Geology topics

Joseph F. Knight

Publications and source records attributed to Joseph F. Knight.

4 recordsLinked to original sources

USGS2025

Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics

Millions of small waterbodies are dispersed throughout the middle of the North American continent, and billions of dollars have been invested to conserve, restore, and manage these waterbodies in the 20th and 21st centuries. Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. These results indicate that sensors commonly used for surface water applications alone may not be able to accurately detect small waterbody surface water, which supports the need for combining monitoring and modeling to understand how small waterbodies may respond to future changes in climate and land use.

Minnesota↗

USGS2018

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km 2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.

Alaska↗

USGS2017

Historical and projected trends in landscape drivers affecting carbon dynamics in Alaska

Modern climate change in Alaska has resulted in widespread thawing of permafrost, increased fire activity, and extensive changes in vegetation characteristics that have significant consequences for socioecological systems. Despite observations of the heightened sensitivity of these systems to change, there has not been a comprehensive assessment of factors that drive ecosystem changes throughout Alaska. Here we present research that improves our understanding of the main drivers of the spatiotemporal patterns of carbon dynamics using in situ observations, remote sensing data, and an array of modeling techniques. In the last 60 yr, Alaska has seen a large increase in mean annual air temperature (1.7°C), with the greatest warming occurring over winter and spring. Warming trends are projected to continue throughout the 21st century and will likely result in landscape-level changes to ecosystem structure and function. Wetlands, mainly bogs and fens, which are currently estimated to cover 12.5% of the landscape, strongly influence exchange of methane between Alaska's ecosystems and the atmosphere and are expected to be affected by thawing permafrost and shifts in hydrology. Simulations suggest the current proportion of near-surface (within 1 m) and deep (within 5 m) permafrost extent will be reduced by 9–74% and 33–55% by the end of the 21st century, respectively. Since 2000, an average of 678 595 ha/yr was burned, more than twice the annual average during 1950–1999. The largest increase in fire activity is projected for the boreal forest, which could result in a reduction in late-successional spruce forest (8–44%) and an increase in early-successional deciduous forest (25–113%) that would mediate future fire activity and weaken permafrost stability in the region. Climate warming will also affect vegetation communities across arctic regions, where the coverage of deciduous forest could increase (223–620%), shrub tundra may increase (4–21%), and graminoid tundra might decrease (10–24%). This study sheds light on the sensitivity of Alaska's ecosystems to change that has the potential to significantly affect local and regional carbon balance, but more research is needed to improve estimates of land-surface and subsurface properties, and to better account for ecosystem dynamics affected by a myriad of biophysical factors and interactions.

Alaska↗

USGS2013

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Minnesota↗