Evaluation of Riparian Tree Cover and Shading in the Chauga River Watershed Using LiDAR and Deep Learning Land Cover Classification

River systems face negative impacts from development and removal of riparian vegetation that provide critical shading in the face of climate change. This study used supervised deep learning to accurately classify the land cover, including shading, of the Chauga River watershed, located in Oconee County, South Carolina, for 2011 and 2019. The study examined the land cover differences along the Chauga River and its tributaries, inside and outside the Sumter National Forest. LiDAR data were incorporated in solar radiation calculations for the Chauga River inside and outside the National Forest. The deep learning classifications produced land cover maps with high overall accuracy (97.09% for 2011; 97.58% for 2019). The most significant difference in land cover was in tree cover in the 50 m buffer of the tributaries inside the National Forest compared to the tributaries outside the National Forest (2011: 95.39% vs. 81.84%, 2019: 92.86% vs. 82.06%). The solar radiation calculations also confirmed a difference between the area inside and outside the National Forest, with the mean temperature being greater outside the protected area (outside: 455.845 WH/m2; inside: 416,770 WH/m2). This study suggests that anthropogenic influence in the Chauga River watershed is greater in the areas outside the Sumter National Forest, which could cause damage to the river ecosystem if left unchecked in the future as development pressures increase. This study demonstrates the accurate application of deep learning for high-resolution classification of river shading combined with the use of LiDAR data to estimate solar radiation reaching the Chauga River. Techniques to monitor riparian zones and shading at high spatial resolutions are critical for the mitigation of the negative impacts of warming climates on aquatic ecosystems

Spatiotemporal Analysis of Soil Quality Degradation and Emissions in the State of Iowa (USA)

The concept of soil quality (SQ) is defined as the soil\u27s capacity to function, which is commonly assessed at the field scale. Soil quality is composed of inherent (soil suitability) and dynamic (soil health, SH) SQ, which can also be analyzed using geospatial tools as a SQ continuum (SQC). This study proposes an innovative spatiotemporal analysis of SQ degradation and emissions from land developments using the state of Iowa (IA) in the United States of America (USA) as a case study. The SQ degradation was linked to anthropogenic soil (SD) and land degradation (LD) in the state. More than 88% of land in IA experienced anthropogenic LD primarily due to agriculture (93%). All six soil orders were subject to various degrees of anthropogenic LD: Entisols (75%), Inceptisols (94%), Histosols (59%), Alfisols (79%), Mollisols (93%), and Vertisols (98%). Soil and LD have primarily increased between 2001 and 2016. In addition to agricultural LD, there was also SD/LD caused by an increase in developments often through urbanization. All land developments in IA can be linked to damages to SQ, with 8385.9 km2 of developed area, causing midpoint total soil carbon (TSC) losses of 1.7 × 1011 kg of C and an associated midpoint of social cost of carbon dioxide emissions (SC-CO2) of $28.8B (where B = billion = 109, USD). More recently developed land area (398.5 km2) between 2001 and 2016 likely caused the midpoint loss of 8.0 × 109 kg of C and a corresponding midpoint of$1.3B in SC-CO2. New developments are often located near urban areas, for example, near the capital city of Des Moines, and other cities (Sioux City, Dubuque). Results of this study reveal several different kinds of SQ damage from developments: loss of potential for future C sequestration in soils, soil C loss, and “realized” soil C social costs (SC-CO2). The state of IA has very limited potential land (2.0% of the total state area) for nature-based solutions (NBS) to compensate for SD and LD. The results of this study can be used to support pending soil health-related legislation in IA and monitoring towards achieving the Sustainable Development Goals (SDGs) developed by the United Nations (UN) by providing a landscape-level perspective on LD to focus field-level initiatives to reduce soil loss and improve SQ. Future technological innovations will provide higher spatial and temporal remote sensing data that can be fused with field-level direct sensing to track SH and SQ changes

Climate Change Planning: Soil Carbon Regulating Ecosystem Services and Land Cover Change Analysis to Inform Disclosures for the State of Rhode Island, USA

