Bi(Nd) is Halite, Rock Salt structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. Nd is bonded to six equivalent Bi atoms to form a mixture of edge and corner-sharing NdBi6 octahedra. The corner-sharing octahedral tilt angles are 0°. All Nd–Bi bond lengths are 3.26 Å. Bi is bonded to six equivalent Nd atoms to form a mixture of edge and corner-sharing BiNd6 octahedra. The corner-sharing octahedral tilt angles are 0°.

NDBI zip

Data obtained from computational DFT calculations on Cubic NdBi is provided. Available data include crystal structure, bandgap energy, stability, density of states, and calculation input/output files.

IntroductionThe effectiveness of early interventions in young autistic children is well established, but there is great interindividual variability in treatment response. Predictors of response to naturalistic developmental behavioral interventions (NDBI), like the Early Start Denver Model (ESDM), are needed.MethodsWe conducted an exploratory study to prospectively seek predictors of response in 32 young children treated with ESDM after receiving an ASD diagnosis. All children were less than 39 months old (mean age: 29.7 mo), and received individualized ESDM for nine months. Tests were administered at the beginning, after 4 months, and at the end of treatment.ResultsFour children (12.5%) were “strong responders”, 8 children (25.0%) were “moderate responders”, and 20 children (62.5%) were “poor responders”. A more favorable response to ESDM was significantly predicted by higher PEP-3 Expressive Language, Receptive Language, Cognitive Verbal/Preverbal, Visuo-Motor Imitation scores, higher GMDS-ER Personal/Social, and VABS-II Communication scores, by lower ADI-R C restricted/stereotypic behaviors, and by joint attention level.DiscussionMost predictors showed a linear association with increasing response to ESDM, but GMDS-ER Personal-Social and joint attention level predicted strong response, while PEP-3 receptive language equally predicted moderate or strong response. Although larger samples will be necessary to reach definitive conclusions, in conjunction with prior reports our findings begin providing information able to assist clinicians in choosing the most appropriate treatment program for young autistic children.

This dataset contains Lake Catchment Change Indicators data produced within the Earth Observation Climate Information Service (EOCIS) project by Plymouth Marine Laboratory.

The Product(s) can be used to map daily variations in the reflectance of water bodies contained in the target lake catchment. The derived quantities turbidity and chlorophyll-a can be used to determine variability in the optical and biochemical conditions of the lake and other included water bodies.

A set of vegetation, built-up area, and water indices are included to aid users in selection data ranges and locations of interest. These include the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI) Augmented Normalized Difference Water Index (ANDWI) and Modified Normalized Difference Water Index (MDNWI).

These data sets have been created following the specific format for climate data at high resolution for the UK (CHUK) within the EOCIS project. The CHUK grid consists of 100m x 100m cells in British National Grid (BNG) projection. The CHUK grid covers the whole area of the British Isles, an area approximately 1,000km x 1,500km, whereas the data sets presented here are limited to parts of this grid for the extent of each lake catchment.

Urban Green Cover and Built-Up Analysis of Melbourne

This dataset explores urban green cover and built-up surface changes across Melbourne — one of Australia’s fastest-growing metropolitan regions, using Sentinel-2 satellite imagery and two key indices:

NDVI (Normalized Difference Vegetation Index) : measuring vegetation greenness and vitality.

NDBI (Normalized Difference Built-up Index): detecting built-up and impervious surfaces.

Each CSV file represents a specific date over the past six months and includes summary values for NDVI and NDBI within a 50 x50 km window around greater Melbourne.

Why this matters

Urban expansion and climate adaptation are deeply connected. Tracking NDVI and NDBI together helps us understand how green spaces shrink or recover as cities grow. These metrics can reveal subtle trends — like seasonal vegetation recovery or increased built-up density — that influence urban heat islands, air quality, and local biodiversity.

Data Details

Each CSV file contains the following statistical metrics derived from Sentinel-2 imagery (10 m resolution):

Metric Description mean_ndvi Average vegetation greenness across the region median_ndvi Median NDVI value, a stable measure of greenness stddev_ndvi Standard deviation showing variability in vegetation mean_ndbi Average built-up intensity median_ndbi Median NDBI value stddev_ndbi Variability of built-up surfaces

The values are computed from Cloud-Optimized GeoTIFFs (COGs) processed by I Hug Trees’ AWS-based environmental analytics pipeline.

