Mountain ecosystems, strongly affected by climate-related variability and human impact, are degrading faster than other terrestrial ecosystems. Currently, the United Nations (UN) utilizes Sustainable Development Goal (SDG) 15: Life on Land (Target 15.4 and Sub-indicators 15.4.2a and 15.4.2b), along with the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring, commonly referred to as SEPAL, to track mountain degradation. This SEPAL analysis does not include soil data, which is critical to understanding mountain degradation. The present research focuses on improving the tracking and evaluation of mountain land degradation (LD) utilizing soil data in the state of Colorado (CO) in the United States of America (USA) as an example. Total anthropogenic LD affects an estimated 19% of Colorado's territory as of 2024, driven mainly by agricultural activities (80%). Between 2001 and 2024, overall LD in CO decreased (-0.4%), but LD from development increased by 23.3%. For mountain areas in CO, the mountain green cover index (MGCI) was 96% for 2024, and it decreased (-0.4%) between 2001 and 2024. The mountain LD proportion was 2.5% as determined by the SEPAL method compared to 4.4% by LULC analysis. Incorporation of soil data into LULC analysis found that between 2001 and 2024 LD increased to 6.6%. All soil types in the mountains exhibited anthropogenic LD due to development with a total developed area of 1385.1 km2. Current total mountain LD (inherent + anthropogenic) in CO may be as high as 38.9%. Future estimates of total mountain LD should include both inherent and anthropogenic LD.

Wetlands are an important part of the Ramsar Convention (RC) and Kunming-Montreal Global Biodiversity Framework (KM-GBF), which is a significant agreement committing 196 countries (the United States is not a participant of KM-GBF) to a reduction and halting of the loss of biodiversity by 2030. In comparison to the broadly focused KM-GBF, goals of RC (172 contracting parties) are wetlands-focused such as “wise use” and protection of wetlands, selection of wetlands of “international importance”, and international collaboration. The goal of this study is to explore how improved wetlands analytics can help achieve the goals of both the RC and KM-GBF using the state of North Carolina (NC) in the United States of America (USA) as an example. Several goals and targets of KM-GBF are applicable to wetlands, with only a handful of indicators (e.g., wetland extent trends (WET) index) to monitor them. The WET index is only relevant at the global or continental scale because it relies on a limited database of wetland sites over time. Unfortunately, the WET index does not allow tracking of smaller spatial extents and new methods are needed to understand how wetlands are changing over time. Geospatial analysis results and recommendations are presented for both RC and KM-GBF. Loss of wetlands can result in transboundary damage, harming biodiversity and ecosystem functions and services (ES). A case study of NC showed that almost 49

Estimating tree height growth and survival rate are important for characterizing forest growth performance and evaluating dynamic changes during the early development of newly planted forests. This study evaluated tree height growth and survival rates in a newly forested area of Chinese fir (Cunninghamia lanceolata (Lamb) Hook) and Schima superba Gardner Champ. planting from two temporal Unmanned Aerial Vehicle (UAV) images over a four-year period. Individual tree crowns were delineated by visual interpretation, and tree heights were estimated using the local maxima method. Using the 2023 digital surface model (DSM) combined with the 2019 digital terrain model (DTM), UAV-based tree height estimation achieved high accuracy, with a root mean square error (RMSE) of 0.48 m for Chinese fir and 0.37 m for S. superba compared with field survey measurements in 2023. It reduced RMSE by 0.30 m for Chinese fir and 0.50 m for S. superba compared to using the 2023 DTM for ground height estimates. From 2019 to 2023, the survival rates of Chinese fir and S. superba were 92.39

Teaching wetlands in a global context requires a pedagogical shift from local field observations to planetary systems analysis. This study proposes a teaching approach that integrates wetlands, analytical tools, policy frameworks, and a global purpose, which can be used in Earth science education. Laboratory exercise was developed and tested using wetlands (including a wetland of international importance and a Ramsar site: Congaree National Park) in the state of South Carolina (SC) in the United States of America (USA). Multidisciplinary students (wildlife biology, forestry, environmental and natural resources) engaged in active learning about subject matter definitions (e.g., hydric soils), used various geospatial analytical tools (e.g., Web Soil Survey), learned about Ramsar Convention (RC), and identified ecosystem services provided by wetlands for achieving the United Nations (UN) Sustainable Development Goals (SDGs). Results of two study years (2024 and 2025) were consistent, showing an increase in wetlands knowledge and applied analytical skills. The online quiz, taken by 113 students, had an average score of 9.3 out of 10. Students increased their knowledge of UN SDGs and ranked SDGs 6, 13, 14, and 15 as the most relevant SDGs to wetlands. A newly proposed teaching approach can be adopted worldwide, as most materials and tools are publicly available.

