems. We discuss the major challenges and future directions for evaluating and assessing wheat N status. Finally, we suggest that the underlying mechanism of protein formation in wheat grains as determined by using hyperspectral imaging systems needs to be further investigated. This overview provides theoretical and technical support to promote applications of hyperspectral imaging systems in wheat N status assessments; in addition, it can be applied to help monitor and evaluate food and nutrition security.

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

Estimation of soil copper content in mining area using ZY1-02D satellite hyperspectral data

The content of soil heavy metal elements such as copper (Cu) is a crucial indicator for soil environmental monitoring and evaluation. However, traditional field sampling and laboratory chemical analysis methods are time-consuming and labor-intensive. The newly launched ZY1-02D satellite has a large imaging width, nanometer-level spectral resolution, and almost continuous spectral curve, providing a promising means for large-scale rapid estimation of soil heavy metal content. For the first time, our study explores the application potential of ZY1-02D satellite hyperspectral data in estimating soil Cu content in mining areas. A comprehensive approach combining correlation analysis, feature selection, and a regression model is proposed to predict the soil Cu content. First, the correlations between sampling soil Cu content and spectral features (including dozens of spectral indices and different transformed spectra calculated based on the pixel spectrum) were analyzed. Then the random frog algorithm was chosen to reduce the spectral features’ dimensionality and select the proper predictors. Several regression models of soil Cu content, including Gaussian process regression (GPR), boosted trees, bagged trees, and support vector machine, were established using these predictors. The model comparison shows that the GPR model has the best agreement with the sampling data. Furthermore, the accuracy of the GPR model with different numbers of predictors was also analyzed. The results show that the proposed estimation approach based on correlation analysis, random frog algorithm, and GPR model has a good performance compared to sampling data (R2 = 0.83, adjusted R2 = 0.81, RMSE = 364.61 mg / kg, and RPD = 1.83). And the results further indicate the great potential of AHSI/ZY1-02D hyperspectral data in predicting soil Cu content in mining areas.

Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry

Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs > 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures.

New Method for Automated Quantification of Vertical Spatio-Temporal Changes within Gully Cross-Sections Based on Very-High-Resolution Models

Gully erosion is one of the most prominent natural denudation processes of the Mediterranean. It causes significant soil degradation and sediment yield. Most traditional field methods for measurement of erosion-induced spatio-temporal changes are time and labor consuming, while their accuracy and precision are highly influenced by various factors. The main research question of this study was how the measurement approach of traditional field sampling methods can be automated and upgraded, while satisfying the required measurement accuracy. The VERTICAL method was developed as a fully automated raster-based method for detection and quantification of vertical spatio-temporal changes within a large number of gully cross-sections (GCs). The developed method was tested on the example of gully Santiš, located at Pag Island, Croatia. Repeat unmanned aerial vehicle (UAV) photogrammetry was used, as a cost-effective and practical method for the creation of very-high-resolution (VHR) digital surface models (DSMs) of the chosen gully site. A repeat aerophotogrammetric system (RAPS) was successfully assembled and integrated into one functional operating system. RAPS was successfully applied for derivation of interval (the two-year research period) DSMs (1.9 cm/pix) of gully Santiš with the accuracy of ±5 cm. VERTICAL generated and measured 2379 GCs, along the 110 m long thalweg of gully Santiš, within which 749 052 height points were sampled in total. VERTICAL proved to be a fast and reliable method for automated detection and calculation of spatio-temporal changes in a large number of GCs, which solved some significant shortcomings of traditional field methods. The versatility and adaptability of VERTICAL allow its application for other, similar scientific purposes, where multitemporal accurate measurement of spatio-temporal changes in GCs is required (e.g., river material dynamics, ice mass dynamics, tufa sedimentation and erosion).

Estimation and Spatial Analysis of Heavy Metals in Metal Tailing Pond Based on Improved PLS With Multiple Factors

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.