Data Source and Credits

These datasets are part of the Urban Greenness and Built-up Monitoring Program by I Hug Trees — an environmental data initiative combining open satellite imagery and AI-assisted analytics. Each file originates from I Hug Trees’ private AWS S3 data archive , hosting time-series imagery, NDVI, and NDBI data for global cities.

Related Research and Visualization

For visual insights, map overlays, and biweekly reports, visit:

I Hug Trees – Green Cover Reports

Research page – Melbourne Urban Green Cover Change Analytics

I Hug Trees Blog

Suggested Uses

Urban planning and environmental policy analysis

NDVI–NDBI correlation studies

Climate and sustainability visualizations

Machine learning models predicting urban greenness trends

📚 How to Cite This Dataset

If you use this dataset in your research, article, or analysis, please cite as follows:

I Hug Trees (2025). "Urban Green Cover and Built-Up Index Analysis – Melbourne Region."
Dataset derived from Sentinel-2 satellite imagery.
Retrieved from https:///ihugtrees.org/data-analytics/sentinel-ndvi/Melbourne-region-history/2025/2025-05-01/time_series.csv
Author: Ramkumar Yaragarla
Publisher: I Hug Trees Data Analytics Initiative Website: I Hug Trees

Suggested citation (APA format):
Yaragarla, R. (2025). Urban Green Cover and Built-Up Index Analysis – Melbourne Region (v1.0) [Data set]. I Hug Trees. https://ihugtrees.org/data-analytics/sentinel-ndvi/Melbourne-region-history/

License

Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0)

This composition appears in the Bi-Nd region of phase space. It's relative stability is shown in the Bi-Nd phase diagram (left). The relative stability of all other phases at this composition (and the combination of other stable phases, if no compound at this composition is stable) is shown in the relative stability plot (right)

For the needs of the “Portal for heritage buildings integration into the contemporary built environment”, in short, PERIsCOPE project, satellite observations were applied. This repository includes the results from the macro-scale analysis, for which thermal data, optical satellite images, and ready satellite products were exploited to provide multi-temporal information.

Satellite-based products estimate the temperature variations in a broader area for the selected urban testbeds (Limassol and Strovolos municipalities, Cyprus). Landsat 7 and 8 archives were downloaded through the EarthExplorer platform (“Landsat Collection 1 Level-1” for both “Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1” and “Landsat 8 OLI/TIRS C1 Level-1”). The Level-1 data downloaded from the platform were rescaled to the top of atmosphere (TOA) reflectance and radiance using radiometric rescaling coefficients provided in the metadata file delivered with the Level-1 product (metadata—MTL file).

More than 140 satellite images were selected (a cloud coverage filter was applied), downloaded, and processed, covering the period between 2013 and 2020. Specifically, 16 images during the Winter season, 30 images over Spring, 57 images for Summer, and 38 during Autumn were finally gathered for both case studies.

The Google Earth Engine cloud platform infrastructure was used to extract optical products, namely the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-up Index (NDBI), which characterise vegetated and built-up areas, respectively.

This repository concerns: (1) the mean Land Surface Temperature (LST) for both test sites (from 2013-2020), their standard deviation, and the maximum and minimum values. In addition, the (2) NDVI and (3) NDBI products per year (2013-2020) are provided. Lastly, (4) the seasonal variations are included.

The data are structured in both forms of ArcGIS Pro Geodatabase (.gdb), while individual .tiff formats are provided in each folder.

Further details can be found in the following references:

1. Agapiou, A.; Lysandrou, V. Observing Thermal Conditions of Historic Buildings through Earth Observation Data and Big Data Engine. Sensors 2021, 21, 4557. https://doi.org/10.3390/s21134557

2. Agapiou A., Lysandrou V., Cuca B., Copernicus earth observations for cultural heritage, Proceedings of the joint international event, 9th ARQUEOLÓGICA 2.0 & 3rd GEORES, Valencia (Spain). 26–28 April 2021, DOI: https://doi.org/10.4995/Arqueologica9.2021.12512

