Ahmad Hojatimalekshah

In much of the northern Great Basin of the western United States, rangelands, and semi‐arid ecosystems invaded by exotic annual grasses such as cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput‐medusae) are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground‐based field methods for measuring the above‐ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes and better harmonize with other remote sensing techniques. Developments and increased adoption of unoccupied aerial systems (UAS) and instrumentation for vegetation monitoring enable greater understanding of vegetation in many ecosystems. Research to understand the relationship of traditional field measurements with remotely sensed data in rangeland environments is growing rapidly, and there is increasing interest in the use of aerial platforms to quantify AGB and fine‐fuel load at pasture and landscape scales. Our study uses relatively inexpensive handheld photography with custom quadrat sampling frames to collect and automatically reconstruct 3D models of the vegetation within 0.2 m² quadrats (n = 288). Next, we examine the relationship between volumetric estimates of vegetation with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm³) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared with validation ground surface reconstructions. This finding is significant given that our study site is characterized by a dense litter layer covering the ground surface, making reconstruction challenging. Overall, our best reconstruction workflow had an R² of 0.42, further emphasizing the importance of high‐resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine‐fuel litter.

Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m⁻³ for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km² may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Rangelands and semi-arid ecosystems are subject to increasing changes in ecologic makeup from a collection of factors. In much of the northern Great Basin of the western United States, rangelands invaded by exotic annual grasses such as cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae) are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes, and better harmonize with other remote sensing techniques. Developments and increased adoption of uncrewed aerial vehicles and instrumentation for vegetation monitoring are enabling greater understanding of vegetation in many ecosystems. Research towards understanding the relationship of traditional field measurements with newer aerial platforms in rangeland environments is growing rapidly, and there is increasing interest in exploring the potential use both to quantify AGB and fine fuel load at pasture and landscape scales. Our study here uses relatively inexpensive handheld photography with custom sampling frames to collect and automatically reconstruct 3D-models of the vegetation within 0.2 m2 quadrats (n = 288). Next, we examine the relationship between volumetric estimates of vegetation to compare with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm3) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared to using the true ground surface. Overall, our reconstruction workflow had an R2 of 0.42, further emphasizing the importance of high-resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine fuel litter.

Snowpack is an important source of freshwater in mountainous regions. Understanding the role of different controls on snow properties (depth, distribution, and snow water equivalent (SWE)) and processes (accumulation and ablation) is important to predict available freshwater. Snow processes vary with respect to the predominant local controls in different landscapes. In many mountainous landscapes, controls on snow properties and processes are highly correlated with vegetation properties. In this dissertation, to elucidate the relationships between snow and vegetation, I use terrestrial laser scanning to explore how forest canopy structure affects snow depth distribution. In addition, I examine different vegetation metrics to find what measure of vegetation best describes snow under the canopy. By leveraging airborne lidar and deep learning, I investigate vegetation and topographical descriptors and their scale of influence on snow depth and pattern. Finally, I use radar remote sensing and machine learning techniques to estimate snow density and snow water equivalent in a mountainous western watershed.

Spaceborne remote sensing of snow currently enables landscape-scale snow covered area, but estimating snow mass in the mountains remains a major challenge from space. Airborne LiDAR can retrieve snow depth, and some promising results have recently been shown from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data is available to evaluate model estimates of density in mountainous terrain. Knowledge of the spatial patterns and predictors of density is critical for accurate assessment of SWE and essential snow physics, such as energy balance and mechanics related to hazards and over-snow mobility. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne LiDAR snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer picking method that leverages co- and cross-polarization coherence and distributed LiDAR–GPR inferred bulk density with machine learning. The root-mean-square error between the distributed estimates is 12 cm for depth, 27 kg/m³ for density, and 42 mm for SWE, and the median relative uncertainty in distributed SWE is 7 %. Wind, terrain, and vegetation interactions display corroborated controls on bulk density that show model and observation agreement. The spatially continuous snow density and SWE estimated over approximately 16 km² represents the next step towards broad-scale SWE retrieval.