The concept of nature-based solutions (NBS) is widely promoted as an approach to effectively counteract climate change and land degradation (LD) as well as simultaneously add environmental and socio-economic benefits. To have a worldwide impact from NBS, it is important to consider potential land and soil resources at various scales, including administrative units (e.g., country, state, county, etc.). Nature-based solutions are considered by many United Nations (UN) initiatives, including the Paris Agreement and the UN Convention to Combat Desertification (UNCCD). Currently, there is no consensus on how to define NBS and their indicators. The innovation of this study is that it defines and evaluates soil- and land-based NBS potential while suggesting indicators for land- and soil-based NBS using the contiguous United States of America (USA) as an example. This study defines potential land for NBS as the sum of the individual satellite-identified areas of barren, shrub/scrub, and herbaceous land covers, which are linked to climate and inherent soil quality (SQ), so that NBS could be implemented without changing other land uses. The potential soil for NBS, based on SQ, is a subset of land available for potential NBS. As of 2021, anthropogenic LD affected 2,092,539.0 km2 in the contiguous USA, with 928,618.0 km2 (15.1% of the contiguous US area) of actual potential land for NBS. The contiguous USA had a negative balance between anthropogenic LD and actual potential land for NBS to compensate for anthropogenic LD of −1,163,921.0 km2. Thirty-seven states also exhibited a negative balance for LD compensation with Iowa having the worst balance of −124,497.0 km2. Many states with positive anthropogenic LD and NBS balances showed that most of the potential NBS land was of low SQ and, therefore, may not be suitable for NBS. Planning for NBS should involve a feasibility analysis of “nationally determined NBS” (NDNBS) through site and context-specific NBS.

Earth science education can be enriched by adding technological knowledge to enable monitoring human earth impacts by using soil science as an example. Modern sensing technologies and a mobile mapping platform can enhance an existing field laboratory exercise to expand students’ knowledge beyond the core subject matter. This multi-year study’s objectives were to enrich laboratory exercise content on soil compaction using a soil penetration resistance (PR) tester (penetrometer) with the concepts of direct (soil PR) and proximal remote sensing (cellphone photos of the sample area), and crowdsourcing of field data using a GPS-enabled mobile phone application in an introductory soil science course at Clemson University, South Carolina (SC), United States of America (USA). Students from multiple Science, Technology, Engineering, and Mathematics (STEM) disciplines (forestry, wildlife biology, and environmental and natural resources) participated in the study. They completed a set of reusable learning objects (RLOs) in the following sequence: pre-testing questionnaire, laboratory video, quiz, and post-testing questionnaire. Students had increased familiarity with the concepts from this exercise, as demonstrated by the post-assessment survey. The quiz, which was taken by 113 students online, had an average total correct score of 9 out of a possible 10. A post-assessment survey indicated that the laboratory exercise was an effective way to learn about field soil PR data, direct and proximal remote sensing, and crowdsourcing with a GPS-enabled cellphone application. Results from the two study years (2022 and 2024) were consistent, indicating validity and confidence in the findings.

