A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R2 (PR2): 0.60–0.70, R2: 0.70–0.80, Nash Sutcliffe efficiency (NS): 0.65–0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments.

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

CLASSIFICATION OF TRANSLATIONAL LANDSLIDE ACTIVITY USING VEGETATION ANOMALIES INDICATOR (VAI) IN KUNDASANG, SABAH

This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities; (ii) tree canopy gap; (iii) density of different layer of vegetation; (iv) vegetation type distribution; (v) vegetation indices (VIs); (vi) root strength index (RSI); and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4–86%, and kappa is between 0.335–0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8–71.3%, and kappa is between 0.243–0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia.

WOMBAT v1.0: a fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Wetland loss in the Ñeembucú Wetlands Complex, Paraguay, using remote sensing

South American wetlands are of global importance, yet limited delineation and monitoring restricts informed decision-making around the drivers of wetland loss. A growing human population and increasing demand for agricultural products has driven wetland loss and degradation in the Neotropics. Understanding of wetland dynamics and land use change can be gained through wetland monitoring. The Ñeembucú Wetlands Complex is the largest wetland in Paraguay, lying within the Paraguay-Paraná-La Plata River system. This study aims to use remotely sensed data to map land cover between 2006 and 2021, quantify wetland change over the 15-year study period and thus identify land cover types vulnerable to change in the Ñeembucú Wetlands Complex. Forest, dryland vegetation, vegetated wetland and open water were identified using Random Forest supervised classifications trained on visual inspection data and field data. Annual change of -0.34, 4.95, -1.65, 0.40 was observed for forest, dryland, vegetated wetland and open water, respectively. Wetland and forest conversion is attributed to agricultural and urban expansion. With ongoing pressures on wetlands, monitoring will be a key tool for addressing change and advising decision-making around development and conservation of valuable ecosystem goods and services in the Ñeembucú Wetlands Complex.

Potential Impact of Biostimulator NeOsol and Three Different Manure Types on Physical Soil Properties and Crop Status in Heavy Soils Conditions

This study was conducted to understand the long-term influence of biostimulator NeOsol in combination with different manure types on soil’s physical properties and crop status. NeOsol is a soil biostimulator that should stimulate the biological reactions of the soil profile and improve the soil’s physical and chemical properties. A six-year experiment was conducted with eight treatments: NPK, cattle manure, pig manure, poultry manure, and the same four treatments with the NeOsol added on top. The in situ sampling of soil properties provided data on unit draft (UD), bulk density (BD), and saturated hydraulic conductivity (SHC). Furthermore, remotely sensed data were analyzed to describe crop status via three selected vegetation indices (VI), and crop yields were assessed last. The variants treated with NeOsol demonstrated decreases in UD over time; BD, SHC, and VI did not significantly change. The impact on yield was significant and increased over time. When comparing the variants with manure application to those without one, the cattle manure led to significantly higher SHC; the pig manure led to significantly lower UD and BD but significantly higher SHC and yield; and the poultry manure led to significantly lower UD and BD but higher yield.