IMPACT OF THE PANDEMIC COVID-19 TO THE INDONESIA SEAS

This paper presents the effect of the COVID-19 pandemic on the Indonesian seas from April to October 2020. Data were mainly obtained through literature studies focusing on coastal and ecosystem services, noise observation in the ocean, and in-situ data for atmospheric conditions. The results of this study found that the pandemic has given the oceans and ecosystems time to recover from anthropogenic stresses even though the tourism and fisheries sectors have experienced strong economic shocks. A decrease in the amount of pollution in several major cities in Indonesia was also found during the pandemic period.

Temporal change in seismic wave attenuation using highly stable vibration sources

We developed a method to detect attenuation changes during seismic wave propagation excited by precisely controlled artificial seismic sources, namely Accurately Controlled Routinely Operated Signal System (ACROSS), and applied it to monitor the temporal changes for in situ data collected by previous studies. Our method, together with the use of the ACROSS sources, is less susceptible to noise level changes, from which conventional methods such as envelope calculation suffer. The method utilizes the noise level that is independently estimated in the frequency domain and eliminates the influence of the noise from the observed signal. For performance testing, we applied this method to a dataset that was obtained in an experiment at Awaji Island, Japan, from 2000 to 2001. We detected a change in amplitude caused by rainfall, variation in atmospheric temperature, and coseismic ground motions. Among them, coseismic changes are of particular interest because there are limited studies on coseismic attenuation change, in contrast to many studies on coseismic velocity decrease. At the 2000 Western Tottori earthquake (MW = 6.6, epicenter distance of 165 km), a sudden decrease in amplitude of up to 5% was observed. The coseismic amplitude reduction and its anisotropic characteristics, which showed a larger reduction in the direction of the major axis of velocity decrease, were consistent with the opening of fluid-filled cracks, as proposed by previous studies. The $$\Delta {Q}^{-1}$$ Δ Q - 1 corresponding to the amplitude change gives similar values to those reported in previous studies using natural earthquakes. Graphical Abstract

Improving the Numerical Modelling of In-Situ Rock Bolts Using Axial and Bending Strain Data from Instrumented Bolts

Numerical modelling has become an important tool in the underground rock bolt reinforcement designing process. Numerical modelling provides the advantage of easily and quickly simulating complex underground geometries and mechanisms with sensitivity analyses. However, a numerical model needs to be calibrated using mathematical solutions, lab testing or with actual in-situ observations and measurements (which is the preferred method) before its results can be quantitatively applied to reinforcement design. Instrumented rock bolts provide a useful data source for calibrating in-situ rock bolt models. In this work, procedures have been presented to identify and determine the orientation of structures in the rock mass based on the strains on the instrumented rock bolts. A method to calibrate the rock bolt model with in-situ data is also presented. The results of the presented procedures have been validated with laboratory tests and numerical modelling. The procedures have been applied to create and calibrate an in-situ rock bolt model in FLAC3D and the results are validated using in-situ data.

Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.

Hydrodynamic and Biochemical Impacts on the Development of Hypoxia in the Louisiana–Texas Shelf Part I: Numerical Modeling and Hypoxia Mechanisms

A three-dimensional coupled hydrodynamic–biogeochemical model with N, P, Si cycles and multiple phytoplankton and zooplankton functional groups was developed and applied to the Gulf of Mexico to study bottom dissolved oxygen dynamics. A 15-year hindcast was achieved covering the period of 2006–2020. Extensive model validation against in situ data demonstrates that the model is capable of reproducing vertical distributions of dissolved oxygen (DO), frequency distributions of hypoxia thickness, spatial distributions of bottom DO concentration and interannual variations of hypoxic area. The impacts of river plume and along-shore currents on bottom DO dynamics were examined based on multiyear bottom DO climatology, the corresponding long-term trends, and interannual variability. Model results suggest that mechanisms of bottom hypoxia developments are different between the west and east Louisiana–Texas Shelf waters. The mid-Atchafalaya nearshore (10–20 m) region firstly suffers from hypoxia in May, followed by the west-Mississippi nearshore region in June. Hypoxic waters expand in the following months and eventually merge in August. Sediment oxygen consumption (SOC) and water stratification (measured by potential energy anomaly, PEA) are two main factors modulating the variability of bottom DO concentration. Generalized Boosted Regression Models provide analysis of the relative importance of PEA and SOC. The analysis indicates that SOC is the main regulator in nearshore regions, and water stratification outcompetes the sedimentary biochemical processes in the offshore (20–50 m) regions. A strong quadratic relationship was found between hypoxic volume and hypoxic area, which suggests that the volume mostly results from the low DO in bottom water and can be potentially estimated based on the hypoxic area.

