Spatial techniques for multi-source national planted forest assessment and reporting
Notice bibliographique
Résumé
The national assessment of New Zealand’s planted forests is required for economic and environmental monitoring and for international reporting to organ isations such as FAO and the Montreal Process. Spat ial techniques can aid in determining national planted forest information; two approaches are described in this presentation. Firstly a number of national spatial datasets were investigated for their potential to c ontribute to the reporting on forest sustainability indicators. Seco ndly, spatial data was combined with non-spatial da ta to develop visual representations for enhanced reporting. For the investigation of national spatial datasets for reporting, one of the issues was to determine h ow well national datasets meet sustainability reporting req uirements. One method to determine this is to compare national data with data at a higher resolution; from this th e error margins in the reporting can be estimated. For example, one of the Montreal Process (MP) indicators (4.3.a) requires reporting on the proportion of forest man agement activities that meet best practice for protecting w ater resources. Riparian strips around waterways is one such practise; by overlaying GIS data of different resol utions for a number of case study areas and summing the differences in the riparian areas of each data set, it was determined that the resolution of the natio nal datasets would be inadequate for reporting on riparian pract ices. Using the same case study data, the effect of deducting the riparian areas from the national planted forest area (MP 2.a Area of forest land for wood producti on) indicated that the national datasets could over-est imate the land under productive forestry by up to 6 %. Monitoring based on sampling provides another avenue for generating reporting data; national datasets were used to guide the locations of national monitoring sites . A sampling approach developed by Environment Waikato for monitoring significant soil erosion, based on aeria l photography evaluations of sample points on a 2km grid, was applied to the whole country. The grid points were overlaid in GIS with land cover and erosion suscept ibility data, and the sampling intensity of particularly th e highly erodible forest lands was determined. This verified that the approach would be useful for national soil repo rting (MP 4.2.b Area of forest land with soil degra dation) though implementation of the approach on only areas of high risk could miss impacts elsewhere. Water quality monitoring is another field that reli es on a sampling approach; planted forest water qua lity reporting (MP 4.3.b water bodies in forest areas wi th significant changes) is based on those national water quality monitoring sites specifically for monitoring water flows from exotic forest catchments. The locations of these monitoring sites were assessed based on national GI S datasets. Land cover, river and catchment data we re combined to analyse whether the existing water monitoring sites are representative of exotic forest ca tchments, and to identify potential additional sites. The ana lysis determined that a number of the existing plan ted forest monitoring sites have other production land uses up -stream from their locations, such as grazed pastur e. In addition, different types of river environments are were found to be underrepresented in the national approach for monitoring water from planted forest. A number of new sites were recommended for this monitoring. Finally, a number of visual representations of the sustainability data were explored for the reporting of national forest data. The aim was to provide a quick overvie w of the state of sustainability indicators. The ap proach needed to cope with such issues as mixing quantitat ive and qualitative data, and variable numbers of i ndicators
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».