https://spdx.org/licenses/CC0-1.0https://spdx.org/licenses/CC0-1.0
We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.
In 2022, the U.S. states with the highest rates of Lyme disease were Rhode Island, Vermont, and Maine. However, the states with the highest total number of Lyme disease cases were New York, Pennsylvania, and New Jersey. That year, there were a total of 2,653 cases of Lyme disease in the state of Maine, with an incidence rate of 192.6 per 100,000 population.
What is Lyme disease? Lyme disease is caused by bacteria usually transmitted to humans through the bite of a tick. Lyme disease is the most common vector-borne disease in the United States, however it is much more prevalent in some states than others, with the upper Midwest and the Northeastern states most at risk. Symptoms of Lyme disease can vary and usually come in stages but may include a rash, fever, headache, stiffness in the joints, tiredness, and muscle aches and pains. Lyme disease is usually treated with antibiotics. In 2022, funding for Lyme disease from the National Institutes of Health (NIH) totaled around 50 million U.S. dollars.
Trends in Lyme disease Although the number of Lyme disease cases per year fluctuates, over the past couple decades, the number of Lyme disease cases in the United States has steadily increased. Between 1996 and 2022, the highest number of Lyme disease cases was in the year 2022 when over 62,500 cases were reported. The lowest number reported during this period was in 1997, with around 12,800 cases. Cases of Lyme disease are much more common in the summer months of June and July as this is when people are most likely to encounter ticks. The risk of Lyme disease is expected to increase in the future as climate change contributes to an expanded habitat for ticks.
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https://spdx.org/licenses/CC0-1.0https://spdx.org/licenses/CC0-1.0
We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.