Antonio García-Abril

Relationship between LiDAR-derived forest canopy height and Landsat images

The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral indices were obtained from three Landsat scenes. The mean, median, SD and coefficient of variation (CV) of LiDAR canopy height measurements were calculated in 30-m square blocks corresponding with Landsat Enhanced Thematic Mapper Plus (ETM+) pixels. Correlation and forward stepwise regression analysis was applied to these data sets. Mean and median LiDAR height versus normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR) and wetness Tasseled Cap showed the best correlation coefficients (R 2 ranging between −0.62 and −0.76). Nineteen regression models were obtained (R 2 = 0.65–0.70). These results show that LiDAR-derived canopy height may be associated with Landsat spectral indices. This approach is of interest in sustainable forest management, although further research is required to improve accuracy.

A review of research on Chinese Tuber species

Truffles are abundant in some regions of China. Nevertheless, it was not until the 1980s that Tuber species were discovered by Chinese mycologists. In recent years, international truffle markets have shown an increasing interest in the import of Chinese Tuber. These truffles serve as a complement to European truffles due to their lower prices and their greater availability in a deficit market. However, Chinese truffles have been the subject of fraudulent commercial practices, and these could have a negative effect on truffle culture. These concerns have been behind numerous recent studies designed to obtain detailed information about Chinese Tuber species. Unfortunately, many of these studies are not published in English, and are dispersed throughout the specific local or national bibliography and proceedings of specialized truffle conferences. In view of the need to expand current knowledge of Chinese Tuber species, we present a comprehensive summary of the taxonomy, ecology, mycorrhizae, genetics, biochemistry, and cultivation of Chinese Tuber species. We also provide a synthetic taxonomy and morphological characterization of 16 Chinese Tuber species in order to assist in their verification and monitoring.

Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization

Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR.

Stand structure, competition and growth of Scots pine (Pinus sylvestris L.) in a Mediterranean mountainous environment

The relationship between competition and tree growth was studied in four stands of Pinus sylvestris L. occurring in a continental Mediterranean mountain area (in the Guadarrama range, Spain), i.e., an uneven-aged stand, a stand with oak (Quercus pyrenaica Willd.) understorey, a plantation, and a mature even-aged stand. Competition was measured by a simple size-ratio distance-independent index and was negatively associated with tree diameter. This negative association was stronger in the uneven-aged, plantation and mature even-aged stands than in the stand with oak understorey. Competition was also negatively associated with current diameter increment. This relationship was moderately strong in the mature even-aged stand and weak in the uneven-aged stand and the plantation. In the uneven-aged and the mature even-aged stands, a weakly significant relationship was found between diameter growth and tree size, whereas these parameters were not associated in the stand with oak understorey. The competition index provided a better prediction of growth rate than the alternative use of diameter. Both diameter and basal area growth were greater in the uneven-aged than in the even-aged stands.RésuméLa relation entre compétition et croissance a été étudiée dans quatre peuplements de Pinus sylvestris L. que l’on rencontre dans la zone continentale des montagnes méditerranéennes (dans la région de Guadarrama en Espagne). Ont été pris en compte un peuplement inéquienne, un peuplement avec du chêne (Quercus pyrenaica Willd.) en sous—étage, une plantation, et un peuplement équienne âgé. La compétition a été mesurée par un index indépendant, simple rapport taille/distance, et était corrélée négativement avec le diamètre des arbres. Cette corrélation négative était plus forte dans le peuplement inéquienne, la plantation et le peuplement équienne âgé que dans le peuplement avec sous-étage de chêne. L’index de compétition était aussi corrélé négativement avec l’accroissement courant en diamètre. La relation était modérément forte dans le peuplement équienne âgé et faible dans le peuplement inéquienne et la plantation. Dans le peuplement inéquienne et le peuplement équienne âgé une relation faiblement significative a été trouvée entre croissance en diamètre et taille de l’arbre, alors que ces paramètres n’étaient pas corrélés dans le peuplement avec sous-étage de chêne. L’index de compétition fournit une meilleure prédiction du taux de croissance que le simple diamètre. La croissance en diamètre et en surface terrière était plus importante dans le peuplement inéquienne que dans le peuplement équienne âgé.

Edaphic controls on ecosystem‐level carbon allocation in two contrasting Amazon forests

Studies of carbon allocation in forests provide essential information for understanding spatial and temporal differences in carbon cycling that can inform models and predict possible responses to changes in climate. Amazon forests play a particularly significant role in the global carbon balance, but there are still large uncertainties regarding abiotic controls on the rates of net primary production (NPP) and the allocation of photosynthetic products to different ecosystem components. We evaluated three different aspects of stand-level carbon allocation (biomass, NPP, and its partitioning) in two amazon forests on different soils (nutrient-rich clay soils versus nutrient-poor sandy soils) but otherwise growing under similar conditions. We found differences in carbon allocation patterns between these two forests, showing that the forest on clay soil had a higher aboveground and total biomass as well as a higher aboveground NPP than the sandy forest. However, differences between the two forest types in terms of total NPP were smaller, as a consequence of different patterns in the carbon allocation of aboveground and belowground components. The proportional allocation of NPP to new foliage was relatively similar between them. Our results of aboveground biomass increments and fine-root production suggest a possible trade-off between carbon allocation to fine roots versus aboveground compartments, as opposed to the most commonly assumed trade-off between total aboveground and belowground production. Despite these differences among forests in terms of carbon allocation, the leaf area index showed only small differences, suggesting that this index is more indicative of total NPP than its aboveground or belowground components.