With their large numbers and widespread distribution, ponds are crucial in stormwater interception, biodiversity, and freshwater resource conservation. However, due to their small size and shallow depth, ponds are highly susceptible to anthropogenic activities and climate variability, making it necessary to map their numbers, distribution, and change dynamics. Relying only on deep learning (DL) techniques is insufficient to create a pond identification dataset that does not contain errors. This study is the first of its kind that proposes a workflow to identify small ponds (<5 ha) with minimal errors from the National Agricultural Imagery Program (NAIP) high-resolution aerial imagery using the combination of DL and a manual cross-correction approach. Ponds in South Carolina, United States, were detected and delineated for 2017 and 2019 using U-Net models. Next, the detection results from both years were used as reference data for cross-correction, removing false detections and adding omissions to obtain the refined high-precision pond datasets. The pond datasets were compared to the existing public datasets (JRC and NWI) to evaluate the performance of the proposed method. Finally, changes in ponds between two years and the predominant land cover around each pond in 2019 were analyzed in our study. The results showed that the refined high-precision pond dataset containing 70,449 ponds in 2017 and 71,858 ponds in 2019, with an average size of 0.5 ha, fills an important gap in existing pond data. The existing public datasets (JRC and NWI) do not identify 61.72% and 41.03% of the new high-precision pond dataset developed as part of our study in 2019. Based on land cover data, the largest number of ponds were located in forested areas (23,188 ponds, 0.76 ponds/km(2)), followed by wetlands (15,782 ponds, 0.76 ponds/km(2)). In contrast, barren land and hay/pasture had the highest pond density, reaching 2.67 ponds/km2 and 1.93 ponds/km(2). A total of 2,979 ponds experienced changes between 2017 and 2019, and 69,664 ponds remained unchanged. The types of pond changes can be categorized as new pond construction, water level changes, and pond disappearance. Our study significantly advances a workflow and method for pond detection that leverages deep learning over large areas in diverse ecological regions and can provide high-precision pond datasets with minimal errors for pond evaluation and management.

Mapping young forests is helpful for future forest management, including monitoring forest growth, assessing regeneration, and supporting effective silvicultural management strategies. Convolutional neural networks (CNNs) enable rapid and efficient tree mapping when combined with remote sensing imagery. In this study, we compared the classification performance of Mask Region-based CNN and Faster Region-based CNN using various UAV image resolutions for a young Casuarina equisetifolia L. forest located in Pingtan Comprehensive Pilot Zone, Fujian, China. The red, green, and blue band imagery obtained from a DJI Matrice 300 Real-Time Kinematic and a ZENMUSE P1 camera was resampled to images with 1.00, 2.00, 3.00, and 4.00 cm resolutions. Tree crowns were delineated from the original red, green, and blue band imagery, ensuring each polygon corresponded to a single tree on the ground. A total of 2273 tree crown polygons were used for model training, and the remaining 2948 tree crowns were used as the test set for model accuracy evaluation. The results showed that the highest accuracy was achieved using Mask Region-based CNN with an image resolution of 2.00 cm (F1 score = 95.18

Wetlands provide the world with important ecosystem services (ES) including carbon (C) storage. The Ramsar Convention (RC) is the only global treaty on wetlands outside of the United Nations (UN) with 172 contracting parties across the world as of 2025. The goals of the convention are to promote the wise use and conservation of wetlands, designation of suitable wetlands as wetlands of international importance, and international cooperation. The problem is that there is no consensus for standard global analysis, which is needed to ensure wetlands conservation. The novelty of this study is the use of methodology that combines satellite-based land cover change analysis with high-resolution spatial databases to help understand the change in wetlands area over time and identify potential hotspots for C loss. Greenhouse gas (GHG) emissions from wetland conversions represent “transboundary” damages. Therefore, C loss from wetlands conversions can be expressed through the “realized” social cost of C (SC-CO2) which is a conservative estimate of the damages caused by carbon dioxide (CO2) release. A case study of the contiguous United States of America (USA) using raster analysis within ArcGIS Pro showed key findings that almost 53% of the wetlands area was lost between 1780 and 1980, starting with 894,880.7 km2 in 1780 and falling to 422,388.2 km2 in 1980. This net loss generated damages including midpoint total soil C loss (6.7 × 1013 kg of C) with associated midpoint “realized” social costs of C (SC-CO2) value of $11.4T (where T = trillion = 1012, $ = United States dollars, USD). Recent analysis of the contiguous USA (2001–2021) revealed wetlands area losses and damages in all states. The newly demonstrated method for rapid monitoring of wetlands changes over time can be integrated into systems for worldwide monitoring to support the RC wise use concept.

