Nilesh Timilsina

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.

The logging industry in the United States faces a demographic shift with aging owners and a decline in intergenerational business transfers. This pilot study explores the potential for new businesses led by minority groups, beginning loggers, and young entrepreneurs in the southeastern United States. Interviews with nine minority, beginning, and young (MBY) logging business owners and four forestry/logging associations uncovered challenges, including high startup costs, limited access to financing and funds, difficulty hiring skilled workers, low profit margins, and a lack of established professional networks. These MBY loggers are well educated and exhibit productive businesses, with an average investment of between $100,000 and $499,999. Proposed solutions to help more MBY logging business owners get started encompass government grants, low-interest loans, marketing initiatives, and legislative measures. Results are limited, and this study serves as a first glimpse into MBY logging business owners’ challenges; additional attention is needed for this population of logging business owners. Study Implications: Understanding the present status and challenges faced by minority, beginning, and young (MBY) logging business owners is instrumental for the industry, as the trend of intergenerational logging business transfer is declining. The study’s results suggest that support from the forest products industry and the government, such as government grants, low-interest loans, and strategic marketing, can enhance the sustainability of MBY logging businesses. These measures, coupled with legislative support, could help to address immediate challenges, such as high startup costs, and lay the groundwork for fostering a resilient logging industry.

Herbicide flashback is an undesirable response that results in damage or mortality of non-target plants near chemically treated plants. In severe instances, crop trees can be injured resulting in financial loss or inability to capture management objectives. Land managers need to know the potential limitations of using triclopyr acid (50% solution in water) and what quantity can cause damage to non-target trees (trees not aimed at for control via herbicide). Three naturally regenerated (previously clearcut) bottomland hardwood sites, ranging from 14–20 years of age, received partial overstory deadening using Trycera^® herbicide. Residual “leave” trees experienced indirect herbicide uptake resulting in some trees either experiencing mortality or partial mortality with top dieback/epicormic branching. An increased probability of damage occurred as tree diameter decreased and the number of treated stems around the non-target stems increased. American sycamore, green ash, and sweetgum were most susceptible to flashback. This study examines the unanticipated chemical effect of deadening adjacent stems surrounding residual leave trees in poorly drained soils.

Herbicide flashback is an undesirable response that results in damage or mortality of non-target plants near chemically treated plants. In severe instances, crop trees can be injured resulting in financial loss or inability to capture management objectives. Land managers need to know the potential limitations of using triclopyr acid (50% solution in water) and what quantity can cause damage to non-target trees (trees not aimed to control via herbicide). Three naturally regenerated (previous clearcut) bottomland hardwood sites, ranging from 14–20 years of age, received partial overstory deadening using Trycera® herbicide. Residual “leave” trees experienced indirect herbicide uptake resulting in some trees either experiencing mortality or partial mortality with top die-back/epicormic branching. An increased probability of damage occurred as tree diameter decreased and the number of treated stems around non-target stems increased. American sycamore, green ash, and sweetgum were most susceptible to flashback. This study examines the unanticipated chemical effect of deadening adjacent stems surrounding residual leave trees in poorly drained soils.

Urban tree canopy (UTC) is commonly used to assess urban forest extent and has traditionally been estimated using photointerpretation and human intelligence (HI). Artificial intelligence (AI) models may provide a less labor-intensive method to estimate urban tree canopy. However, studies on how human intelligence and artificial intelligence estimation methods compare are limited. We investigated how human intelligence and artificial intelligence compare with estimates of urban tree canopy and other landcovers. Change in urban tree canopy between two time periods and an assessment agreement accuracy also occurred. We found a statistically significant (p < 0.001) difference between the two interpretations for a statewide urban tree canopy estimate (n = 397). Overall, urban tree canopy estimates were higher for human intelligence (31.5%, 0.72 SE) than artificial intelligence (26.0%, 0.51 SE). Artificial intelligence approaches commonly rely on a training data set that is compared against a human decision maker. Within the artificial intelligence training region (n = 21) used for this study, no difference (p = 0.72) was found between the two methods, suggesting other regional factors are important for training the AI system. Urban tree canopy also increased (p < 0.001) between two time periods (2013 to 2018) and two assessors could detect the same sample point over 90 % of the time.