IntroductionSeagrass ecosystems are vital for carbon sequestration, shoreline stabilization, habitat provision, and nutrient cycling, thereby playing a key role in climate mitigation and adaptation. Understanding how anthropogenic pressures affect seagrass ecosystems is essential for effective coastal zone management.MethodsBy integrating decadal satellite-based urbanization metrics with in-situ measurements of seagrass biomass and sediment organic carbon at four sites (Pamban, Mandapam North, Devipattinam, Thondi) for the periods 2010 and 2022, we quantified how the urban expansion affected the seagrass biomass and sediment carbon storage potential. Landsat series satellite imagery was analyzed using the Normalized-Difference-Built-up Index (NDBI) to quantify urban expansion. Relationships with seagrass parameters were analyzed through multivariate statistical approaches, including principal component analysis (PCA).ResultsResults indicated a significant increase in built-up area (~12 Km2) near high-impact sites (Devipattinam, Thondi), versus ~4 Km2 increase at low-impact sites (Pamban and Mandapam North). For the built-up areas, this correlates with elevated suspended sediment matter (SSM) (31.2% increase in high urban sites), reductions in seagrass above-ground biomass (AGB: -25.52% of high urban sites), and decreased sediment organic carbon (SOC: -42.67%). Multivariate analyses, also revealed strong associations between urbanization, SSM, biomass loss, and sediment organic carbon reduction.Discussion and conclusionThese findings demonstrate that coastal urbanization in Palk Bay significantly undermines seagrass blue carbon potential and beneficial ecosystem services. The integrated field and remote sensing approach provides a scalable framework for monitoring tropical seagrass ecosystems, offering actionable insights for coastal zone management, conservation, and climate mitigation under increasing anthropogenic pressures.

Normalized Difference Built Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) are remote sensing derived indexes that estimate land cover of impervious surface, vegetation, and water, respectively. NDVI is one measure of “greenness” which has been used in public health studies.

The rapid population growth in British Columbia has led to the necessity of innovative housing solutions. Local municipalities in BC, such as Bowen Island, are exploring the implementation of Density Transfer Modelling (DTM) as a planning tool to address these challenges. The study examines Density Transfer Modelling by Geographic Information System (GIS) application on Bowen Island, managed under the Islands Trust Act, to balance development with ecological preservation. This involves identifying "donor" sites (areas of high ecological value with existing development) to transfer development rights from, and "receiver" sites (areas suitable for increased urban density) using the Normalized Difference Built-up Index and residential density classifications. Two main Comprehensive Development Areas (CDAs) on Bowen Island, Arbutus Ridge and Snug Cove are highlighted. The DTM calculates that this area supports the development of up to 30 additional detached homes in Arbutus Ridge Development Area. The Snug Cove Comprehensive Development Area (Snug Cove CDA) has been identified as a key area for increased residential development with a focus on increasing affordability and creating a pedestrian-friendly environment. According to DTM calculations Snug Cove Residential Area supports the development of 2186 dwelling units. The goal of the Snug Cove Development Area is to build a variety of housing types, including duplexes, triplexes, and multi-unit buildings, clustered near essential services and transportation hub(ferry). Both CDAs exemplify how density transfer modellings can be effectively utilized within designated development areas to support sustainable urban planning goals.

BackgroundWhile early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world’s children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care.ObjectivesFirst, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa.MethodsThis is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18–72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants.ResultsParticipant enrolment started in April 2023. Estimated primary completion date is March 2027.ConclusionThe ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators.Trial registrationNCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).

The image index mosaics called S2ind are computed from Sentinel-2 images. Time period is from early April to late October, and the mosaics are computed on the 15th and the last day of month, each mosaic consisting of images of previous month. The purpose of these index mosaics is to enhance some physical property of target, like vegetation or built-up areas. Computed image indices are Normalized Difference Vegetation Index (NDVI), Tillage Index (NDTI), Built-up index (NDBI), Snow index (NDSI) and Moisture index (NDMI).

