The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

The adoption of novel methods in forest management planning requires the incorporation of precise forest and tree data to improve scheduling and meet multi-objective criteria principles. This study evaluates advanced methods for mapping tree structural attributes to create detailed baselines for forest carbon biomass, a key indicator in environmental policies. We specifically investigate the combined use of mobile sensors (hand-held laser scanning, HLS) and airborne (unmanned laser scanning, ULS), to estimate biomass and carbon stocks in a Mediterranean mixed forest. The novelty of our study lies in the synergistic application of HLS and ULS technologies and the evaluation of different ULS flight altitudes (50, 70, 90, 110 m) and scanning modes to optimize data accuracy and coverage. The main questions addressed are: (1) How do different flight altitudes and scanning modes of ULS affect the accuracy of biomass and carbon stock estimations? (2) What is the impact of merging HLS and ULS data on the precision of tree structural attribute measurements? (3) Can the combined use of HLS and ULS overcome the limitations of individual systems, particularly in complex forest structures? Our case study is conducted in a 1-ha plot in a complex, terraced forest region in Central Portugal, chosen for its high species diversity and structural complexity, which present significant challenges for remote sensing technologies. This site represents a typical Mediterranean mixed forest, allowing us to test methods in conditions that are both typical and challenging for forest monitoring. The distribution of HLS estimates was aligned with reference DBH measurement, though systematically lower (~ 2–3 cm bias). The impact of these measurement errors on total biomass estimation was around 13%. In contrast, major discrepancies were observed in tree height estimations when comparing HLS, ULS, fused ULS-HLS point clouds, with field reference data. ULS operated effectively at heights up to 110 m, increasing coverage without compromising result quality. However, merging point cloud datasets did not significantly improve the accuracy of tree height estimates due to the complexity and high species mingling of the forest stand. We recommend caution in using field measurements for validating tree height estimates with laser sensors under these conditions.

Using a metaphor based on a historical debate between socialist and free-market economists, Salliou and Stritih (Environ. Res. Lett. 18 151001) advocate for decentralizing environmental management to harness emergent complexity and promote ecosystem health. Concerningly, however, their account seems to leave little room for top-down processes like government-led sustainability programs or centrally-planned conservation initiatives, the cornerstone of the post-2020 biodiversity framework. While we appreciate their call for humbleness, we offer a few words in defense of planning. Drawing on evidence from ecology, economics, and systems theory, we argue that (1) more complexity is not always better; (2) even if it were, mimicking minimally-regulated markets is probably not the best way to get it; and (3) sophisticated decision support tools can support humble planning under uncertainty. We sketch a re-interpretation of the socialist calculation debate that highlights the role of synthesis and theoretical pluralism. Rather than abandoning big-picture thinking, scientists must continue the difficult work of strengthening connections between and across multiple social, ecological, and policy scales.

El establecimiento de especies arbóreas de rápido crecimiento (Eucalyptus spp.) es crucial para suministrar productos forestales a las economías en desarrollo. Sin embargo, la creciente frecuencia, gravedad y duración de las sequías amenazan la viabilidad de estos importantes ecosistemas. En Mato Grosso (Brasil), donde el estrés hídrico es el principal factor limitante del eucalipto, se registró en 2019 el segundo evento de sequía más grande de toda la serie histórica. Los datos del inventario forestal que comprenden miles de mediciones de árboles tomadas en 2019, 2020 y 2021 se han modelado utilizando modelos de efectos mixtos para identificar los factores más significativos que influyen en la mortalidad y la dinámica de crecimiento de los cuatro genotipos de eucalipto diferentes. Se utilizó un vehículo aéreo no tripulado (UAV) para obtener un mosaico de imágenes en el espectro visible -rojo, verde y azul (RGB)- a muy alta resolución (VHR), además de modelos digitales de superficie (DSM) y cálculos del índice de vegetación (VI), que se utilizaron en la clasificación de la mortalidad mediante una segmentación centrada en el objeto. Las tasas de crecimiento y mortalidad se vieron significativamente afectadas durante la sequía; el efecto de la sequía de 2019 fue más pronunciado en rodales con alta densidad de árboles. La selección del material genético y la densidad de plantación pueden utilizarse como factores silvícolas para gestionar más eficientemente las plantaciones forestales frente a los efectos del cambio climático, incluyendo los eventos de estrés hídrico extremo. Implicaciones: Este estudio ilustra la necesidad de ajustar las directrices silvícolas para reducir el impacto de la sequía en las plantaciones de Eucalyptus y cómo las tecnologías de teledetección, las mejoras genéticas y la investigación operativa aplicada pueden integrarse para mejorar la eficiencia y la resiliencia de las plantaciones de Eucalyptus y explorar los límites óptimos de productividad bajo el cambio global.