Esta colección de imágenes proporciona acceso a un monitoreo satelital de alta resolución de las zonas tropicales con el objetivo principal de reducir y revertir la pérdida de bosques tropicales, contribuir a combatir el cambio climático, conservar la biodiversidad, contribuir al rebrote, la restauración y el mejoramiento de los bosques, y facilitar el desarrollo sostenible, todo lo cual debe ser de uso no comercial. …

Questa raccolta di immagini fornisce l'accesso al monitoraggio satellitare ad alta risoluzione dei tropici allo scopo principale di ridurre e invertire la perdita di foreste tropicali, contribuire alla lotta contro il cambiamento climatico, conservare la biodiversità, contribuire alla ricrescita, al restauro e al miglioramento delle foreste e facilitare lo sviluppo sostenibile, il tutto per uso non commerciale. …

Bộ sưu tập hình ảnh này cung cấp quyền truy cập vào dữ liệu giám sát bằng vệ tinh có độ phân giải cao về vùng nhiệt đới với mục đích chính là giảm và đảo ngược tình trạng mất rừng nhiệt đới, góp phần chống lại biến đổi khí hậu, bảo tồn đa dạng sinh học, góp phần tái sinh, khôi phục và cải thiện rừng, đồng thời tạo điều kiện cho sự phát triển bền vững. Tất cả đều phải là mục đích Không nhằm mục đích thương mại. …

이 이미지 모음은 열대우림의 손실을 줄이고 되돌리는 것, 기후 변화 방지에 기여, 생물 다양성 보존, 산림 재생, 복원 및 개선, 지속 가능한 개발 촉진을 주된 목적으로 열대 지역의 고해상도 위성 모니터링에 대한 액세스를 제공하며, 이 모든 목적은 비상업적인 용도로만 사용해야 합니다. …

此图片集提供热带地区的高分辨率卫星监测数据，其主要目的是减少和扭转热带森林的流失，为应对气候变化、保护生物多样性、促进森林重新生长、恢复和增强以及促进可持续发展做出贡献，所有这些用途均不得用于商业用途。…

Overview

A geospatial dataset of point geometries with a land use / land cover label and several remote-sensing derived predictor variables that can be used to train and test a land use / land cover classifier.

This dataset was generated with support from a Climate Change AI Innovation Grant and the Australian Centre for International Agricultural Research.

Each of the point geometries was assigned one of the following class labels:

1. water
2. mangrove
3. bare soil / rock
4. urban / impervious
5. cropland / agriculture
6. grassland
7. shrubland
8. trees

The class property associated with each POINT feature stores the point's class label.

Class definitions

The cropland / agriculture class is defined as any location where agricultural activities associated with cropping or livestock management were visible in high-resolution images. Land that is recently fallow, but where evidence of cropping or grazing activities is present, would be labelled as cropland. Grassland is defined as any low vegetation (e.g. below knee height) without a bush, shrub, or woody structure. Scrubland is defined as any vegetation that is below head height, does not form a closed canopy, and has a clearly visible bush, shrub, or woody structure. Trees are defined as any vegetation greater than head height forming a clear canopy.

Methods

Image interpretation and labelling points with a land cover class was undertaken within a custom Google Earth Engine application. Within a region of interest, a year’s worth of Sentinel-2 images was clustered into 15 classes using a k-means algorithm. A stratified random sample of points was generated for manual labelling using clusters as strata. Ground truth datasets ere generated in the Ba, Magodro, Rewa, Sigatoka, RakiRaki, Sigatoka, Suva, Suva (urban), Lautoka (urban), Noco, Vuya, Nadi, and Labasa regions.

To support image interpretation and labelling a point’s land cover using high-resolution images (Google satellite basemaps), ancillary datasets were used (e.g. Planet and Sentinel-2 images) in conjunction with field verification.

