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Full-Text Articles in Life Sciences
Utilizing National Agriculture Imagery Program Data To Estimate Tree Cover And Biomass Of Piñon And Juniper Woodlands, April Hulet, Bruce A. Roundy, Steven L. Petersen, Stephen C. Bunting, Ryan R. Jensen, Darrell B. Roundy
Utilizing National Agriculture Imagery Program Data To Estimate Tree Cover And Biomass Of Piñon And Juniper Woodlands, April Hulet, Bruce A. Roundy, Steven L. Petersen, Stephen C. Bunting, Ryan R. Jensen, Darrell B. Roundy
Articles
With the encroachment of piñon (Pinus ssp.) and juniper (Juniperus ssp.) woodlands onto sagebrush steppe rangelands, there is an increasing interest in rapid, accurate, and inexpensive quantification methods to estimate tree canopy cover and aboveground biomass. The objectives of this study were 1) to evaluate the relationship and agreement of piñon and juniper (P-J) canopy cover estimates, using object-based image analysis (OBIA) techniques and National Agriculture Imagery Program (NAIP, 1-m pixel resolution) imagery with ground measurements, and 2) to investigate the relationship between remotely-sensed P-J canopy cover and ground-measured aboveground biomass. For the OBIA, we used eCognition® Developer …
Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer
Impact Of Multi-Scale Predictor Selection For Modeling Soil Properties, Bradley A. Miller, Sylvia Koszinski, Marc Wehrhan, Michael Sommer
Bradley A Miller
Applying a data mining tool used regularly in digital soil mapping, this research focuses on the optimal inclusion of predictors for soil–landscape modeling by utilizing as wide of a pool of variables as possible. Predictor variables for digital soil mapping are often chosen on the basis of data availability and the researcher's expert knowledge. Predictor variables commonly overlooked include alternative analysis scales for land-surface derivatives and additional remote sensing products. For this study, a pool of 412 potential predictors was assembled, which included qualitative location classes, elevation, land-surface derivatives (with a wide range of analysis scales), hydrologic indicators, as well …
Detection Of Temporal Changes In Vegetative Cover On South Padre Island, Texas Using Image Classifications Derived From Aerial Color-Infrared Photographs, Ruben A. Mazariegos, Kenneth R. Summy, Frank W. Judd, Robert I. Lonard, James H. Everitt
Detection Of Temporal Changes In Vegetative Cover On South Padre Island, Texas Using Image Classifications Derived From Aerial Color-Infrared Photographs, Ruben A. Mazariegos, Kenneth R. Summy, Frank W. Judd, Robert I. Lonard, James H. Everitt
Physics and Astronomy Faculty Publications and Presentations
Supervised image classifications developed from 23 x 23 cm aerial color-infrared aerial photographs (1:5,000 scale) were used to evaluate temporal changes in vegetative cover occurring within three 150 x 300-m research sites on South Padre Island, Texas. Use of high-resolution digitized imagery (ground pixel resolution of ca. 0.1 m) and survey-grade GPS for positional measurements of ground control points (20-25 1.0m2 targets within each research site) resulted in consistently high levels of geometric accuracy, with root mean square errors (RMSEs) ranging between 0.397 – 2.867. Similarly, use of relatively simple information categories (dry and wet sand, live and dead vegetative …
New Methodologies For Grasslands Monitoring, Katarzyna Dabrowska-Zielinska, Piotr Goliński, Marit Jorgensen, Jørgen Mølmann, Gregory Taff, Monika Tomaszewska, Barbara Golińska, Maria Budzynska, Martyna Gatkowska
New Methodologies For Grasslands Monitoring, Katarzyna Dabrowska-Zielinska, Piotr Goliński, Marit Jorgensen, Jørgen Mølmann, Gregory Taff, Monika Tomaszewska, Barbara Golińska, Maria Budzynska, Martyna Gatkowska
IGC Proceedings (1997-2023)
Monitoring grassland areas to assess changes in their condition over time has been the subject of a lot of research at different scales. Initially the techniques focused on field-based measurements, and modelling. However, several obtained data were site specific. Based on the increase in availability of remote sensing data and products, there is an expectation that remote sensing can provide rapid and definite answers to the challenges of detecting and monitoring grassland conditions and associated changes in productivity. At the time of European Copernicus Programme, the new possibilities of satellite data from the group of Sentinel satellites give the new …