Factors affecting distribution and removal rates of small mammals in a lowland swamp forest

Smith, G. C.; Gentry, John B.; Kaufman, Donald W.; Smith, Michael H.

doi:10.4098/at.arch.80-5

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The site encompassed an area of 900 square kilometers and featured diverse landcover types, including vernal pools, rolling hills, wooded forests, and agricultural fields. Tully is characterized by its rural setting and has been previously documented in studies by Kappel & Miller (2003), Kappel (2014), andSmith (2014).…”

Section: Data and Study Areamentioning

confidence: 99%

Assessment of Image Classification Algorithms for Land Cover Classifications in Tully, Ny

Poudel,

Bhatti,

Bevilacqua

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The identification, delineation, and mapping of landcover is integral for resource management and planning as it establishes a baseline for thematic mapping and change detection analysis. The availability of high-resolution satellite imagery and the development of machine learning algorithms have significantly improved the prediction and accuracy of landcover classification. In this study, landcover classification is performed on seven-band Landsat 9 imagery and eight-band PlanetScope imagery for the village of Tully, NY, with an area of 900 square kilometers. The resolution of Landsat imagery is 30 meters, whereas the resolution of PlanetScope imagery is 3 meters. Classification schema is developed in ArcGIS Pro with five classification levels: conifer forest, hardwood forest, agriculture, developed, and water. Pixel-based supervised classification is performed using Support Vector Machine (SVM), Random Tress (RT), K-Nearest Neighbor (K-NN), and Maximum Likelihood Classifier (MLC). The reference dataset is acquired by an image interpreter using high-resolution imagery for map accuracy assessment. All the classification methods for Landsat imagery have more than 78% accuracy, but SVM performed best with 82% accuracy. For PlanetScope imagery, SVM performed best with 85% accuracy, whereas MLC had the lowest accuracy of 77%.

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Section: Data and Study Areamentioning

confidence: 99%