Two quality-checks were applied to the labelled land cover points. First, each point was manually screened and quality checked to ensure consistency in class labels. Second, using Planet NICFI basemaps and Sentinel-2 RGB composites 2019, 2020, and 2021, each of the labelled land cover points was screened for a change in land cover event occurring at any point during those three years. If a change in land cover was observed, the point was dropped from the dataset.

For each labelled point in 2019, 2020, and 2021 features were extracted comprising annual median cloud free spectral reflectance across Sentinel-2 wavebands, monthly NDVI composites, and annual median NDVI, NDBI, NDWI, and GCVI bands and elevation, slope, and aspect bands.

This resulted in a dataset of 13,914 labelled points across three years: 2019, 2020, and 2021. The difference in the number of points across years is due to cloud cover preventing features being generated in some years

Feature definitions:

• B_* - median annual cloud free spectral reflectance for Sentinel-2 wavebands
• ndvi - median annual cloud free NDVI computed from Sentinel-2
• gcvi - median annual cloud free GCVI computed from Sentinel-2
• ndwi - median annual cloud free NDWI computed from Sentinel-2
• ndbi - median annual cloud free NDBI computed from Sentinel-2
• ndvi_* - median monthly cloud free NDVI computed from Sentinel-2
• elevation - elevation computed from SRTM
• aspect - aspect computed from SRTM
• slope - slope computed from SRTM

Cette collection d'images permet d'accéder à la surveillance par satellite haute résolution des régions tropicales dans le but principal de réduire et d'inverser la perte des forêts tropicales, de contribuer à la lutte contre le changement climatique, de préserver la biodiversité, de contribuer à la repousse, à la restauration et à l'amélioration des forêts, et de faciliter le développement durable, qui doivent tous être à usage non commercial. Pour savoir comment accéder aux cartes de base, suivez ces instructions. Les mosaïques du programme de données satellitaires NICFI (également appelées mosaïques Planet-NICFI) contiennent des collections mensuelles et semestrielles générées tous les six mois. Le type de la mosaïque est stocké dans le champ de métadonnées de l'image "cadence". Utilisez ce champ ainsi que les dates de début et de fin de chaque mosaïque pour trouver les images souhaitées. Vous trouverez des informations complètes sur les cartes de base dans la spécification des cartes de base du programme de données satellitaires NICFI. Pour en savoir plus sur le programme de données satellitaires de l'initiative internationale sur le climat et les forêts de la Norvège (NICFI) et sur les données proposées, veuillez consulter le site Web du programme. Pour appuyer la mission du NICFI, vous pouvez utiliser ces données pour un certain nombre de projets, y compris, mais sans s'y limiter: Promouvoir la recherche scientifique sur les forêts tropicales du monde et les services essentiels qu'elles fournissent. Mettre en œuvre et améliorer les règles de gestion durable des forêts et de l'utilisation des terres dans les pays et les juridictions en développement possédant des forêts tropicales. Améliorer la transparence et la responsabilité dans les régions tropicales Protéger et améliorer les droits des peuples autochtones et des communautés locales dans les pays possédant des forêts tropicales Innover des solutions pour réduire la pression exercée sur les forêts par les marchés financiers et les produits de base mondiaux

इस इमेज कलेक्शन में, उष्णकटिबंधीय इलाकों की उपग्रह से ली गई हाई रिज़ॉल्यूशन वाली तस्वीरों का ऐक्सेस मिलता है. इनका मुख्य मकसद, उष्णकटिबंधीय जंगलों के नुकसान को कम करना और उन्हें वापस लाना है. साथ ही, जलवायु परिवर्तन से निपटने, जैव विविधता को बनाए रखने, जंगलों को फिर से हरा-भरा करने, उन्हें बेहतर बनाने, और टिकाऊ विकास को बढ़ावा देने में मदद करना है. इनका इस्तेमाल, सिर्फ़ गैर-व्यावसायिक मकसद से किया जाना चाहिए. …