High‑resolution elevation data underpins almost every spatial analysis we do in GIS—especially in forests where vertical structure defines habitat, biomass, wind exposure, fire behavior, hydrology, and the microclimates that sustain rare species. In rugged or densely vegetated environments, a coarse or biased elevation model propagates error everywhere: orthorectification drifts, hillshades mislead, slope/aspect misclassify, and canopy metrics saturate. The result is decisions made on blurred terrain that hides the very patterns we seek to manage. Precision elevation—derived from airborne LiDAR (Light Detection and Ranging)—solves this by separating the ground from the vegetation and delivering both a bare‑earth Digital Terrain Model (DTM) and a Digital Surface Model (DSM). Subtracting DTM from DSM gives a Canopy Height Model (DHM) that captures the true vertical architecture of the forest at sub‑meter resolution.
Category Archives: landuse
Agricultura de Precisión. Uso del Satélite para la toma de decisiones en el campo
Quieres conocer cuál es el momento óptimo para plantar? Para fumigar? Para recolectar?. Sabías que dos de cada tres agricultores no cosechan en la fase de madurez adecuada?. Aquí abajo te describo un método completamente automatizado mediante el uso combinado de varios índices de vegetación como NDVI, NDWI, SAVI y EVI que podemos extraer del Satétile SENTINEL-2 en la plataforma COPERNICUS de la UE para conocer exactamente y anticipar las mejores decisiones de intervención sobre tus tierras.
Sentinel-1 SAR: Un aliado indispensable para el análisis y seguimiento de inundaciones – Derna, Libia (2023)
La gestión y monitorización de fenómenos hidrológicos extremos, como inundaciones repentinas o fallos estructurales en presas, representan un desafío crítico para los especialistas en geomática, hidrología y planificación territorial. En este contexto, la tecnología radar de apertura sintética (SAR) a bordo del satélite Sentinel-1 de la Agencia Espacial Europea (ESA) ofrece una capacidad sin precedentes para capturar información precisa y fiable sobre la dinámica superficial, independientemente de las condiciones atmosféricas y lumínicas.
Urban delineation methods beyond administrative boundaries
As a geographer working with Geographic Information Systems (GIS), I am particularly interested in exploring urban delineation methods that move beyond the constraints of administrative boundaries. Instead of relying on official municipal limits—which can often be outdated or misaligned with functional realities on the ground—I focus on delineating urban areas based on physical indicators such as built-up surface, population density, and spatial continuity. This approach allows for a more accurate and dynamic understanding of urban space.
Urban Heat Islands, Trees, and Climate Justice in the Anthropocene: A Remote Sensing-Based Reflection
In recent years, the need to understand the urban environment has grown more urgent than ever. Climate change is not an abstract future scenario; it is already here, reshaping our cities day by day. Among the many phenomena that demand our attention, the Urban Heat Island (UHI) effect stands out—not only for its environmental and public health impacts but also for its socio-political implications. Through satellite imagery and remote sensing, we can now visualize and quantify these dynamics with increasing precision. This post reflects on such an analysis I conducted using LANDSAT 8 imagery (Scene ID: LC08_L2SP_201032_20250328_20250401_02_T1, Date Acquired: 2025/03/28), and discusses the findings in the broader context of urban planning, climate justice, and the urgent need to protect urban vegetation.
Wildfires in Russia through SENTINEL 2
In recent days, the Sakha region in Russia has experienced significant wildfire activity, More than 100 wildfires spanning more than 300,000 hectares are currently active. driven by a combination of extreme weather conditions and environmental factors. The fires, which have spread across vast areas of forest and tundra, have been exacerbated by unusually high temperatures and prolonged periods of drought. These conditions have led to exceptionally dry vegetation, creating an ideal fuel for the fires.
Water Quality Viewer (UWQV) for Sentinel -2
The Ulyssys Water Quality Viewer harnesses the power of Sentinel-2 satellite data to provide comprehensive and accurate water quality assessments. Its intuitive legend and visualization capabilities significantly enhance the user’s ability to interpret and act on environmental data, contributing to better water management and conservation efforts worldwide, particularly in ecologically and economically significant areas like La Mata and Torrevieja salt lagoons.
Sequía en Cataluña – Timelapse 2017-2024
Las restricciones más duras de emergencia por sequía se extenderán a más de seis millones de personas de los 202 municipios de Barcelona y algunas poblaciones de Girona. Así lo ha anunciado este jueves la Generalitat, que suma estas localidades a las 37 (125.000 habitantes) de las comarcas de Alt Empordà, Baix Camp y Baix Llobregat, que ya estaban en la fase más severa desde agosto. Esto significa que casi el 80% de la población catalana vive en una zona en emergencia por la crisis hídrica.
The Remarkable Visibility of Almeria’s Greenhouses from Space
In the sphere of Earth observation and remote sensing, the Almeria greenhouses stand out as an astonishing spectacle visible even from the edge of space, defined by the Kármán line at approximately 100 kilometers in altitude. This unique visibility is not only a testament to the vast scale of human agricultural activity but also a captivating example of the power of satellite-based remote sensing technologies.
Measuring snow coverage using EOB – Earth Observation – Sentinel HUB
Playing with Sentinel 2 images I realized that the amount of snow this year has been very low compared to last winter. Here are a couple of images from January 2022 and 2023 of the Canfranc – Paticosa – Jaca area. One of the most interesting things about this EO browser is that in addition to a standard 2D visualisation, we can visualise in 3D. Here is an video example. Another interesting thing is to be able to quantify the snowfall. There is a snow classifier based on NSDI (Normalized Difference Snow Index, The Normalized Difference Snow Index (NDSI) snow cover is an index that is related to the presence of snow in a pixel and is a more accurate description of snow detection as compared to Fractional Snow Cover (FSC).
Geovisualization.net 