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Three-dimensional (3D) data from airborne laser scanning (ALS) and, more recently, digital aerial photogrammetry (DAP) have been successfully used to model forest attributes. While multi-temporal, wall-to-wall ALS data is not usually available, aerial imagery is regularly acquired in many regions. Thus, the combination of ALS and DAP data provide a sufficient temporal resolution to properly monitor forests. However, field data is needed to fit new forest attribute models for each 3D data acquisition, which is not always affordable. In this study, we examined whether transferability of growing stock volume (GSV) models may provide an improvement in the efficiency of forest inventories updating. We used two available ALS datasets acquired with different characteristics in 2009 and 2010, respectively, generated two DAP point clouds from imagery collected in 2010 and 2017, and utilized field data from two ground surveys conducted in 2009 and 2016-2017. We first analyzed the stability of point cloud derived metrics. Then, Support Vector Regression models based on the most stable metrics were fitted to assess model transferability by applying them to other datasets in four different cases: (1) ALS-ALS, (2) DAP-DAP temporal, (3) ALS-DAP and (4) ALS-DAP temporal. Some metrics were found to be enough stable in each case, so they could be used interchangeably between datasets. The application of models to other datasets resulted in unbiased predictions with relative root mean square error differences ranging from -8.27% to 14.59%. Results demonstrated that 3D-based GSV models may be transferable between point clouds of the same type as well as point clouds acquired using different technologies such as ALS and DAP, suggesting that DAP data may be used as a cost-efficient source of information for updating ALS-assisted forest inventories.

[ES] El incremento de los efectos negativos del cambio climático y la aparición de especies invasoras en los bosques de todo el mundo requieren el desarrollo de métodos innovadores para monitorear y medir cuantitativamente el estado de salud de las masas arboladas. Estos efectos son especialmente notables en el área mediterránea, donde el decaimiento de las masas por sequías recurrentes ha incrementado los daños por plagas secundarias cuyas poblaciones, de otro modo, estarían en equilibrio. Las tecnologías de teledetección nos permiten afrontar trabajos en grandes superficies con una precisión razonable. En particular, se ha demostrado que nuevos índices espectrales obtenidos a partir de imágenes hiperespectrales y térmicas de alta resolución son buenos predictores para la detección temprana de cambios fisiológicos relacionados con enfermedades. En este estudio piloto desarrollado en una masa de Pinus halepensis en la Comunitat Valenciana, se lleva a cabo una simulación controlada de decaimiento por medio del anillado secuencial de árboles, haciendo un posterior seguimiento en campo del decaimiento que provoca. La captura de imágenes hiperespectrales de alta resolución ha permitido analizar la relación entre la información espectral en cada uno de los árboles anillados con su decoloración y estado de decaimiento observado. La metodología propuesta permite la detección de árboles afectados con tres meses de antelación a la aparición de síntomas visuales, clasificándolos con un nivel de acierto superior a 0,9 con los clasificadores Random Forest y Support Vector Machine. Los índices que generaron mejores resultados fueron PRI, VOG1, VOG2, GM1 y OSAVI. Este estudio piloto permite pensar que algunos de estos índices puedan ser utilizados en la detección temprana de marchitamientos generales de los pinares y, por tanto, tengan aplicación en la monitorización de las principales amenazas de los bosques europeos, las plagas de perforadores o los organismos de cuarentena como Bursaphelenchus xylophilus., [EN] The increasing negative effects of climate change and the emergence of invasive species in forests around the world require the development of innovative methods to monitor and quantitatively measure the health status of woodlands. These effects are especially notable in the Mediterranean area, where the decline of stands due to recurrent droughts has increased the damage caused by secondary pests whose populations would otherwise be in balance. Remote sensing technologies allow us to work on large surfaces with reasonable precision. In particular, new spectral indices obtained from high-resolution hyperspectral and thermal images have been shown to be good predictors for the early detection of physiological changes related to diseases. In this pilot study developed in a stand of Pinus halepensis in the Comunitat Valenciana, a controlled simulation of a decay is carried out by means of sequential girdling of trees, making a subsequent field monitoring of the caused decay. Through a hyperspectral camera, the spectral information of each of these trees is analyzed in relation to their discoloration and state of observed decay. The proposed methodology allows the detection of affected trees three months before the appearance of visual symptoms, obtaining a precision higher than 0.9 with Random Forest and Support Vector Machine classifiers. The vegetation indices with better results were PRI, VGO1, VGO2, GM1 and OSAVI. This pilot study allows us to think that some of these indices can be used in the early detection of general pine wilt and, therefore, have application in the monitoring of the main threats to European forests, borer pests or quarantine organisms such as Bursaphelenchus xylophilus.

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund’ Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.

REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.