The Gavkhouni Wetland Dryness and Its Impact on Air Temperature Variability in the Eastern Part of the Zayandeh-Rud River Basin, Iran

The Gavkhouni wetland provides many environmental and economic benefits for the central region of Iran. In recent decades, it has completely dried up several times with substantial impacts on local ecosystems and climate. Remote sensing-based Land Surface Temperature (LST), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) in combination with in-situ data were used to investigate the trend of the Gavkhouni wetland dryness and the associated impact on the variability of local air temperature. The results indicate that the wetland has increasingly experienced drier conditions since the year 2000. The wetland was almost completely dry in 2009, 2011, 2015 and 2017. In addition, the results show that Gavkhouni wetland dryness has a significant impact on local climate, increasing the mean seasonal air temperature by ~1.6 °C and ~1 °C in spring and summer, respectively. Overall, this study shows that remote sensing imagery is a valuable source for monitoring dryness and air temperature variations in the region. Moreover, the results provide a basis for effective water allocation decisions to maintain the hydrological and ecological functionality of the Gavkhouni wetland. Considering that many factors such as latitude, cloud cover, and the direction of prevailing winds affect land surface and air temperatures, it is suggested to use a numerical climate model to improve a regional understanding of the effects of wetland dryness on the surrounding climate.

Ethnobotanical Wealth of Home Gardens

The use of ethnomedicinal plants of home gardens has played an important role in treating minor illnesses related to physical and psychological wellbeing among rural folks. Local healers often use plants from their home garden or nearby forest. In this chapter, the traditional uses of medicinal plants using various tools with sample data have been used to facilitate ethnobotanical research on home gardens. Samples measuring the relative importance of species through quantitative analysis are tabulated. These tools, if applied during in-situ data collection, would help generate reliable information on characteristics of home gardens and medicinal plants. Conservation of medicinal plants from the perspective of home gardening is important. To document how medicinal plants are used to treat different diseases, their use value and ethical considerations would be significant for bioprospecting of medicinal plants and protecting intellectual right of the associated traditional knowledge.

Disaggregation of SMAP Soil Moisture at 20 m Resolution: Validation and Sub-Field Scale Analysis

This paper introduces a modified version of the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) algorithm to disaggregate an SMAP surface soil moisture (SSM) product at a 20 m spatial resolution, through the use of sharpened Sentinel-3 land surface temperature (LST) data. Using sharpened LST as a high resolution proxy of SSM is a novel approach that needs to be validated and can be employed in a variety of applications that currently lack in a product with a similar high spatio-temporal resolution. The proposed high resolution SSM product was validated against available in situ data for two different fields, and it was also compared with two coarser DisPATCh products produced, disaggregating SMAP through the use of an LST at 1 km from Sentinel-3 and MODIS. From the correlation between in situ data and disaggregated SSM products, a general improvement was found in terms of Pearson’s correlation coefficient (R) for the proposed high resolution product with respect to the two products at 1 km. For the first field analyzed, R was equal to 0.47 when considering the 20 m product, an improvement compared to the 0.28 and 0.39 for the 1 km products. The improvement was especially noticeable during the summer season, in which it was only possible to successfully capture field-specific irrigation practices at the 20 m resolution. For the second field, R was 0.31 for the 20 m product, also an improvement compared to the 0.21 and 0.23 for the 1 km product. Additionally, the new product was able to depict SSM spatial variability at a sub-field scale and a validation analysis is also proposed at this scale. The main advantage of the proposed product is its very high spatio-temporal resolution, which opens up new opportunities to apply remotely sensed SSM data in disciplines that require fine spatial scales, such as agriculture and water management.

A close up of daily temperature and moisture in two Mexican high-elevation forests

The scarcity of meteorological stations and the strong need for climatic information in alpine forests require the use of large-scale climatic algorithms but the lack of in situ information produces high uncertainty on their suitability. In this study, we used linear mixed models to study the topographic effect (elevation and aspect) and time variations (from hourly to monthly) on temperature (T) and relative humidity (RH) with a 5-year instrumental database. Furthermore, we compared climatic information from a geographical algorithm and our in-situ data. Our data covered two mountains (Tláloc-TLA and Jocotitlán-JOC, State of México), four elevation belts (from 3500 m to 3900 m a.s.l.), and two aspects (Northwest and Southwest). We found differences for average temperature (TLA = 7.56 °C ± 0.03 °C and JOC = 6.98 °C ± 0.02 °C), and relative humidity between mountains (TLA = 69.3% ± 0.12% and JOC = 72.5% ± 0.13%,). The most significant variables explaining T were the elevation (Δ= -0.36 °C by 100 m) and aspect, while the aspect was relevant for RH. May was the warmest month (9.50 °C ± 0.10 °C for average temperature) while September the wettest for both mountains (85.1% ± 0.30% and 87.4% ± 0.25 % RH, respectively). Despite the higher correlations between climatic sources (up to r = 0.83), the geographical algorithm overestimates T and underestimates RH. We propose that when climatic information from geographical algorithms is used in alpine forests, calibrations are needed whenever possible with in situ information.

Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries

The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These quality-controlled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP (Jolivot et al., 2021).