Predicting Risks of Severe Windstorm Damage to Southeastern U.S. forests

ContextThe southeastern U.S. experiences tornadoes and severe thunderstorms that can cause economic and ecological damage to forest stands resulting in loss of timber, reduction in short-term carbon sequestration, and increasing forest pests and pathogens. ObjectivesThis project sought to determine landscape-scale patterns of recurring wind damages and their relationships to topographic attributes, overall climatic patterns and soil characteristics in southeastern forests. MethodsWe assembled post-damage assessment data collected since 2012 by the National Oceanic and Atmospheric Administration (NOAA). We utilized a regularized Generalized Additive Model (GAM) framework to identify and select influencing topographic, soil and climate variables and to discriminate between damage levels (broken branches, uprooting, or trunk breakage). Further, we applied a multinomial GAM utilizing the identified variables to generate predictions and interpolated the results to create predictive maps for tree damage. ResultsTerrain characteristics of slope and valley depth, soil characteristics including erodibility factor and bedrock depth, and climatic variables including temperatures and precipitation levels contributed to damage severity for pine trees. In contrast, valley depth and soil pH, along with climactic variables of isothermality and temperature contributed to damage severity for hardwood trees. Areas in the mid-south from Mississippi to Alabama, and portions of central Arkansas and Oklahoma showed increased probabilities of more severe levels of tree damage. ConclusionsOur project identified important soil and climatic predictors of tree damage levels, and areas in the southeastern U.S. that are at greater risk of severe wind damage, with management implications under continuing climate change.

Avocado Cover Expansion in the Monarch Butterfly Biosphere Reserve, Central Mexico

Avocado cultivation has reduced the extent of forest ecosystems in central Mexico, even in natural protected areas such as the Monarch Butterfly Biosphere Reserve (MBBR) where information on the extent and expansion dynamics of avocado cover is scant. This study aimed to identify avocado plantations within the MBBR through photo interpretation for the 2006–2018 period. Change rates of the avocado cover extent were calculated for the northern, central, and southern zones of the MBBR, and topographic attributes such as elevation, soil type, slope, and slope aspect were identified. A total extent of 958 ha is covered by avocado plantations within the MBBR. The southern zone hosted the largest area under avocado cultivation (570 ha), but the northern zone had the highest change rate between 2006 and 2018 (422%). Most avocado orchards have been established mainly in Acrisol soils, south-facing slopes, on steep hillsides, and in elevations between 2050 and 2800 m. The conversion from traditional agricultural lands has been the main mechanism for the establishment of avocado orchards. However, 40 ha under avocado cultivation derived from deforestation, mainly in the central zone. The expansion of avocado plantations could trigger environmental impacts, even threatening the overwintering habitat and the migratory phenomenon of the monarch butterflies.

Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models

Accurate yield estimation and optimized agricultural management is a key goal in precision agriculture, while depending on many different production attributes, such as soil properties, fertilizer and irrigation management, the weather, and topography.The need for timely and accurate sensing of these inputs at the within field-scale has led to increased adoption of very high-resolution remote and proximal sensing technologies. With regard to topography attributes, greater attention is currently being devoted to LiDAR datasets (Light Detection and Ranging), mainly because numerous topographic variables can be derived at very high spatial resolution from these datasets. The current study uses LiDAR elevation data from agricultural land in southern Ontario, Canada to derive several topographic attributes such as slope, and topographic wetness index, which were then correlated to seven years of crop yield data. The effectiveness of each topographic derivative was independently tested using a moving-window correlation technique. Finally, the correlated derivatives were selected as explanatory variables for geographically weighted regression (GWR) models. The global coefficient of determination values (determined from an average of all the local relationships) were found to be R2 = 0.80 for corn, R2 = 0.73 for wheat, R2 = 0.71 for soybeans and R2 = 0.75 for the average of all crops. These results indicate that GWR models using topographic variables derived from LiDAR can effectively explain yield variation of several crop types on an entire-field scale.

Large-Scale Modeling of Solum Thickness Based on the Local Topographic Attributes of Ground and Bedrock Surfaces

This study was aimed to address the importance of neighborhood scale and using bedrock topography in the soil-landscape modeling in a low-relief large region. For this study, local topographic attributes (slopes and curvatures) of the ground surface (DTM) and bedrock surface (DBM) were derived at five different neighborhood sizes (3×3, 9×9, 15×15, 21×21, and 27×27). Afterward, the topographic attributes were used for multivariate adaptive regression splines (MARS) modeling of solum thickness. The results demonstrate that there are statistical differences among DTM and DBM morphometric variables and their correlation to solum thickness. The MARS analyses revealed that the neighborhood scale could remarkably affect the soil–landscape modeling. We developed a powerful MARS model for predicting soil thickness relying on the multi-scale geomorphometric analysis (R2= 83%; RMSE= 12.70 cm). The MARS fitted model based on DBM topographic attributes calculated at a neighborhood scale of 9×9 has the highest accuracy in the prediction of solum thickness compared to other DBM models (R2 = 61%; RMSE = 19cm). This study suggests that bedrock topography can be potentially utilized in soil-related research, but this idea still needs further investigations.

