Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

Between flood and drought: How cities are facing water surplus and scarcity

Droughts and floods are weather-related hazards affecting cities in all climate zones and causing human deaths and material losses on all inhabited continents. The aim of this article is to review, analyse and discuss in detail the problems faced by urban ecosystems due to water surplus and scarcity, as well as the need of adaptation to climate change taking into account the legislation, current challenges and knowledge gaps. The literature review indicated that urban floods are much more recognised than urban droughts. Amongst floods, flash floods are currently the most challenging, which by their nature are difficult to monitor. Research and adaptation measures related to water-released hazards use cutting-edge technologies for risk assessment, decision support systems, or early warning systems, among others, but in all areas knowledge gaps for urban droughts are evident. Increasing urban retention and introducing Low Impact Development and Nature-based Solutions is a remedy for both droughts and floods in cities. There is the need to integrate flood and drought disaster risk reduction strategies and creating a holistic approach.

Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects

Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.

Monitoring early-season agricultural drought using temporal Sentinel-1 SAR-based combined drought index

Early-season agricultural drought is frequent over South Asian region due to delayed or deficient monsoon rainfall. These drought events often cause delay in sowing and can even result in crop failure. The present study focuses on monitoring early-season agricultural drought in a semi-arid region of India over 5-year period (2016-2020). It utilizes hydro-climatic and biophysical variables to develop a combined drought index (CDI), which integrates anomalies in soil moisture conditions, rainfall, and crop-sown area progression. Synthetic aperture radar (SAR)-based soil moisture index (SMI) represents in situ measured soil moisture with reasonable accuracy (r=0.68). Based on the highest F1-score, SAR backscatter in VH (vertical transmit-horizontal receive) polarization with specific values for parameter threshold (-18.63 dB) and slope threshold (-0.072) is selected to determine the start of season (SoS) with a validation accuracy of 73.53%. The CDI approach is used to monitor early-season agricultural drought and identified drought conditions during June-July in 2019 and during July in 2018. Conversely, 2020 experienced consistently wet conditions, while 2016 and 2017 had near-normal conditions. Overall, the study highlights the use of SAR data for early-season agricultural drought monitoring, which is mainly governed by soil moisture-driven crop-sowing progression. The proposed methodology holds potential for effective monitoring, management, and decision-making in early-season agricultural drought scenarios.

A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland

Soil moisture plays an important role in ecology, hydrology, agriculture and climate change. This study proposes a soil moisture prediction model, based on the depth and water balance equation, which integrates the water balance equation with the seasonal ARIMA model, and introduces the depth parameter to consider the soil moisture at different depths. The experimental results showed that the model proposed in this study was able to provide a higher prediction accuracy for the soil moisture at 40 cm, 100 cm and 200 cm depths, compared to the seasonal ARIMA model. Different models were used for different depths. In this study, the seasonal ARIMA model was used at 10 cm, and the proposed model was used at 40 cm, 100 cm and 200 cm, from which more accurate prediction values could be obtained. The fluctuation of the predicted data has a certain seasonal trend, but the regularity decreases with the increasing depth until the soil moisture is almost independent of the external influence at a 200 cm depth. The accurate prediction of the soil moisture can contribute to the scientific management of the grasslands, thus promoting ecological stability and the sustainable development of the grasslands while rationalizing land use.

Using remote sensing to identify liquid manure applications in eastern North Carolina

Nutrient pollution from farm fertilizers and manure is a global concern. Excess nitrogen and phosphorous has been linked to algal blooms and a host of other water quality issues. In the U.S., most animal production occurs in concentrated animal feeding operations (CAFOs) housing a significant number of animals in a confined space. CAFOs tend to cluster in space and thus generate large quantities of manures within a small area. Liquid manure from CAFOs is often stored in open-air lagoons and then applied via irrigation to crops on nearby 'sprayfields'. The full scope and extent of CAFO impacts remain unclear because of the paucity of public information regarding animal numbers, barn and lagoon locations, and manure management practices. Where and when manure is applied on the landscape is key missing data that is needed to better understand and mitigate consequences of CAFO management practices. The aim of this study was to detect land applications of liquid manure using a remote sensing approach. We used random forest models incorporating C-Band synthetic-aperture radar, multispectral imagery, and other predictors to examine soil moisture conditions indicating probable liquid manure applications across known sprayfields in eastern North Carolina. Our models successfully distinguished saturated and unsaturated soils within corn, soybean, grassland, and 'other' crops, with 93-98% accuracy against validation for clear weather periods during the dormant, early, and late growing seasons. A Kruskal-Wallis test revealed that the mean soil saturation frequency was significantly higher on sprayfields than non-sprayfields of the same crop type (p < 2.2e-16). We also found that manure applications were concentrated within ∼1 km from the point of generation. This is the first application of satellite-based radar for identifying the location and timing of manure applications over broad areas. Future work can build on these methods to further understand manure management at CAFOs, as well as to improve pollution source tracking and modeling.

Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem

Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions.