The Kunming-Montreal Global Biodiversity Framework (GBF) is an important agreement committing 196 countries (the United States is not part of GBF) to reduce and stop the loss of biodiversity by 2030. Biodiversity and soil diversity (pedodiversity) are intricately linked by sharing biosphere. Similarly to biodiversity, pedodiversity is classified using various classification systems adopted by countries in the world (e.g., United States Soil Taxonomy). The loss of pedodiversity is often caused by land use and land cover (LULC) changes, which impact biodiversity. These losses need to be acknowledged and accounted for by the GBF. The innovation of this study is that it proposes to include pedodiversity and its metrics into the GBF using the contiguous United States of America (USA) and GBF targets as an example. This study proposes to use geospatial technologies (e.g., land cover change matrix) linked to soil databases to monitor temporal changes and no net loss in pedodiversity. Loss of pedodiversity can result in damages (e.g., pollution), which can harm biodiversity and ecosystem functions and services (ES). As of 2021, over two million square kilometers were anthropogenically degraded in the contiguous USA, with all ten soil orders being affected by this degradation (relevant to target ten focused on the sustainable use of natural resources). Analysis of changes in LULC between 2001 and 2021 showed an increase in anthropogenic land degradation (LD) (+3.4%), which resulted in a net loss of pedodiversity and affected all of the ten soil orders in the contiguous USA. Future GBF refinements could use pedodiversity metrics to analyze the ability to support biodiversity.

The concept of soil quality (SQ) is defined as the soil's 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.

Despite water ecosystems being capable of sustaining biodiversity and enhancing the overall resilience of the urban environment, they are highly susceptible to biological invasions. Invasive aquatic plants (IAPs) threaten the natural environment by reducing the diversity of native aquatic plants and animal communities. Detecting IAPs and mapping their distribution is crucial for the protection of urban water ecosystems. This study is the first of its kind to use high-resolution unmanned aerial vehicle (UAV) imagery and deep learning approaches to monitor the expansion of Pistia stratiotes (water lettuce). A DJI Matrice 300 RTK was utilized to capture time-series images with a resolution of 0.018 m on the Guanyin Lake, Cangshan District, Fuzhzou, China, during an outbreak and subsequent rapid growth stage of water lettuce. Three deep learning model architectures and three backbones were combined to detect water lettuce. Model performance and the ability to generalize the model were evaluated to determine the optimal model for water lettuce detection from time-series high-resolution UAV imagery. Results show that the DeepLabv3 + model with ResNet-34 achieved superior performance in detecting water lettuce from time-series imagery, yielding an average accuracy of 90.24 % (85.33 %≤F1_score ≤ 96.54 %) for water lettuce detection on five different dates. For the UAV image acquired on September 26th, the U-Net model with ResNet-18 yielded the highest accuracy (F1_score = 92.46 %), but it was not the optimal model for multi-temporal water lettuce detection on subsequent dates. The distribution of water lettuce can have large variations at different times, with an average change rate of 49.50 % every two days and the highest change rate up to 60.33 %. The study demonstrates that the combination of UAV imagery and a deep learning model can achieve excellent accuracy for water lettuce monitoring and provide a method to map IAPs in dynamic urban water systems over time.

Soil-based emissions from land conversions are often overlooked in climate planning. The objectives of this study were to use quantitative data on soil-based greenhouse gas (GHG) emissions for the state of Georgia (GA) (USA) to examine context-specific (temporal, biophysical, economic, and social) climate planning and legal options to deal with these emissions. Currently, 30% of the land in GA has experienced anthropogenic land degradation (LD) primarily due to agriculture (64%). All seven soil orders were subject to various degrees of anthropogenic LD. Increases in overall LD between 2001 and 2021 indicate a lack of land degradation neutrality (LDN) in GA. Besides agricultural LD, there was also LD caused by increased development through urbanization, with 15,197.1 km2 developed, causing midpoint losses of 1.2 × 1011 kg of total soil carbon (TSC) with a corresponding midpoint social cost from carbon dioxide (CO2) emissions (SC-CO2) of USD $20.4B (where B = billion = 109, $ = U.S. dollars (USD)). Most developments occurred in the Metro Atlanta and Coastal Economic Development Regions, which indicates reverse climate change adaptation (RCCA). Soil consumption from developments is an important issue because it limits future soil or forest carbon (C) sequestration potential in these areas. Soil-based emissions should be included in GA’s carbon footprint. Understanding the geospatial and temporal context of land conversion decisions, as well as the social and economic costs, could be used to create incentives for land management that limit soil-based GHG emissions in a local context with implications for relevant United Nations (UN) initiatives.