Sentinel-2 image index mosaics are produced using images acquired using the MSI-instrument of the Sentinel 2A and 2B satellites. The original data is obtained automatically as tile packages (granules) from the Sodankylä National Satellite Data Center (NSDC) data archive and processed at CalFin-cluster of National Satellite Data Centre. Following image indices are computed:

• NDVI = (B8-B4)/(B8+B4)
• NDTI = (B11-B12)/(B11+B12)
• NDMI = (B8-B11)/(B8+B11)
• NDBI = ((B11-B8)/(B11+B8))-((B8-B4)/(B8+B4))
• NDSI = (B3-B11)/(B3+B11)

where B* indicates the Sentinel-2 image band Top-of-Atmosphere reflectance used in index computation.

The monthly mosaics are composed based on maximum NDVI, in other words the pixel values to mosaics are selected from image where the NDVI value is the highest within selected time frame. Then, pixel values are scaled from interval -1,...,1 to interval 0,...,200 and saved as 8-bit integers instead of 32-bit real numbers. This has been done in order to reduce data size. The amount of monthly Sentinel-2 data is so large that it is not possible to process the whole Finland as one process. Instead, Finland has been divided to ten areas and mosaics are done for each area, and then these mosaics of areas are transformed to TM35Fin-coordinate system (EPSG 3067) and mosaicked to cover whole Finland and surroundings. The single mosaics are stored in Cloud-Optimized-Geotiff format. The final spatial resolution of S2ind-image index mosaics is 10 m per pixel. The metadata mosaic, in other words the number of day from the start of the year, is called META.

The mosaics as available from the interfaces of NSDC which are:

• WMS (Geoserver): https://data.nsdc.fmi.fi/geoserver/wms
• WCS (Geoserver): https://data.nsdc.fmi.fi/geoserver/wcs

• S3-bucket: Mosaics are also available using URL, like http://pta.data.lit.fmi.fi/sen2/s2m_type/pta_sjp_s2ind_type_startdate_enddate.tif

• type: the type of mosaic, ndvi, ndti, ndmi, ndbi or ndsi for different image indices and meta for metadata mosaic

• startdate: the start date of mosaic, the 1st or 15th day of the month

• enddate: the end date of the mosaic, the 15th or the last day of the month

• The start and end dates have to cover period of one month, e.g. 20190401-20190430 or 20180615-20180715.

• Example URL https://pta.data.lit.fmi.fi/sen2/s2m_ndvi/pta_sjp_s2ind_ndvi_20190401_20190430.tif Viewing services are

• SYKE Tarkka-viewing service (www.syke.fi/tarkka).

• FMI Sentinel catalog (https://pta.fmi.fi/)

These S2ind-mosaics are produced by Finnish Environment Institute SYKE using CalFin-cluster of Sodankylä National Satellite Data Center, and they were developed as part of sub program “Distribution and Processing of Satellite Imagery” of "Geospatial Platform of Finnish Public Administration"-program (2017-2019). This SYKE’s dataset can be used according to open data license (CC BY 4.0).

Kuvaindeksimosaiikit S2ind lasketaan Sentinel-2 kuvista. Ajanjakso on vuosittain huhtikuun alusta lokakuun loppuun, mosaiikit lasketaan kuukauden 15 ja viimeinen päivä ja kukin mosaiikki käsittää edellisen kuukauden kuvat. Kuvaindeksimosaiikkien tarkoituksena on korostaa jotain kohteen ominaisuutta kuten kasvillisuuden tai elottoman alueen esiintyminen. Lasketut kuvaindeksit ovat Normalized Difference Vegetation Index (NDVI, kasvillisuusindeksi), Tillage Index (NDTI, maanmuokkaus), Built-up index (NDBI, eloton alue), Snow index (NDSI, lumi) ja Moisture index (NDMI, kosteus).

Sentinel-2 kuvaindeksimosaiikit tuotetaan Sentinel-2A ja -2B satelliittien MSI-instrumentin ottamista kuvista. Prosessointi tapahtuu Ilmatieteen laitoksen Kansallisen satelliittidatakeskuksen infrastruktuuria hyödyntäen, kuvat on arkistoitu sinne ja prosessoitu CalFin-prosessointiklusterissa. Kuvaindeksit ovat

• NDVI = (B8-B4)/(B8+B4)
• NDTI = (B11-B12)/(B11+B12)
• NDMI = (B8-B11)/(B8+B11)
• NDBI = ((B11-B8)/(B11+B8))-((B8-B4)/(B8+B4))
• NDSI = (B3-B11)/(B3+B11)

jossa B* on Sentinel-2 kuvan kanavan * Top-of-Atmosphere-reflektanssi.

