The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution. The HLSL30 Vegetation Indices (HLSL30_VI) product is derived from Landsat 8 and Landsat 9 OLI data products. Vegetation indices combine specific bands of satellite data to quantify various aspects of vegetation. Analysis of vegetation indices allows for tracking changes in vegetation over time, identifying areas of stress or deforestation, and assessing crop health. Vegetation indices provide a reliable and efficient means of understanding the complex dynamics of vegetation health. The HLSS30_VI and HLSL30_VI products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSL30_VI product is provided in Cloud Optimized GeoTIFF (COG) format, and each variable is distributed as a separate file. Nine indicators of vegetation health are included in the HLSL30_VI product: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Triangular Vegetation Index (TVI). See the User Guide for a more detailed description of the individual vegetation health variables provided in the HLSL30_VI product.

The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution. The HLSL30 Vegetation Indices (HLSL30_VI) product is derived from Landsat 8 and Landsat 9 OLI data products. Vegetation indices combine specific bands of satellite data to quantify various aspects of vegetation. Analysis of vegetation indices allows for tracking changes in vegetation over time, identifying areas of stress or deforestation, and assessing crop health. Vegetation indices provide a reliable and efficient means of understanding the complex dynamics of vegetation health. The HLSS30_VI and HLSL30_VI products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system, and thus are “stackable” for time series analysis. The HLSL30_VI product is provided in Cloud Optimized GeoTIFF (COG) format, and each variable is distributed as a separate file. Nine indicators of vegetation health are included in the HLSL30_VI product: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Triangular Vegetation Index (TVI). See the User Guide for a more detailed description of the individual vegetation health variables provided in the HLSL30_VI product.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Hyperspectral Vegetative Indices data product (GLHYVI) is to provide vegetative, stress, and other index data in 44 science dataset layers. Included in the product are vegetative indices such as Normalized Difference Vegetation Index (NDVI), Triangular Vegetation Index (TVI), Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Difference Vegetation Index (DVI). Stress indices include, but are not limited to, Carter Stress, Gitelson and Merzlyac Stress, Maccioni Stress, and Vogelmann Stress.GLHYVI data are processed as a raster data product (GeoTIFF) at 1 meter spatial resolution over locally defined areas. A browse image displaying NDVI is also included.

IGUALA is the advanced analytical system of the Madrid City Council that allows the creation of the Aggregate Territorial Vulnerability Index (IVTA). It is a data driven system, which makes decisions based on the data collected by the City Council's information systems. The information is grouped into 5 areas of composition: · Social welfare and equality (BI): incorporates information on actions aimed at improving the quality of life in the field of social services, equality, security and community integration. · Urban environment and mobility (MA) : focuses on the quality of urban life. It is determined by the possibilities and barriers that citizenship encounters, in three basic dimensions: environment, mobility and urbanism-public road. · Education and culture (EC) : includes information on levels of study, absenteeism and the provision of cultural services. It presents itself as a facilitator of change on the social scale and provides opportunities in the most vulnerable territorial units. · Economy and employment (EE) : has a more direct impact on the revenue generated by a territorial unit. Elements such as commercial activity, unemployment and family economic situation are key factors to be able to determine it. · Health (HS) : It revolves around the characteristics and special needs that a person can have in terms of health, in the agility in the care and in the quality of the services offered. Each year the Territorial Vulnerability Indices (TVI) are calculated for each area as a weighted sum of different indicators and from them the Aggregate Territorial Vulnerability Index (TAVI). In addition, as supplementary information, other descriptive indicators are also published for each district and neighbourhood, which provide data relating to socio-demographic or area-specific characteristics. You can learn more about the methodology used at: More information on this initiative can be found at: https://iguala.madrid.es/pages/presentation https://iguala.madrid.es/

The escalating incidents of wildfire pose a critical challenge in Canada, necessitating an understanding of forest structures for the effective implementation of fuel treatment strategies to mitigate wildfire risks. While previous research has explored forest structures and wildfire risk, a gap remains in translating these studies into operational solutions for foresters and land managers. This study addresses this critical gap by employing Light Detection and Ranging (LiDAR) technology for a preliminary assessment of wildfire risks in the Wildfire Urban Interface (WUI) of Whistler. Utilizing LiDAR’s ability to provide a detailed point cloud, the study introduces a simplified yet effective method for mapping crown fuel, ladder fuel and surface fuel. In assessing the crown fuel and ladder fuel, the project uses Canopy Height Models (CHM) and tree segmentation techniques to quantify forest structure. A notable result is the development of a tree volume index (TVI), which allows for a macro-scale evaluation of fuel volumes crucial for wildfire risk assessment. The analysis identifies a moderate positive correlation (0.55) between the LiDAR-derived TVI and data from the Canada Forest Satellite-Based Inventory 2020 (SBFI), demonstrating the approach’s validity and effectiveness in assessing wildfire risks. Furthermore, the study identifies potential surface fuel sources by analyzing pixel metrics, such as the percentage of vegetation returns between 1 and 2.5 m, thus offering insights into wildfire risk assessment. This pilot study exemplifies a novel approach to preliminary fuel mapping, facilitating the understanding of complex forest structures across extensive areas through LiDAR technology, and presents a practical methodology to improve wildfire risk management strategies.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.

The purpose of G-LiHT’s Hyperspectral Vegetative Indices data product (GLHYVI) is to provide vegetative, stress, and other index data in 44 science dataset layers. Included in the product are vegetative indices such as Normalized Difference Vegetation Index (NDVI), Triangular Vegetation Index (TVI), Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Difference Vegetation Index (DVI). Stress indices include, but are not limited to, Carter Stress, Gitelson and Merzlyac Stress, Maccioni Stress, and Vogelmann Stress.

GLHYVI data are processed as a raster data product (GeoTIFF) at 1 meter spatial resolution over locally defined areas. A browse image displaying NDVI is also included.