Soil makes important contributions to the United Nations (UN) Land Degradation Neutrality (LDN) concept and targets; however, currently, soil is not integrated into measurable information (e.g., indicators, metrics) to monitor land degradation (LD) patterns and trends. This study examines the role of soil in LDN in the UN Convention to Combat Desertification (UNCCD), and UN Sustainable Development Goal (SDG 15: Life on Land). This study is specifically focused on the LDN and biodiversity loss as they relate to an indicator 15.3.1 Proportion of land that is degraded over total land area. Tracking of LD status can be improved by using detailed soils databases combined with satellite-derived land cover maps. This study has applied these newly improved methods to quantify and map the anthropogenic LD status and trends in the contiguous United States of America (USA), as well as to identify potential land areas for nature-based solutions (NBS) to compensate for LD. Anthropogenic LD in 2016 in the contiguous USA affected over two million square kilometers, about one-third of the country’s total area, with high variability by state. Between 2001 and 2016, LD in the USA showed an overall increase of 1.5%, with some states exhibiting increases in degraded land while other states had overall improvements to their land. All ten soil orders present in the contiguous USA have been anthropogenically degraded, with Mollisols, Alfisols, and Vertisols having the highest LD levels. Compensating for LD requires a variety of strategies and measures (e.g., NBS), which often require additional land. In 2016, the potential land area for NBS was over two million square kilometers, an area approximately equal to that of degraded land. Some of the states that have high proportions of land available for potential NBS are dominated by soils (Aridisols) typical of deserts and therefore may have less promise for NBS. The variability of LD needs to be evaluated at finer spatial scales for realistic LDN analysis.

The United Nations (UN) Land Degradation Neutrality (LDN) evaluation stresses the need to account for different types of land degradation (LD) as part of the UN Sustainable Development Goal (SDG 15: Life on Land) and UN Convention to Combat Desertification (UNCCD). For example, one of the indicators, 15.3.1 Proportion of land that is degraded over total land area, can be differentiated between different types of LD (e.g., urban development, agriculture, barren) when considering land use and land cover (LULC) change analysis. This study demonstrates that it is important to consider not only the overall anthropogenic LD status and trend over time, but also the type of LD to confirm LDN. This study’s innovation is that it leverages remote-sensing-based LULC change analysis to evaluate LDN by different types of LD using the state of Ohio (OH) as a case study. Almost 67% of land in OH experienced anthropogenic LD primarily due to agriculture (81%). All six soil orders were subject to various degrees of anthropogenic LD: Mollisols (88%), Alfisols (70%), Histosols (58%), Entisols (55%), Inceptisols (43%), and Ultisols (22%). All land developments in OH can be linked to damages from LD, with 10,116.3 km2 developed, resulting in midpoint losses of 1.4 × 1011 kg of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO2) of $24B (where B = billion = 109, USD). Overall, the anthropogenic LD trend between 2001 and 2016 indicated LDN, however, during the same time, there was a six percent increase in developed area (577.6 km2), which represents a consumptive land conversion that likely caused the midpoint loss of 8.4 × 109 kg of TSC and a corresponding midpoint of $1.4B in SC-CO2. New developments occurred adjacent to current urban areas, near the capital city of Columbus, and other cities (e.g., Dayton, Cleveland). Developments negated OH’s overall LDN because of multiple types of damages: soil C loss, associated “realized” soil C social costs (SC-CO2), and loss of soil C sequestration potential. The state of OH has very limited potential land (1.2% of the total state area) for nature-based solutions (NBS) to compensate for the damages, which extend beyond the state’s boundaries because of the greenhouse gas emissions (GHG).