Soil fertility and GIS raster models for tropical agroforestry planning in economically depressed and contaminated Caribbean areas (coffee and kidney bean plantations)

In the jaragua-bahoruco-enriquillo biosphere reserve, located on the southern border between the Dominican Republic and Haiti, there are depressed rural areas with soils with high content in Cadmium and other heavy metals which originate naturally in the geological substrate. Data from 80 soils and an inventory of 76 plantations (coffee and kidney bean were used) to design a GIS (geographic information systems) tool which integrated statistical multivariate methods, soil parameters including heavy metal content into models of land planning, agricultural development, forests and protection of the health of the area’s inhabitants and the natural environment. This GIS tool is based on raster models of an open source, which use combination and reclassification operations based on the maps, geostatistical methods (Kriging), statistical analyses external to the GIS, and cartography of limiting and excluding particular factors for crops (including heavy metal soil content). The GIS tool developed discriminates extreme situations in sustainable agroforestry planning in contaminated rural areas of the Caribbean, Central America and other tropical regions.

Increasing the use of expert opinion in forest characterisation approaches based on LiDAR data

ContextForest structure characterisation approaches using LiDAR data and object-based image analysis remain scarce to forestry agencies as these automated procedures usually require the use of expensive software and highly skilled analysts. The integration of forest expert opinion into semi-automated approaches would simplify the access of forest managers to new technologies and would allow the incorporation of personal experience and the introduction of specific forest management criteria.AimThe aim of this study is to explore new alternatives to a previously published automated approach based on LiDAR data and object-based image analysis.MethodsWe compare four approaches, ranging from null to high incorporation of expert opinion and from fully automated to fully manual. These four approaches consist of three stages: (1) forest stand identification from LiDAR models, (2) forest stand classification into forest structure classes (manual and based on cluster analysis), and (3) validation.ResultsQuantitative attributes for validation (i.e. hypsographs and percentiles) provided slightly lower degree of separability for forest structure classes, in the mixed procedures with increasing incorporation of expert opinion than for the fully automated approach.ConclusionsThe new mixed approaches proposed are comparable to the automated procedures for the characterisation of forest structure in heterogeneous pine forest stands. They also offer additional advantages: (1) they make it possible to give a specific management focus and (2) they provide accessibility by the forest managers to the source of LiDAR information.

Problems of using rockroses in Tuber melanosporum culture: soil and truffle harvest associated with Cistus laurifolius

The Périgord black truffle (Tuber melanosporum Vittad.) can mycorrhize with rockroses (Cistus L.) in Mediterranean forests. These shrubs may be of interest for truffle cultivation as they produce early carpophores, collaborate in tree mycorrhization and in the survival of Tuber melanosporum mycelium. However, there are very few quantitative studies on truffle production associated with Cistus. For this reason, we have carried out a study on the natural production of Tuber melanosporum associated with Cistus laurifolius L. in central Spain. An Anova test confirmed the existence of significant differences in carpophore production in four habitats, with and without the presence of C. laurifolius. Twenty-one burns associated with C. laurifolius showed less significant production than the rest. We also confirmed that in 122 burns, Tuber melanosporum was always unequivocally associated with the root base of Quercus L. or Corylus L. trees. Similarly, five truffle-collectors confirmed that they had never found a single burn with carpophore production associated exclusively with rockroses. It has also been statistically verified that many Cistus die in Tuber melanosporum burns, whereas in old burns with minimum carpophore production, a new colonization of young Cistus has been observed. This study also confirms that C. laurifolius mycorrhizes easily with Tuber melanosporum. We propose that Cistus laurifolius may act as transmitters of Tuber melanosporum, although they do not induce fruiting. Consequently, the commercial cultivation of Cistus laurifolius mycorrhized with Tuber melanosporum for production is not recommended in truffle culture. The results reported substantiate the negative observations in cultivations of T. melanosporum with other Cistus species.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACT In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

Planning and selection of green roofs in large urban areas. Application to Madrid metropolitan area

Abstract Due to the numerous environmental problems facing todays society, and especially urban areas, green roofs are presented as an adequate technique to fight the consequences of pollution, traffic and lack of green areas. These green structures help to reduce the effects of Urban Heat Island, to decrease noise and atmospheric pollution, to protect homes from isolation and cold; they also capture rainwater and improve biodiversity. A new methodology is presented to select the best location of green roofs in large cities. In the first phase, this methodology helps to determine the most suitable neighborhoods, analyzing four main variables of interest in urban environs: pollution, traffic, green areas and population. In order to benefit a greater number of inhabitants, the neighborhoods with the worst air quality, more traffic, less green areas and higher population density, are selected. In the second phase, we used LIDAR technology to identify available roofs for the installation of the green roofs according to the height and roof typology of the buildings. To select the optimal roofs, connectivity analysis techniques were used. The results show that the most conflictive neighborhoods from the environmental point of view are those located in the city center, so they result the ideal places for the location of green roofs. In general, all neighborhoods except one presented high connectivity values. This methodology helps to improve the connectivity of the green spaces of Madrid, favoring the dispersion of plant and animal species, air quality and promoting sustainable and quality urban development.