Mosaikointi perustuu ajanjakson maksimi-NDVI-arvoon, eli mosaiikin pikselin indeksiarvot valitaan siitä kuvasta jolla NDVI-arvo on kaikkein suurin. Pikselin arvot skaalataan lukualueesta -1,...,1 lukualueeseen 0,...,200 ja talletetaan käyttäen 8-bittisiä kokonaislukuja, 32-bittisten reaalilukujen sijaan. Tämä tehdään tallennettavan datamäärän pienentämiseksi. Sekä koko mosaiikin pinta-ala että kuukausittaisten kuvien lukumäärä on sen verran suuri, että koko mosaiikkia ei lasketa yhtenä prosessina vaan Suomi on jaettu kymmeneen alueeseen. Ensinnä muodostetaan kunkin alueen oma mosaiikki jotka koordinaatistomuunnosvaiheessa maantieteellinen Lat/Lon-koordinaatisto -> TM35Fin- koordinaatisto yhdistetään koko Suomen ja lähialueet kattavaksi mosaiikiksi. Mosaiikit on tallennettu yksittäin omiksi Cloud-Optimized Geotiff-tiedostoiksi. Mosaiikkien pikselikoko on 10 metriä. Metadatamosaiikkia, eli päivän numero laskien vuoden alusta, kutsutaan nimellä META.

Mosaiikit on saatavilla Kansallisen satelliittidatakeskuksen käyttöliittymistä, jotka ovat

• WMS (Geoserver): https://data.nsdc.fmi.fi/geoserver/wms
• WCS (Geoserver): https://data.nsdc.fmi.fi/geoserver/wcs

• S3-bucket: Mosaiikit on myös saatavissa käyttäen URL-osoitetta kuten http://pta.data.lit.fmi.fi/sen2/s2m_type/pta_sjp_s2ind_type_startdate_enddate.tif jossa

• type: mosaiikin tyyppi, indeksimosaiikit ndvi, ndti, ndmi, ndbi tai ndsi ja meta tarkoittaa metadatamosaiikkia

• startdate: mosaiikin ajanjakson aloituspäivä, kuukauden 1. tai 15. päivä

• enddate: mosaiikin ajanjakson lopetuspäivä, kuukauden 15. tai viimeinen päivä

• Aloitus- ja lopetuspäivien välisen ajanjakson täytyy kattaa kuukausi, esimerkiksi 20190401-20190430 tai 20180615-20180715.

• Esimerkki URL https://pta.data.lit.fmi.fi/sen2/s2m_ndvi/pta_sjp_s2ind_ndvi_20190401_20190430.tif

Katselupalveluita ovat muun muassa

• SYKE Tarkka (https://www.syke.fi/tarkka).
• FMI Sentinel catalog (https://pta.fmi.fi/)

Suomen Ympäristökeskus tuottaa nämä Sentinel-2 kuvaindeksimosaiikit käyttäen Kansallisen satelliittidatakeskuksen CalFin-prosessointiklusteria, ja ne on kehitetty osana Paikkatietoalusta-hankkeen (2017-2019) Satelliittidatan jakelu ja prosessointi-osahanketta. Aineisto kuuluu SYKEn avoimiin aineistoihin (CC BY 4.0).

S2ind-mosaics have been developed as part of Finnish Geospatial Platform project (http://www.paikkatietoalusta.fi/en), which started 2017 and was completed at the end of 2019.