Quaternary formations mapping in the region of Volta Grande do Rio Uruguai (Brazil)

Quaternary formations (detrital and weathered materials) are an important natural resource for different areas of scientific investigation, from understanding their relation to erosive processes and morphodynamic processes that create landforms or to understanding the history of the first human settlements (geoarcheology). Quaternary coverings can be formed in situ or be transported by external geologic agents. Regarding soils, Quaternary formations are related to landscape topography and are transformed according to the characteristics of this topography. Hence, classifying and mapping these soils is not always easy. The present article aims to map the Quaternary formations along a stretch of the Uruguay River basin known as Volta Grande (SC/RS-Brazil), by using topographic attributes derived from the SRTM GL1-Up Sampled digital elevation model, soil particle-size analysis, and a generated Multiresolution Index of Valley Bottom Flatness (MRVBF) index . The results of the analysis show that: (i) colluvium is the predominant Quaternary formation in the study area; (ii) there is a predominance of clay, corroborating previous studies of the region; (iii) the spatial distribution of the study area’s Quaternary formations reflect local slope dynamics based on morphology and topographic position; and, (iv) the existence of colluvium-alluvium on the Uruguay River’s banks indicates that slope attributes contributed to the pedogeomorphological dynamics of the study area and not only fluvial dynamics. Based on the results, the methodology applied in this study might be useful for pedogeomorphological studies, notably in the analysis and mapping of Quaternary formations, despite some of its limitations.

High-resolution near-surface soil moisture through the combination of Sentinel-1 and Cosmic-Ray Neutron Probe in a Mediterranean agroforestry

<p>The precise estimation and mapping of the near-surface soil moisture (~5cm, SM<sub>5cm</sub>) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM<sub>5cm</sub> at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM<sub>5cm</sub> in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM<sub>5cm</sub> were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two  supporting estimates of SM<sub>5cm</sub> by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM<sub>5cm</sub> was further downscaled to obtain point-scale supporting SM<sub>5cm</sub> data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM<sub>5cm</sub> retrievals with RMSE values ranging from 0.034 to 0.050 cm³ cm<sup>-3</sup> similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm³ cm<sup>-3</sup>. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM<sub>5cm</sub> at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.</p><p> </p>

Coupling GIS and Machine Learning for the quantitative assessment of geomorphodiversity

<p>The aim of this work is to use machine learning methods coupled with GIS to evaluate the Geomorphodiversity Index (GmI).</p><p>The starting assumptions are that:</p><p>-          the geomorphodiversity or the variety of landforms reflects not only the geomorphological processes but the geological component too.</p><p>-          The numerical assessment is preferable to have a spatial distribution of the geomorphodiversity, being an objective and repeatable procedure and allowing for the comparison of areas in different geographical contexts.</p><p>-          The digital data and in particular the Digital Elevation Models, can define the topographic attributes necessary to gain the quantitative index without taking into account the traditional geomorphological maps. The topographic attributes reveal and summarize the presence and the efficiency of the driving forces that shape the Earth surface. The drainage density could fill the gap on the flat areas where the topographic attributes failed (for medium horizontal resolution values).</p><p>Trying to do an unsupervised clustering approach a K-Means and Mini-Batch K-Means for "partitional" clustering and Agglomerative Clustering for "hierarchical" clustering algorithms have been used with Anaconda (an open-source software distribution platform used for data analysis and specifically, Spyder was used as IDE -Integrated Development Environment- and Python 3.7 as a programming language) coupled with ArcGIS 10.1 © ESRI software. The research is split in three different steps: the selection of the data into ArcGIS, managing, analysis and clustering of the dataset with Python, reclassification of the data and comparison with the GmI already known in the literature in ArcGIS.</p><p>The test area is the Umbria region (central Italy) where the GmI derived from a simple GIS analysis is already available.</p><p>The advantages of the unsupervised method are that:</p><p>-          no weight was previously assigned to the individual typographic parameters.</p><p>-          Calculation times are extremely short.</p><p>-          The results are similar but more accurate than GIS analysis alone.</p>

Using Topographic Attributes to Predict the Understorey Structure of a Wet Eucalypt Forest

Background: Forest understorey structure is an important component of forest ecosystems that affects forest-dwelling species, nutrient cycling, fire behaviour, biodiversity, and regeneration capacity. Mapping the structure of forest understorey vegetation with field surveys or high-resolution LiDAR data is costly. We tested whether landscape topography and underlying geology could predict the understorey structure of a 19 km2 area of wet eucalypt primary forest located at the Warra Long Term Ecological Research Supersite, Tasmania, Australia. In this study, we used random forest regressions based on twelve topographic attributes derived from digital terrain models (DTMs) at various resolutions and a geology variable to predict the densities of three understorey layers compared to density estimates from a high resolution (28.66 points/m2) LiDAR survey. Results: We predicted the vegetation density of three canopy strata with a high degree of accuracy (validation root mean square error ranged from 8.97% to 13.69%). 30 m resolution DTMs provided greater predictive accuracy than DTMs with higher spatial resolution. Variable importance depended on spatial resolutions and canopy strata layers, but among the predictor variables, geology generally produced the highest predictive importance followed by solar radiation. Topographic position index, aspect, and SAGA wetness index had moderate importance. Conclusions: This study demonstrates that geological and topographic attributes can provide useful predictions of understorey vegetation structure in a primary forest. Given the good performance of 30 m resolution, the predictive power of the models could be tested on a larger geographical area using lower density LiDAR point clouds. This study should help in assessing fuel loads, carbon stores, biomass, and biological diversity, and could be useful for foresters and ecologists contributing to the planning of sustainable forest management and biodiversity conservation.