Analysis of Site-dependent Pinus halepensis Mill. Defoliation Caused by ‘Candidatus Phytoplasma pini’ through Shape Selection in Landsat Time Series

Mediterranean pine forest decline: A matter of root-associated microbiota and climate change

Forest ecosystems worldwide currently face worrying episodes of forest decline, which have boosted weakening and mortality of the trees. In the Mediterranean region, especially in the southeast Iberian Peninsula, Pinus sylvestris forests are severely affected by this phenomenon, and it has been commonly attributed to drought events. Remarkably, the role of root microbiota on pine decline has been overlooked and remains unclear. We therefore used metabarcoding to identify the belowground microbial communities of decline-affected and unaffected pine trees. Taxonomic composition of bacterial and fungal rhizosphere communities, and fungal populations dwelling in root endosphere showed different profiles depending on the health status of the trees. The root endosphere of asymptomatic trees was as strongly dominated by 'Candidatus Phytoplasma pini' as the root of decline-affected pines, accounting for >99 % of the total bacterial sequences in some samples. Notwithstanding, the titer of this phytopathogen was four-fold higher in symptomatic trees than in symptomless ones. Furthermore, the microbiota inhabiting the root endosphere of decline-affected trees assembled into a less complex and more modularized network. Thus, the observed changes in the microbial communities could be a cause or a consequence of forest decline phenomenon. Moreover, 'Ca. Phytoplasma pini' is positively correlated to Pinus sylvestris decline events, either as the primary cause of pine decline or as an opportunistic pathogen exacerbating the process once the tree has been weaken by other factors.

The Role of Remote Sensing for the Assessment and Monitoring of Forest Health: A Systematic Evidence Synthesis

Forests are increasingly subject to a number of disturbances that can adversely influence their health. Remote sensing offers an efficient alternative for assessing and monitoring forest health. A myriad of methods based upon remotely sensed data have been developed, tailored to the different definitions of forest health considered, and covering a broad range of spatial and temporal scales. The purpose of this review paper is to identify and analyse studies that addressed forest health issues applying remote sensing techniques, in addition to studying the methodological wealth present in these papers. For this matter, we applied the PRISMA protocol to seek and select studies of our interest and subsequently analyse the information contained within them. A final set of 107 journal papers published between 2015 and 2020 was selected for evaluation according to our filter criteria and 20 selected variables. Subsequently, we pair-wise exhaustively read the journal articles and extracted and analysed the information on the variables. We found that (1) the number of papers addressing this issue have consistently increased, (2) that most of the studies placed their study area in North America and Europe and (3) that satellite-borne multispectral sensors are the most commonly used technology, especially from Landsat mission. Finally, most of the studies focused on evaluating the impact of a specific stress or disturbance factor, whereas only a small number of studies approached forest health from an early warning perspective.

Discriminant Analysis of the Damage Degree Caused by Pine Shoot Beetle to Yunnan Pine Using UAV-Based Hyperspectral Images

Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R690 and R798 as variables had the highest accuracy (R2 = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.