The dataset is a collection of 1m resolution snow depth, elevation, aspect, slope, canopy percent cover, canopy height, foliage height diversity (FHD) with 0.5 m, 1 m, and 2 m voxel sizes. We processed lidar data of Grand Mesa, Colorado, representing a data collection effort by the NASA SnowEx campaign. Using the data we investigate how structural diversity and topography affect snow depth patterns. Snow depth is computed by snow-off and snow-on dataset from September 2016 and February 2020, respectively. Snow-off lidar data were collected by the Airborne Snow Observatory (ASO), lidar system created by NASA/JPL. Snow-on lidar data are provided by Quantum Spatial as a part of the NASA SnowEx (https://snow.nasa.gov/campaigns/snowex) campaign in 2020.

Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine-scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA (winter 2016–2017), to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We utilize these descriptors along with topographical metrics in multiple linear and decision tree regressions to investigate snow depth variations under the canopy and in open areas. Canopy, topography, and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity; FHD) along with local-scale processes like wind and topography are highly influential in snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results indicate that FHD can explain up to 27 % of sub-canopy snow depth variation at sites where the effect of topography and wind is negligible. Solar radiation and elevation are the dominant controls on snow depth in open areas. Fine-scale analysis from TLS provides information on local-scale controls and provides an opportunity to be readily coupled with lidar or photogrammetry from uncrewed aerial systems (UASs) as well as airborne and spaceborne platforms to investigate larger-scale controls on snow depth.

Understanding the impact of tree structure on snow depth and extent is important in order to make predictions of snow amounts, and how changes in forest cover may affect future water resources. In this work, we investigate snow depth under tree canopies and in open areas to quantify the role of tree structure in controlling snow depth, as well as the controls from wind and topography. We use fine scale terrestrial laser scanning (TLS) data collected across Grand Mesa, Colorado, USA, to measure the snow depth and extract horizontal and vertical tree descriptors (metrics) at six sites. We apply the Marker-controlled watershed algorithm for individual tree segmentation and measure the snow depth using the Multi-scale Model to Model Cloud Comparison algorithm. Canopy, topography and snow interaction results indicate that vegetation structural metrics (specifically foliage height diversity) along with local scale processes such as wind are highly influential on snow depth variation. Our study specifies that windward slopes show greater impact on snow accumulation than vegetation metrics. In addition, the results emphasize the importance of tree species and distribution on snow depth patterns. Fine scale analysis from TLS provides information on local scale controls, and provides an opportunity to be readily coupled with airborne or spaceborne lidar to investigate larger-scale controls on snow depth.

Qazvin plain as one of the important plains in northwest Iran in terms of industrial and agricultural activities, reveals evidence of land subsidence in the last decade. We used a descending ENVISAT dataset during 2003–2006 and two ascending and descending Sentinel-1 tracks acquired during 2014–2016 to study the subsidence in the area. Our time-series analysis of the ENVISAT data reveals that the area is subsiding at an average rate of 40 mm yr⁻¹ with a maximum value of 50 mm yr⁻¹ in the line of sight direction. Moreover, our result for the Sentinel-1 ascending and descending tracks shows that the average subsidence rate reaches 65 mm yr⁻¹ in the line of sight direction. Comparing the obtained mean velocity maps of the two datasets indicates the growing subsidence pattern in the northwest-southeast direction inside the plain. The investigation of the water source reveals that the overexploitation of aquifers caused an approximately 17 m decline in the groundwater level from 2001 to 2011. This drastic drop in the groundwater level has resulted in progressive subsidence over time. The comparison between the extent of subsidence from the image processing and the water level depletion contour map in the Qazvin plain depicts that subsidence is occurring in areas with a high decrease in underground water level. This study suggests that, besides decreasing the number of deep wells as well as their outflow water, the types of agricultural production related to water consumption and irrigation methods in an area such as Iran can play an important role in decreasing the stress on aquifers.