Sentinel-1 SAR Backscatter Response to Agricultural Drought in The Netherlands

Drought is a major natural hazard that impacts agriculture, the environment, and socio-economic conditions. In 2018 and 2019, Europe experienced a severe drought due to below average precipitation and high temperatures. Drought stress affects the moisture content and structure of agricultural crops and can result in lower yields. Synthetic Aperture Radar (SAR) observations are sensitive to the dielectric and geometric characteristics of crops and underlying soils. This study uses data from ESA’s Sentinel-1 SAR satellite to investigate the influence of drought stress on major arable crops of the Netherlands, its regional variability and the impact of water management decisions on crop development. Sentinel-1 VV, VH and VH/VV backscatter data are used to quantify the variability in the spatio-temporal dynamics of agricultural crop parcels in response to drought. Results show that VV and VH backscatter values are 1 to 2 dB lower for crop parcels during the 2018 drought compared to values in 2017. In addition, the growth season indicated by the cross-ratio (CR, VH/VV) for maize and onion is shorter during the drought year. Differences due to irrigation restrictions are observed in backscatter response from maize parcels. Lower CR values in 2019 indicate the impact of drought on the start of the growing season. Results demonstrate that Sentinel-1 can detect changes in the seasonal cycle of arable crops in response to agricultural drought.

Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800–1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Mapping in the Free State Province, South Africa

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.

Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

Improving Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images

Co-registering the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data of the European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then, the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three-dimensional (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Lastly, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and we compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the third-order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10 m pixel, that of the hilly areas is about 1.5 10 m pixels, and that of the mountainous areas is between 1.7 and 2.3 10 m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.

Impacts of Climate Variability and Drought on Surface Water Resources in Sub-Saharan Africa Using Remote Sensing: A Review

Climate variability and recurrent droughts have caused remarkable strain on water resources in most regions across the globe, with the arid and semi-arid areas being the hardest hit. The impacts have been notable on surface water resources, which are already under threat from massive abstractions due to increased demand, as well as poor conservation and unsustainable land management practices. Drought and climate variability, as well as their associated impacts on water resources, have gained increased attention in recent decades as nations seek to enhance mitigation and adaptation mechanisms. Although the use of satellite technologies has, of late, gained prominence in generating timely and spatially explicit information on drought and climate variability impacts across different regions, they are somewhat hampered by difficulties in detecting drought evolution due to its complex nature, varying scales, the magnitude of its occurrence, and inherent data gaps. Currently, a number of studies have been conducted to monitor and assess the impacts of climate variability and droughts on water resources in sub-Saharan Africa using different remotely sensed and in-situ datasets. This study therefore provides a detailed overview of the progress made in tracking droughts using remote sensing, including its relevance in monitoring climate variability and hydrological drought impacts on surface water resources in sub-Saharan Africa. The paper further discusses traditional and remote sensing methods of monitoring climate variability, hydrological drought, and water resources, tracking their application and key challenges, with a particular emphasis on sub-Saharan Africa. Additionally, characteristics and limitations of various remote sensors, as well as drought and surface water indices, namely, the Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Normalized Difference Vegetation (NDVI), Vegetation Condition Index (VCI), and Water Requirement Satisfaction Index (WRSI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Land Surface Water Index (LSWI+5), Modified Normalized Difference Water Index (MNDWI+5), Automated Water Extraction Index (shadow) (AWEIsh), and Automated Water Extraction Index (non-shadow) (AWEInsh), and their relevance in climate variability and drought monitoring are discussed. Additionally, key scientific research strides and knowledge gaps for further investigations are highlighted. While progress has been made in advancing the application of remote sensing in water resources, this review indicates the need for further studies on assessing drought and climate variability impacts on water resources, especially in the context of climate change and increased water demand. The results from this study suggests that Landsat-8 and Sentinel-2 satellite data are likely to be best suited to monitor climate variability, hydrological drought, and surface water bodies, due to their availability at relatively low cost, impressive spectral, spatial, and temporal characteristics. The most effective drought and water indices are SPI, PDSI, NDVI, VCI, NDWI, MNDWI, MNDWI+5, AWEIsh, and AWEInsh. Overall, the findings of this study emphasize the increasing role and potential of remote sensing in generating spatially explicit information on drought and climate variability impacts on surface water resources. However, there is a need for future studies to consider spatial data integration techniques, radar data, precipitation, cloud computing, and machine learning or artificial intelligence (AI) techniques to improve on understanding climate and drought impacts on water resources across various scales.

