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The study introduces two quantitative indices, RCI 1 (ECRD/ECTD) and RCI 2 (MRD/MTD), to assess post-fire resprouting capacity in the arid Chaco of Argentina. Testing 1,046 plants across 20 species, the researchers found that both indices correlate strongly with resprouting vigour and provide more detail than survival-only assessments. Results indicate that resprouting capacity decreases as the estimated composite diameter of trunks (ECTD) increases, and that growth form influences RCI 2 values, with tree/shrubs and trees generally showing higher RCI 1 values than shrubs.

CoastalDEM v2.1 is a high-resolution global digital elevation model (DEM) designed to improve the accuracy of coastal flood and sea level rise assessments. By employing a convolutional neural network (CNN) trained on NASA's ICESat-2 satellite lidar data and using NASADEM as a base, it significantly reduces the positive vertical bias common in satellite radar DEMs, particularly in densely populated urban areas where buildings often distort elevation readings. Validation against ICESat-2 and airborne lidar data from the US and Australia indicates that CoastalDEM v2.1 outperforms other leading global models like TanDEM-X and MERIT, especially in the critical elevation band below 5 meters.

The document describes the technical framework of WeatherPower v3.4, which uses a 0.05° grid of wind speed and solar irradiance data from MESO to estimate electricity generation. It details the integration of utility-scale and residential solar data (from EIA860, Tracking the Sun, and Project Sunroof) and wind turbine data (from USWTDB). The tool calculates primary metrics like electricity generated and indices (SPI and WPI), as well as comparative metrics such as CO2 emissions avoided and equivalent homes powered. A validation exercise using ERCOT data indicates that wind generation estimates are reasonably accurate, though on average about 10% higher than reported data.

The report describes the technical framework of WeatherPower v3.0, which uses a 0.05° grid of weather data (wind speed and solar irradiance) and installed capacity data to estimate electricity generation for 'Yesterday', 'Today', and 'Tomorrow'. It outlines the data sources for utility and residential solar and wind capacities, the mathematical power curves used to calculate generation, and the reporting metrics used to contextualize the results, such as CO2 emissions avoided and equivalent homes powered. The document also includes a validation study comparing its wind generation estimates against actual data from the Electric Reliability Council of Texas (ERCOT).

The World Resources Institute developed a three-step method to estimate thermal power plant water demand: identifying cooling and fuel types via satellite images, assigning water-use factors from empirical data, and calculating total use based on net generation. Tested on 200 U.S. plants, the method achieved 90% precision in cooling and 89% in fuel identification, though water demand estimation precision was lower at 69%. The authors note limitations regarding image resolution, the inability to distinguish specific generation technologies within a fuel type, and the reliance on U.S.-based water-use factors which may vary globally.

The Land Disturbance Alert Classification System (LDACS) is a global data set that enhances NASA's 30-meter resolution DIST-ALERT system by integrating fire, water, cropland, and natural ecosystem data. It classifies vegetation disturbances into four categories: potential land conversion, fire-related, water-related, and cropland dynamics. This system specifically addresses monitoring gaps for non-forest ecosystems like grasslands, savannas, shrublands, and wetlands. Global 2023 data indicates that cropland dynamics represent one-third of classified disturbances, while potential land conversion accounts for 4%. Initial validation in Brazil's Cerrado biome demonstrated an 87% accuracy rate for classified alerts.