El dashboard “NDBI, NDVI, NDWI y MNDWI en las Zonas Metropolitanas de México (2002–2022)” permite analizar de forma interactiva la relación entre diferentes índices espectrales derivados de imágenes satelitales y la temperatura promedio superficial en las 74 zonas metropolitanas del país, de acuerdo con la delimitación SEDATU–CONAPO–INEGI (2015). El tablero ofrece dos tipos principales de análisis: Correlaciones entre cada índice espectral (NDBI, NDVI, NDWI y MNDWI) y la temperatura promedio, mostrando los coeficientes de correlación de Pearson, Spearman y Kendall Tau junto con sus valores p. Correlaciones cruzadas entre pares de índices (por ejemplo, NDVI vs. NDWI), lo que permite examinar las relaciones entre cobertura vegetal, cuerpos de agua y superficie edificada. También incluye gráficos de series temporales que muestran la evolución de cada índice y de la temperatura promedio a lo largo del periodo 2002–2022, así como un mapa coroplético que representa espacialmente los valores de correlación por zona metropolitana, facilitando la comparación entre regiones. Los cálculos se realizaron a partir de imágenes del conjunto USGS Landsat 7 Surface Reflectance Tier 1 (Colección 2, Nivel 2), procesadas en la plataforma Google Earth Engine para el periodo comprendido entre enero de 2002 y enero de 2023. Para cada mes y zona metropolitana se seleccionaron las 15 imágenes con menor nubosidad, descartando aquellas con un promedio superior al 20 por ciento. Los índices se calcularon utilizando las siguientes combinaciones de bandas de reflectancia superficial:

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

Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest lands areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000-2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 sq. km in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity.

Beta Notice: This item is currently in beta and is intended for early access, testing, and feedback. It is not recommended for production use, as functionality and content are subject to change without notice.Sentinel-2, 10m Multispectral 13-band imagery, rendered on-the-fly. Available for visualization and analytics, this Imagery Layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.This imagery layer can be used for multiple purposes including but not limited to vegetation, land cover, plant health, deforestation and environmental monitoring.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean SeaNote: Areas of interest going beyond the Mission baseline (as laid out in the Mission Requirements Document) will be assessed, and may be added to the baseline if sufficient resources are identified.Temporal CoverageThe revisit time for each point on Earth is every 5 days.This layer is updated daily with new imagery.This imagery layer is designed to include imagery collected within the past 14 months. Custom Image Services can be created for access to images older than 14 months.The number of images available will vary depending on location. Image Selection/FilteringThe most recent and cloud free image, for any location, is displayed by default.Any image available, within the past 14 months, can be displayed via custom filtering.Filtering can be done based on Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More…NOTE: Not using filters, and loading the entire archive, may affect performance.Analysis ReadyThis imagery layer is analysis ready with TOA correction applied. Visual RenderingDefault rendering is NDVI Colormap (Normalized Difference vegetation index with colormap) computed as NIR(Band8)-Red(Band4)/NIR(Band8)+Red(Band4) . The raw version of this layer is NDVI-Raw.Green represents vigorous vegetation and brown represents sparse vegetation.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI Raw, Normalized Burn Ratio Multispectral Bands BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional Notes Overviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available. NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request.Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , or alternatively access Sentinel2Look Viewer, EarthExplorer or the Copernicus Open Access Hub to download the scenes. For information on Sentinel-2 imagery, see Sentinel-2.

The building form attributes (NDBI, BD, FAR, AH, and HSTD) and nonbuilding form parameters (SuVC and SVF) were systematically quantified. GIS-based processing integrates all spatial datasets. The urban block distance to Dianchi Lake (UB-DDL) and the urban block distance to forested mountain area (UB-DFMA) were quantified via the buffer zone tool in GIS software. In addition, LCZ mapping for non-built-up types is categorized on the basis of two indicators, the NDBI and SuVC. LST data were acquired in February, April, July and October 2022, with concurrent vegetation cover measurements collected during four campaigns.

Measures of the environmental inequality among Culturally and Linguistically Diverse (CALD) populations derived from the time series extracted from Landsat satellite images in the years 2001, 2006, 2011, 2016, 2021, including land surface temperature, urban heat intensity index, NDVI (normalised difference vegetation index) and NDBI (normalised difference built-up index) for each SA1. The time-series datasets were retrieved on Google Earth Engine using ABS SA1 2021 boundary.

This dataset captures multi-decadal patterns of vegetation dynamics, urban expansion, and ecological risk across the City of Vancouver using consistent Landsat satellite observations from 1990 to 2025. Vancouver provides a uniquely relevant case study due to its rapid population growth, strong environmental planning reputation, and location within the highly biodiverse and climate-sensitive Pacific Northwest. This Dataset is primarily analysis results of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were calculated from Landsat spectral bands for each year at the pixel level. The dataset offers a comprehensive view of how ecological conditions in a model green city have evolved under sustained urban pressure by comparing urban sprawl and vegetation loss during the peak vegetation summer months.