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy

Quantifying Damages to Soil Health and Emissions from Land Development in the State of Illinois (USA)

The concept of soil health is increasingly being used as an indicator for sustainable soil management and even includes legislative actions. Current applications of soil health often lack geospatial and monetary analyses of damages (e.g., land development), which can degrade soil health through loss of carbon (C) and productive soils. This study aims to evaluate the damages to soil health (e.g., soil C, the primary soil health indicator) attributed to land developments within the state of Illinois (IL) in the United States of America (USA). All land developments in IL can be associated with damages to soil health, with 13,361.0 km2 developed, resulting in midpoint losses of 2.5 × 1011 of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO2) of $41.8B (where B = billion = 109, USD). More recently developed land area (721.8 km2) between 2001 and 2016 likely caused the midpoint loss of 1.6 × 1010 kg of TSC and a corresponding midpoint of$2.7B in SC-CO2. New developments occurred adjacent to current urban areas near the capital cities of Springfield, Chicago, and St. Louis (the border city between the states of Missouri and IL). Results of this study reveal several types of damage to soil health from developments: soil C loss, associated “realized” soil C social costs (SC-CO2), and loss of soil C sequestration potential from developments. The innovation of this study has several aspects. Geospatial analysis of land cover combined with corresponding soil types can identify changes in the soil health continuum at the landscape level. Because soil C is a primary soil health indicator, land conversions caused by developments reduce soil health and the availability of productive soils for agriculture, forestry, and C sequestration. Current IL soil health legislation can benefit from this landscape level data on soil C loss with GHG emissions and associated SC-CO2 costs by providing insight into the soil health continuum and its dynamics. These techniques and data can also be used to expand IL’s GHG emissions reduction efforts from being solely focused on the energy sector to include soil-based emissions from developments. Current soil health legislation does not recognize that soil’s health is harmed by disturbance from land developments and that this disturbance results in GHG emissions. Soil health programs could be broadened to encourage less disturbance of soil types that release high levels of GHG and set binding targets based on losses in the soil health continuum

Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach

Altres ajuts: The work of Yonatan Tarazona Coronel has been partially funded by American Program in GIS and Remote Sensing and National Program of Scholarships and Educational Credit (PRONABEC-Peru) through RJ: Nº 4276-2018-MINEDU/VMGI-PRONABEC-OBE and RJ: Nº 942-2019-MINEDU/VMGI-PRONABEC-OBE.This article focuses on mapping tropical deforestation using time series and machine learning algorithms. Before detecting changes in the time series, we reduced seasonality using Photosynthetic Vegetation (PV) index fractions obtained from Landsat images. Single and multi-temporal filters were used to reduce speckle noise from Synthetic Aperture Radar (SAR) images (i.e., ALOS PALSAR and Sentinel-1B) before fusing them with optical images through Principal Component Analysis (PCA). We detected only one change in the two PV series using a non-seasonal detection approach, as well as in the fused images through five machine learning algorithms that were calibrated with Cross-Validation (CV) and Monte Carlo Cross-Validation (MCCV). In total, four categories were obtained: forest, cropland, bare soil, and water. We then compared the change map obtained with time series and that obtained with the classification algorithms with the best calibration performance, revealing an overall accuracy of 92.91% and 91.82%, respectively. For statistical comparisons, we used deforestation reference data. Finally, we conclude with some discussions and reflections on the advantages and disadvantages of the detections made with time series and machine learning algorithms, as well as the contribution of SAR images to the classifications, among other aspects

The first generation of a regional-scale 1-m forest canopy cover dataset using machine learning and google earth engine cloud computing platform: A case study of Arkansas, USA

Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R2 values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world

Assessing Ecosystem Services of Atmospheric Calcium and Magnesium Deposition for Potential Soil Inorganic Carbon Sequestration