Evaluation of Different Radiative Transfer Models for Microwave Backscatter Estimation of Wheat Fields

This study aimed to analyze existing microwave surface (Oh, Dubois, Water Cloud Model “WCM”, Integral Equation Model “IEM”) and canopy (Water Cloud Model “WCM”, Single Scattering Radiative Transfer “SSRT”) Radiative Transfer (RT) models and assess advantages and disadvantages of different model combinations in terms of VV polarized radar backscatter simulation of wheat fields. The models are driven with field measurements acquired in 2017 at a test site near Munich, Germany. As vegetation descriptor for the canopy models Leaf Area Index (LAI) was used. The effect of empirical model parameters is evaluated in two different ways: (a) empirical model parameters are set as static throughout the whole time series of one growing season and (b) empirical model parameters describing the backscatter attenuation by the canopy are treated as non-static in time. The model results are compared to a dense Sentinel-1 C-band time series with observations every 1.5 days. The utilized Sentinel-1 time series comprises images acquired with different satellite acquisition geometries (different incidence and azimuth angles), which allows us to evaluate the model performance for different acquisition geometries. Results show that total LAI as vegetation descriptor in combination with static empirical parameters fit Sentinel-1 radar backscatter of wheat fields only sufficient within the first half of the vegetation period. With the saturation of LAI and/or canopy height of the wheat fields, the observed increase in Sentinel-1 radar backscatter cannot be modeled. Probable cause are effects of changes within the grains (both structure and water content per leaf area) and their influence on the backscatter. However, model results with LAI and non-static empirical parameters fit the Sentinel-1 data well for the entire vegetation period. Limitations regarding different satellite acquisition geometries become apparent for the second half of the vegetation period. The observed overall increase in backscatter can be modeled, but a trend mismatch between modeled and observed backscatter values of adjacent time points with different acquisition geometries is observed.

Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline

Increased frequency of tree mortality and forest decline due to anomalous drought events calls for the adoption of effective monitoring of tree water status over large spatial and temporal scales. We correlated field-measured and remotely sensed plant water status parameters, to test the possibility of monitoring the risk of drought-induced dehydration and hydraulic failure using satellite images calibrated on reliable physiological indicators of tree hydraulics. The study was conducted during summer 2019 in the Karst plateau (NE Italy) in a woodland dominated by Fraxinus ornus L.; Sentinel-2 images were acquired on a seasonal scale on the same dates when absolute water content (AbWC), relative water content (RWC), and minimum water potential (Ψmin) were measured in the field. Plant water status parameters were correlated with normalized difference vegetation index (NDVI and NDVI 8A), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI). Significant Pearson and Spearman linear correlations (α < 0.05) emerged between all tree-level measured variables and NDWI, while for NDVI, NDVI 8A, and SAVI no correlation was found. Our results suggest the possibility of using the NDWI as a proxy of tree water content and water potential.

Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Estimating late spring frost-induced growth anomalies in European beech forests in Italy

Weather extremes and extreme climate events, like late spring frosts, are expected to increase in frequency and duration during the next decades. Although spring phenology of European beech is well adapted to escape freeze damages on longer time scales, the effects of occasional late spring frosts (LSF) are among the main climatic damages to these forests to such an extent that they limit beech distribution and elevation range, especially at its southern margin. The aim of this work was to evaluate the short-term effects of two consecutive LSF events occurred in 2016 and 2017 in Italy on the beech forest vegetation activity. Remotely sensed land surface temperature (LST) data were used to detect the pixels where LSF occurred, while enhanced vegetation index (EVI) data were used to quantify LSF effects by computing a spring vegetation activity anomaly index (sAI). In 2016 and 2017, the LSF covered, respectively, about 29% and 32% of the total Italian beech-dominated area. The two LSF widely differed in their spatial patterns and their effects. In 2016, the pixels belonging to the sAI classes with the highest spring anomalies were also those where prolonged LSF occur, while, in 2017, the pixels belonging to the highest sAI classes were those that underwent the shorter (but probably more intense) LSF events. Under scenarios of increased frequency risk of repeated LSF, the proposed methodology may represent an automatic and low-cost tool both for monitoring and predicting European beech growth patterns.

Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa

Drought limits the production of plantation forests, notably in the drought-prone Zululand region of South Africa. During the last 40 years, the country has faced a series of severe droughts, however that of 2015 stands out as the most extreme and prolonged. The 2015 drought impaired forest productivity and led to widespread tree mortality in this region, but the identification of tree response to drought stress remains uncertain because of its spatial variability. To address this problem, a method that can capture drought patterns and identify trees with similar reactions to drought stress is desired. This could improve the accuracy of detecting trees suffering from drought stress which is key for forest management planning. In this study, we aimed to evaluate the utility of unsupervised mapping approaches in compartments of Eucalyptus trees with similar drought characteristics based on the Normalized Difference Water Index (NDWI) and to demonstrate the value of cloud-based Google Earth Engine (GEE) resources for rapid landscape drought monitoring. Our results showed that calculating distances between pixels using three different matrices (Random Forest (RF) proximity, Euclidean and Manhattan) can accurately detect similarities within a dataset. The RF proximity matrix produced the best measures, which were clustered using Wards hierarchical clustering to detect drought with the highest overall accuracy of 87.7%, followed by Manhattan (85.9%) and Euclidean similarity measures (79.9%), with user and producer results between 84.2% to 91.2%, 42.8% to 98.2% and 37.2% to 94.7%, respectively. These results confirm the value of the RF proximity matrix and underscore the capability of automatic unsupervised mapping approaches for monitoring drought stress in tree plantations, as well as the value of using GEE for providing cost effective datasets to resource stricken countries.