An investigation into remote sensing techniques and field observations for modeling of dynamic hydraulic roughness from riparian vegetation

Riparian vegetation provides many noteworthy functions in river and floodplain systems including its influence on hydrodynamic processes. Traditional methods for predicting hydrodynamic characteristics in the presence of vegetation involve the application of static roughness ( n) values, which neglect changes in roughness due to local flow characteristics. The objectives of this study were to: (1) implement numerical routines for simulating dynamic hydraulic roughness ( n) in a two-dimensional (2D) hydrodynamic model; (2) evaluate the performance of two dynamic roughness approaches; and (3) compare vegetation parameters and hydrodynamic model results based on field-based and remote sensing acquisition methods. A coupled vegetation-hydraulic solver was developed for a 2D hydraulics model using two dynamic approaches, which required vegetation parameters to calculate spatially distributed, dynamic roughness coefficients. Vegetation parameters were determined by field survey and using airborne LiDAR data. Water surface elevations modeled using conventional and the proposed dynamic approaches produced similar profiles. The method demonstrates the suitability in modeling the system where there is no calibration data. Substantial spatial variations in both n and hydraulic parameters were observed when comparing the static and dynamic approaches. Thus, the method proposed here is beneficial for describing the hydraulic conditions for the area having huge variation of vegetation. The proposed methods have the potential to improve our ability to simulate the spatial and temporal heterogeneity of vegetated floodplain surfaces with an approach that is more physically-based and reproducible than conventional “look up” approaches. However, additional research is needed to quantify model performance with respect to spatially distributed flow properties and parameterization of vegetation characteristics.

Habitat type impacts small mammal diversity in the Ukaguru Mountains, Tanzania

We assessed the habitat association of the diversity and abundance of small mammals in the Ukaguru Mountains within the Eastern Arc Mountains, Tanzania. An estimation of the vegetation parameters and live-trapping of small mammals for three consecutive nights per month for 21 months were carried out in farmland, disturbed and intact forests. A total of 1196 individual small mammals comprising 13 species were captured. Species diversity and evenness in intact forest were much higher compared to disturbed forest. Principal component analysis (PCA) explained 87.7% of the variance with two factors. The study suggests vegetation and habitat disturbances are factors responsible for the observed diversity of small mammals in the Ukaguru Mountains. Mastomys natalensis, Mus triton and Praomys delectorum accounted for 90.0% of total captures. M. triton and M. natalensis were the most abundant in farmland with 46.8 and 42.8% of total captures respectively and associated with the herbaceous vegetation. P. delectorum correlated with litter depth and trees and accounted for 90.0 and 80.0% total captures in disturbed and intact forests respectively but the mean abundances in both habitats were not significantly different (p = 0.72).

Effects of Plant Crown Shape on Microwave Backscattering Coefficients of Vegetation Canopy

A microwave scattering model is a powerful tool for determining relationships between vegetation parameters and backscattering characteristics. The crown shape of the vegetation canopy is an important parameter in forestry and affects the microwave scattering modeling results. However, there are few numerical models or methods to describe the relationships between crown shapes and backscattering features. Using the Modified Tor Vergata Model (MTVM), a microwave scattering model based on the Matrix Doubling Algorithm (MDA), we quantitatively characterized the effects of crown shape on the microwave backscattering coefficients of the vegetation canopy. FEKO was also used as a computational electromagnetic method to make a complement and comparison with MTVM. In a preliminary experiment, the backscattering coefficients of two ideal vegetation canopies with four representative crown shapes (cylinder, cone, inverted cone and ellipsoid) were simulated: MTVM simulations were performed for the L (1.2 GHz), C (5.3 GHz) and X (9.6 GHz) bands in fully polarimetric mode, and FEKO simulations were carried out for the C (5.3 GHz) band at VV and VH polarization. The simulation results show that, for specific input parameters, the mean relative differences in backscattering coefficients due to variations in crown shape are as high as 127%, which demonstrates that the crown shape has a non-negligible influence on microwave backscattering coefficients of the vegetation canopy. In turn, this also suggests that investigation on effects of plant crown shape on microwave backscattering coefficients may have the potential to improve the accuracy of vegetation microwave scattering models, especially in canopies where volume scattering is the predominant mechanism.

Woodland Extraction Rate Estimation in the Savanna Ecosystem (Case Study of Foley and Makomoto in Central-East Botswana)

Savanna woodlands make a significant contribution to rural households’ livelihoods, providing a wide range of resources as well as generating income. However, the sustainability of the woodland ecosystem is generally affected by the human harvesting intensities. This study aimed at quantifying the woodland resource extraction rates and its effects on woodland structure and density. Data were collected from households using a semi-structured questionnaire, key informant interviews, and from 128 rectangular plots measuring 30 m × 30 m. Vegetation parameters, such as tree density, tree height, diameter at breast height, species, stump density, and stump diameter, were recorded. The results of the study revealed that a total of 649 woody stumps were recorded, with an average of 56 trees removed per hectare. The findings showed that the intensities of stumps varied widely, with a high number of stumps recorded at a distance of 10–15 km from the settlements, where major land-use activities are fuelwood extraction, cultivation, and livestock farming. The results also showed that the increased commercial fuelwood production led to cutting of large mopane trees, which is an indicator of unsustainable harvesting. Based on the harvest rates provided by the respondents, results showed that large quantities of fuelwood were harvested for trading, which could put pressure on the woodland ecosystem, consequently resulting in woodland degradation. This study provides forest and range resource managers with valuable information on the quantities of stumps as an indication of tree removals and could be useful in developing effective monitoring strategies and promoting sustainable forest and woodland management.

Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields

The release of high-spatiotemporal-resolution Sentinel-1 Synthetic Aperture Radar (SAR) data to the public has provided an unprecedented opportunity to map soil moisture at watershed and agricultural field scales. However, the existing retrieval algorithms fail to derive soil moisture with expected accuracy. Insufficient understanding of the effects of soil and vegetation parameters on the backscatters is an important reason for this failure. To this end, we present a Sensitivity Analysis (SA) to quantify the effects of parameters on the dual-polarized backscatters of Sentinel-1 based on a Water Cloud Model (WCM) and multiple global SA methods. The identification of the incidence angle and polarization of Sentinel-1 and the description scheme of vegetation parameters (A, B and α) in WCM are especially emphasized in this analysis towards an optimal estimation of parameters. Multiple SA methods derive identical parameter importance ranks, indicating that a highly reasonable and reliable SA is performed. Comparison between two existing vegetation description schemes shows that the scheme using Vegetation Water Content (VWC) outperforms the scheme combing particle moisture content and VWC. Surface roughness, soil moisture, VWC, and B, are most sensitive on the backscatters. Variation of parameter sensitivity indices with incidence angle at different polarizations indicates that VV- and VH- polarized backscatters at small incidence angles are the optimal options for soil moisture and surface roughness estimation, respectively, while VV-polarized backscatter at larger incidence angles is well-suited for VWC and B estimation and HH-polarized backscatter is well suited for roughness estimation. This analysis improves the understanding of the effects of vegetated surface parameters on multi-angle and multi-polarized backscatters of Sentinel-1 SAR, informing improvement in SAR-based soil moisture retrieval.

Root density drives aggregate stability of soils of different moraine ages in the Swiss Alps

Aims The stability of hillslopes is an essential ecosystem service, especially in alpine regions with soils prone to erosion. One key variable controlling hillslope stability is soil aggregate stability. We aimed at identifying dominant controls of vegetation parameters on aggregate stability and analysed their importance for soil aggregate stability during landscape development. Methods We quantified the aggregate stability coefficient (ASC) and measured plant cover, diversity, root mass and root length, density (RMD, RLD) along two chronosequences with contrasting bedrocks (siliceous, calcareous) in the Swiss Alps. Results We found that ASC developed slower along the calcareous chronosequence. Furthermore, we observed a significant positive effect of vegetation cover and diversity on ASC that was mediated via root density. These relationships developed in a time-depended manner: At young terrain ages, vegetation parameters had a strong effect on aggregate stability compared to older stages. Moreover, RLD was the most powerful predictor of ASC on young terrain, whereas on older moraines RMD became more important. Conclusions We highlight that root density plays a major role in governing ASC for soils differing in moraine ages. The changing importances of RLD and RMD for ASC development suggest different mechanistic linkages between vegetation and hillsope stability during landscape development.

A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

Hydrologic models predict the spatial and temporal distribution of water and energy at the land surface. Currently, parameter availability limits global-scale hydrologic modelling to very coarse resolution, hindering researchers from resolving fine-scale variability. With the aim of addressing this problem, we present a set of globally consistent soil and vegetation parameters for the Variable Infiltration Capacity (VIC) model at 1/16° resolution (approximately 6 km at the equator), with spatial coverage from 60°S to 85°N. Soil parameters derived from interpolated soil profiles and vegetation parameters estimated from space-based MODIS measurements have been compiled into input files for both the Classic and Image drivers of the VIC model, version 5. Geographical subsetting codes are provided, as well. Our dataset provides all necessary land surface parameters to run the VIC model at regional to global scale. We evaluate VICGlobal’s ability to simulate the water balance in the Upper Colorado River basin and 12 smaller basins in the CONUS, and their ability to simulate the radiation budget at six SURFRAD stations in the CONUS.

MAPPING COASTAL AND WETLAND VEGETATION COMMUNITIES USING MULTI-TEMPORAL SENTINEL-2 DATA

Operational monitoring of complex vegetation communities, such as the ones growing in coastal and wetland areas, can be effectively supported by satellite remote sensing, providing quantitative spatialized information on vegetation parameters, as well as on their temporal evolution. With this work, we explored and evaluated the potential of Sentinel-2 data for assessing the status and evolution of coastal vegetation as the primary indicator of ecosystem conditions, by mapping the different plant communities of Venice lagoon (Northeast Italy) via a rule-based classification approach exploiting synoptic seasonal features of spectral indices and multispectral reflectance. The results demonstrated that coastal and wetland vegetation community type maps derived for two different years scored a good overall accuracy around 80%, with some misclassification in the coastal areas and overestimation of salt marsh communities coverage, and that virtual collaborative environments can facilitate the use of Sentinel-2 data and products to multidisciplinary users.