Land use change in an agriculturally impaired sub-watershed of the Chesapeake Bay
Notice bibliographique
Résumé
The Chesapeake Bay watershed spans several states, supports diverse ecosystems, and is economically crucial to local communities. However, the land use throughout this region often has detrimental impacts on stream health. In particular, agricultural land use negatively affects water quality through nutrient and pesticide input, cattle trampling of streams, and increased sedimentation. In the Shenandoah Valley region of northwestern Virginia, part of the Chesapeake Bay watershed, agriculture is the primary land use. This has led to the designation of the Smith Creek watershed, located in the Shenandoah Valley, as a United States Department of Agriculture showcase watershed in 2010. Widespread restoration efforts have been conducted throughout the watershed over the past decade, such as improving in-stream habitat, establishing riparian buffers, and excluding cattle from streams. Linking stream health to land use, however, requires high resolution, up-to-date land cover classifications. The most recent such product for the study area was generated using 2013 imagery, which does not capture the restoration progress that has occurred in the Smith Creek watershed since 2010. The goals of this project are two-fold. First, this project aims to produce a high-resolution land cover classification using 2020 and 2021 imagery. Second, using the new land cover classification, the study will analyze land cover change in the Smith Creek watershed over the past decade. The project represents a collaboration between a Biology Department Master’s student and undergraduate students in the Geography Department at James Madison University. Ten meter (m) resolution imagery with four bands (red, green, blue, and red-edge) collected by the Sentinel 2 satellite in April 2020, September 2020, and January 2021 on days with no cloud cover obscuring the study region was obtained from the United States Geological Survey’s Earth Explorer database. Images were clipped to the Smith Creek watershed boundary using ArcPro v 2.7 (Esri, Redlands, CA) then merged together in PCI Geomatica 2018 (PCI Geomatics, Markham, Canada) to produce one 12-band image. A principal components analysis was conducted to identify the image bands that best differentiated pixels from one another, and the image was narrowed down to the five best bands. The image was then segmented and an object-based classification was conducted to classify land cover following the same categories as previous classifications in the study area. Once manual corrections and ground-truthing have been completed, a land cover change analysis will be conducted by comparing land use classification rasters from 2013 and potentially previous years as well.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».