Climate change and land degradation (LD) are some of the most critical challenges for humanity. Land degradation (LD) is the focus of the United Nations (UN) Convention to Combat Desertification (UNCCD) and the UN Sustainable Development Goal (SDG 15: Life on Land). Land degradation is composed of inherent and anthropogenic LD, which are both impacted by inherent soil quality (SQ) and climate. Conventional LD analysis does not take into account inherent SQ because it is not the result of land use/land cover change (LULC), which can be tracked using remote sensing platforms. Furthermore, traditional LD analysis does not link anthropogenic LD to climate change through greenhouse gas (GHG) emissions. This study uses one of the indicators for LD for SDG 15 (15.3.1: Proportion of land that is degraded over the total land area) to demonstrate how to account for inherent SQ in anthropogenic LD with corresponding GHG emissions over time using the state of Arizona (AZ) as a case study. The inherent SQ of AZ is skewed towards low-SQ soils (Entisols: 29.3%, Aridisols: 49.4%), which, when combined with climate, define the inherent LD status. Currently, 8.6% of land in AZ has experienced anthropogenic LD primarily because of developments (urbanization) (42.8%) and agriculture (32.2%). All six soil orders have experienced varying degrees of anthropogenic LD. All land developments in AZ can be linked to damages from LD, with 4862.6 km2 developed, resulting in midpoint losses of 8.7 × 1010 kg of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO2) of $14.7B (where B = billion = 109, USD). Arizona was not land degradation neutral (LDN) based on an increase (+9.6%) in the anthropogenic LD overall and an increase in developments (+29.5%) between 2001 and 2021. Considering ongoing climate change impacts in AZ, this increase in urbanization represents reverse climate change adaptation (RCCA) because of the increased population. The state of AZ has 82.0% of the total state area for nature-based solutions (NBS). However, this area is dominated by soils with inherently low SQ (e.g., Entisols, Aridisols, etc.), which complicates efforts for climate change adaptation.

The United Nations (UN) Sustainable Development Goals (SDGs) offer an opportunity to improve soil science education on sustainability because they provide specific context to educate faculty and students from various disciplines, including Science, Technology, Engineering, and Mathematics (STEM) about SDGs. Soil science is a STEM discipline with a wide range of applications in the SDGs. The objectives of this study were to use a matrix approach (framework for presenting options for discussion and implementation) to integrate SDGs into an existing introductory soil science course taught to undergraduate students from different STEM fields (environmental and natural resources; wildlife biology; and forestry). The course was enriched with a lecture on SDGs and students were asked to link soil properties and class activities to specific SDGs. A post-assessment survey revealed an increase in students’ familiarity with SDGs, and their relevance to soil properties and course activities. Students acknowledged the importance of soils and individual actions for achieving the SDGs. There was an overall increase in student familiarity (+59.4%) with SDGs. Most students agreed (46.7%) and strongly agreed (23.3%) that the course activities were an effective way to learn about SDGs with examples from soil science. Identified learning gaps in subject matter found through the surveys on SDGs were clarified during later classroom discussions. The advantage of this teaching approach is that it seamlessly integrates SDGs with existing course materials while relying on students’ critical thinking skills to effectively analyze soil science information and form a judgement on how it relates to SDGs.

Mapping the distribution of living and dead trees in forests, particularly in ecologically fragile areas where forests serve as crucial ecological environments, is essential for assessing forest health, carbon storage capacity, and biodiversity. Convolutional neural networks, including Mask R-CNN, can assist in rapid and accurate forest monitoring. In this study, Mask R-CNN was employed to detect the crowns of Casuarina equisetifolia and to distinguish between live and dead trees in the Pingtan Comprehensive Pilot Zone, Fujian, China. High-resolution images of five plots were obtained using a multispectral Unmanned Aerial Vehicle. Six band combinations and derivatives, RGB, RGB-digital surface model (DSM), Multispectral, Multispectral-DSM, Vegetation Index, and Vegetation-Index-DSM, were used for tree crown detection and classification of live and dead trees. Five-fold cross-validation was employed to divide the manually annotated dataset of 21,800 live trees and 7157 dead trees into training and validation sets, which were used for training and validating the Mask R-CNN models. The results demonstrate that the RGB band combination achieved the most effective detection performance for live trees (average F1 score = 74.75%, IoU = 70.85%). The RGB–DSM combination exhibited the highest accuracy for dead trees (average F1 score = 71.16%, IoU = 68.28%). The detection performance for dead trees was lower than for live trees, which may be due to the similar spectral features across the images and the similarity of dead trees to the background, resulting in false identification. For the simultaneous detection of living and dead trees, the RGB combination produced the most promising results (average F1 score = 74.18%, IoU = 69.8%). It demonstrates that the Mask R-CNN model can achieve promising results for the detection of live and dead trees. Our study could provide forest managers with detailed information on the forest condition, which has the potential to improve forest management.

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.

Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.