Many soil regulating ecosystem services (ES) are linked to Earth’s atmosphere, but associated monetary values often are unknown or difficult to quantify. Atmospheric deposition of calcium (Ca2+) and magnesium (Mg2+) are abiotic flows (wet, dry, and total) from the atmosphere to land surfaces, which potentially can become available to sequester carbon (C) as soil inorganic carbon (SIC). However, these processes typically have not been included in economic valuations of ecosystem services. The primary objective of this study was to demonstrate an approach for valuing non-constrained potential SIC sequestration from atmospheric Ca2+ and Mg2+ deposition based on the concept of the avoided social cost of carbon dioxide emissions (SC-CO2). Maximum monetary values associated with the non-constrained potential SIC sequestration were compiled for the contiguous United States (U.S.) by soil order, land resource region (LRR), state, and region using available deposition data from the National Atmospheric Deposition Program (NRSP-3). For the entire contiguous U.S., an average annual monetary value for the non-constrained potential SIC sequestration due to atmospheric Ca2+ and Mg2+ deposition was $135M (i.e.,$135 million U.S. dollars, where M = million = 106). Mollisols, Alfisols, and Entisols were soil orders with the highest average annual monetary values for non-constrained potential SIC sequestration. When normalized by land area, however, Vertisols had the highest average annual monetary values followed by Alfisols and Mollisols for non-constrained potential SIC sequestration. From a more agricultural perspective, the LRRs with the highest average annual monetary values for non-constrained potential SIC sequestration were the Western Range and Irrigated Region (D), the Central Feed Grains and Livestock Region (M), and the Central Great Plains Winter Wheat and Range Region (H). When normalized by area, the LRRS with the highest average annual monetary values were the Southwest Plateaus and Plains Range and Cotton Region (I) and the Florida Subtropical Fruit, Truck Crop and Range Region (U). Among the U.S. states, the highest average annual monetary values for non-constrained potential SIC sequestration were Texas, Kansas, and New Mexico, but when normalized by area the highest values by state were Kansas, Iowa, and Texas. Geographical regions in the contiguous U.S. with the highest average annual monetary values for non-constrained potential SIC sequestration were the South Central, Midwest, and West; when normalized by area, the highest values by region were South Central, Midwest, and Northern Plains. Constraints on maximum monetary values, based on physical, chemical, biological, economic, social, and political limitations, need to be considered and quantified to obtain more precise and accurate accounting of the ES associated with SIC sequestration due to atmospheric Ca2+ and Mg2+ deposition

Teaching Field Data Crowdsourcing Using a GPS-Enabled Cellphone Application: Soil Erosion by Water as a Case Study

Crowdsourcing is an important tool for collecting spatio-temporal data, which has various applications in education. The objectives of this study were to develop and test a laboratory exercise on soil erosion by water and field data crowdsourcing in an online introductory soil science course (FNR 2040: Soil Information Systems) at Clemson University. Students from different STEM disciplines (wildlife biology, forestry, and environmental and natural resources) participated in the study in the fall of 2021. They completed a sequence of self-contained digital teaching modules or reusable learning objects (RLOs), which are often used in online learning. The exercise included a field exercise and learning module to teach students about different types of water-based soil erosion as well as field data collection and crowdsourcing tools. As a result of this exercise, student familiarity with crowdsourcing was effectively increased, as shown by the post-assessment survey with a +31.2% increase in the “moderately familiar” category and a +28.3% increase in the “extremely familiar” category. The online quiz contained ten questions and was taken by 56 students with an average score of 9.5 (out of 10). A post-assessment survey found that most of the students indicated that the laboratory was an effective learning experience about field data crowdsourcing using a GPS-enabled cellphone application. Detailed students’ comments indicated enjoyment of learning (e.g., data collection, learning about different technologies), the value of multimedia (e.g., ArcGIS Survey123, cellphone), the flexibility of learning (e.g., field work), the content applicability (e.g., actual field examples of erosion by water), and criticism (e.g., technical issues). A word cloud derived from students’ comments about their laboratory exercise experience indicated the most frequent words used by students, such as “erosion”, “enjoyed”, and “different”, among others. Incorporating a learning module and field exercise using modern data collection technology into an undergraduate soil science education course enabled students to understand the value and methods for leveraging cellphone-based field collection methods to crowdsource data for environmental assessment. Practical recommendations for planning and executing future crowdsourcing exercises were developed using the current study as an example

Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

Fluvial dynamics are an important aspect of land-use planning as well as ecosystem conservation. Lack of floodplain and flood inundation maps can cause severe implication on land-use planning and development as well as in disaster management. However, flood hazard delineation traditionally involves hydrologic models and uses hydraulic data or historical flooding frequency. This entails intensive data gathering, which leads to extensive amount of cost, time, and complex models, while typically only covers a small portion of the landscape. Therefore, alternative approaches had to be explored. This study explores an alternative approach in delineating flood hazard areas through a straightforward interpolation process while using high-resolution LiDAR-based datasets. The objectives of this study are: (1) to delineate flood hazard areas through a straightforward, nonhydraulic, and interpolation procedure using high-resolution (LiDAR-based) datasets and (2) to determine whether using high-resolution data, coupled with a straightforward interpolation procedure, will yield reliable potential flood hazard maps. Results showed that a straightforward interpolation method using LiDAR-based data produces a reliable potential flood zone map. The resulting map can be used as supplementary information for rapid analysis of the topography which could have implications in area development planning and ecological management and best practices

Assessing the Value of Soil Inorganic Carbon for Ecosystem Services in the Contiguous United States Based on Liming Replacement Costs

Soil databases are very important for assessing ecosystem services at different administrative levels (e.g., state, region etc.). Soil databases provide information about numerous soil properties, including soil inorganic carbon (SIC), which is a naturally occurring liming material that regulates soil pH and performs other key functions related to all four recognized ecosystem services (e.g., provisioning, regulating, cultural and supporting services). However, the ecosystem services value, or “true value,„ of SIC is not recognized in the current land market. In this case, a negative externality arises because SIC with a positive value has zero market price, resulting in the market failure and the inefficient use of land. One potential method to assess the value of SIC is by determining its replacement cost based on the price of commercial limestone that would be required to amend soil. The objective of this study is to assess SIC replacement cost value in the contiguous United States (U.S.) by depth (0⁻20, 20⁻100, 100⁻200 cm) and considering different spatial aggregation levels (i.e., state, region, land resource region (LRR) using the State Soil Geographic (STATSGO) soil database. A replacement cost value of SIC was determined based on an average price of limestone in 2014 ($10.42 per U.S. ton). Within the contiguous U.S., the total replacement cost value of SIC in the upper two meters of soil is between$2.16T (i.e., 2.16 trillion U.S. dollars, where T = trillion = 1012) and $8.97T. States with the highest midpoint total value of SIC were: (1) Texas ($1.84T), (2) New Mexico ($355B, that is, 355 billion U.S. dollars, where B = billion = 109) and (3) Montana ($325B). When normalized by area, the states with the highest midpoint SIC values were: (1) Texas (2.78 m−2), (2) Utah (1.72 m−2) and (3) Minnesota (1.35 m−2). The highest ranked regions for total SIC value were: (1) South Central (1.95T), (2) West ($1.23T) and (3) Northern Plains ($1.01T), while the highest ranked regions based on area-normalized SIC value were: (1) South Central (1.80 m−2), (2) Midwest (0.82 m−2) and (3) West (0.63 m−2). For land resource regions (LRR), the rankings were: (1) Western Range and Irrigated Region (1.10T), (2) Central Great Plains Winter Wheat and Range Region ($926B) and (3) Central Feed Grains and Livestock Region ($635B) based on total SIC value, while the LRR rankings based on area-normalized SIC value were: (1) Southwest Plateaus and Plains Range and Cotton Region (3.33 m−2), (2) Southwestern Prairies Cotton and Forage Region (2.83 m−2) and (3) Central Great Plains Winter Wheat and Range Region (1.59 m−2). Most of the SIC is located within the 100⁻200 cm depth interval with a midpoint replacement cost value of 2.49T and an area-normalized value of $0.34 m−2. Results from this study provide a link between science-based estimates (e.g., soil order) of SIC replacement costs within the administrative boundaries (e.g., state, region etc.)