Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 “forest„ points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km2 in Tuscany, central Italy

A collection of functions to determine annual tree carbon increment via stem-analysis

Stem analysis process is commonly employed in a wide range of applications of forest importance. We developed a program to compute stem increments in terms of volume, biomass and carbon storage. The stem-analysis process involves the felling of the tree, the extraction of a number of cross-sections from the stem and the measuring of ring-width series on each section. The synchronization of ring-width series provides a number of tree- and stand-related measures including stem growth pattern, diameter at breast height and tree height growth trend, site-index assessment, timber-quality assessment. Relevant features of the program include: (i) tables and charts can be handled in the “R” environment or exported to any spreadsheet program; (ii) algorithms are independent from the ring width measuring device; (iii) the computation of stem volume, yearly- and mean volume increment is provided as well as the lateral surface area, stem carbon pool, its yearly- and mean increment and associated measurement errors. Forest biomass destructive surveys can usefully apply stem-analysis techniques in order to assess forest past carbon increment trend and set up the basis for non-destructive future carbon surveys.Une compilation de fonctions pour la détermination de l’incrément annuel de carbone à partir de la technique d’analyse de tige. La technique d’analyse de tige est employée communément pour de larges gammes d’applications d’intérêt forestier. Nous avons développé un programme pour calculer l’accroissement de la tige en terme de volume, biomasse et stockage du carbone. La technique d’analyse de tige implique l’abattage de l’arbre, la préparation de nombreuses coupes transversales et la mesure d’une série de largeur de cernes sur chaque section de tige. La synchronisation d’une série de largeur de cernes donne un nombre de mesures corrélées de l’arbre et de la plantation incluant le patron de croissance de la tige, le diamètre à 1,30 m de hauteur et la tendance de la croissance de l’arbre en hauteur, l’estimation de l’indice de productivité, l’estimation de la qualité du bois. Les caractéristiques importantes du programme incluent : (i) des tables de sortie et des graphiques qui peuvent être gérés dans l’environnement « R » ou exportés dans une feuille de calcul ; (ii) les algorithmes sont indépendant de l’appareil mesurant la largeur des cernes ; (iii) il fournit le calcul annuel du volume de la tige, du volume moyen produit, et de l’aire de la surface latérale, aussi bien que la biomasse du tige, les stocks de carbone, les incrément annuels, et leurs erreurs associés. Les études de biomasse forestière destructives peuvent appliquer utilement les techniques d’analyse de tige pour estimer rétrospectivement la tendance des incréments de carbone des forêts et la mise au point des bases de futures études non destructives du carbone

Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status

Recent decades have seen a progressive degradation of soils owing to an intensification of farming practices (weeding and high trafficking), increasing use of pesticides and fertilizers, mainly nitrogen, resulting in a steady decline in soil organic matter, a key component to maintain soil fertility. The work has coupled the normalized difference vegetation index (NDVI) of wheat cultivation in Central Italy to soil properties where the wheat was grown to identify the properties linked to within-field variability in productivity. NDVI was assessed through Copernicus Sentinel-2 (S-2) data during the wheat anthesis phase. The main outcome showed a significant correlation of NDVI variability to soil colloidal status and to the relative quantity in the exchange complex of the Ca2+ ions. No relationship emerged between NDVI and soil macronutrients (nitrogen, phosphorus, and potassium) concentration. The work suggested that such elements (nitrogen, especially) should not be provided solely considering the vegetation index spatial variations. Rational and sustainable management of soil fertility requires the integration of the NDVI data with the whole complex of soil physical/chemical status. In this way, the identification of the real key factors of fertility will avoid the negative impact of overfertilization. As an example, a fertilization plan was simulated for the sunflower–wheat sequence. The results showed that in the study area additional supplies of N and K would be unnecessary

Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status

Recent decades have seen a progressive degradation of soils owing to an intensification of farming practices (weeding and high trafficking), increasing use of pesticides and fertilizers, mainly nitrogen, resulting in a steady decline in soil organic matter, a key component to maintain soil fertility. The work has coupled the normalized difference vegetation index (NDVI) of wheat cultivation in Central Italy to soil properties where the wheat was grown to identify the properties linked to within-field variability in productivity. NDVI was assessed through Copernicus Sentinel-2 (S-2) data during the wheat anthesis phase. The main outcome showed a significant correlation of NDVI variability to soil colloidal status and to the relative quantity in the exchange complex of the Ca2+ ions. No relationship emerged between NDVI and soil macronutrients (nitrogen, phosphorus, and potassium) concentration. The work suggested that such elements (nitrogen, especially) should not be provided solely considering the vegetation index spatial variations. Rational and sustainable management of soil fertility requires the integration of the NDVI data with the whole complex of soil physical/chemical status. In this way, the identification of the real key factors of fertility will avoid the negative impact of overfertilization. As an example, a fertilization plan was simulated for the sunflower–wheat sequence. The results showed that in the study area additional supplies of N and K would be unnecessary

Efficient Estimation of Biomass from Residual Agroforestry

Cost-effective sampling methods for the estimation of variables of interest that are time-consuming are a major concern. Ranked set sampling (RSS) is a sampling method that assumes that a set of sampling units drawn from the population can be ranked by other means without the actual measurement of the variable of interest. We used data on vegetation dynamics from satellite remote sensing as a means in which to rapidly rank sampling units across various land covers and to estimate their residual agroforestry biomass contribution for a small cogeneration facility located in the center of a study area in central Italy. A remote sensing map used as an auxiliary variable in RSS enabled us to cut down the photo-interpretation of the residual biomass present in sampling units from 745 to 139, increase the relative precision of the estimate over common simple random sampling, and avoid individual subjective bias being introduced. The photo-interpretation of the sampling units resulted in a 1.12 Mg ha−1 year−1 mean annual density of residual biomass supply, although unevenly distributed among land cover classes; this led to an estimate of a yearly supply of 132 Gg over the whole 2276 km2 wide study area. Further applications of this study might include the spatial quantification of biomass supply-related ecosystem services

Biometric assessment of aboveground carbon pools and fluxes in three European forests by Randomized Branch Sampling

Accurate measurements of carbon pools and fluxes and of the related uncertainties are required to support the estimation of regional and continental carbon budgets. For this purpose a rigorous statistical method, known as Randomized Branching Sampling (RBS), has been applied for the direct assessment of carbon pools, fluxes (Net Primary Productivity) and plant surface areas in three forests. RBS is an unequal probability selection scheme that is design unbiased and efficient. Through its theory and design, RBS provides an unbiased estimate of uncertainties both at single tree and ecosystem scales. RBS designed samplings proved to be less time-consuming than traditional ones by lowering the number of sample branches needed to achieve the target precision levels and by getting rid of fresh weight measurements in the field. RBS estimates of C pools were compared and discussed to traditional estimates achieved by allometric functions fitted using the power equation Y ¼ b Xa revealing good agreement; differences between the RBS and allometric approaches were higher in older or more structured forests. Optimal scaling exponents for foliage, branch and stem components, for pool, flux and surface parameters in European beech, Scots pine and Norway spruce stands were estimated by analysis of the precision of target aggregate estimators. In all stands, the scaling exponent for the stand-scale estimates proved to be lower than the scaling exponent estimated from the allometric fitting and than analytically derived exponents. This discrepancy could lead, should the latter scaling exponents be used, to over-estimate C pools in forests.JRC.H.7-Climate Risk Managemen

Text mining in remotely sensed phenology studies. A review on research development, main topics, and emerging issues

As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly di°cult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979-2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network

Vulnerability of Wheat Crops to Flooding Outweighs Benefits from Precision Farming and Agroecology Practices: A Case Study in Central Italy

This study aimed at testing whether the integration of precision farming (PF) and agroecological practices could influence wheat yield in the short term on soils exposed to varying degrees of risk from flooding. The study embraced two years (2018–2019 and 2020–2021) of wheat cultivation in Central Italy. A two-way factorial grid with agronomic practice (two levels: agroecology vs. conventional on-farm management) and soil vulnerability to flooding (three levels: extreme, mild, non-vulnerable) as factors was set up. The agroecology level included a number of agroecology practices (rotation, use of nitrogen-fixing crops, mulching, and reduction in chemical fertilization). Crop phenology and photosynthetic activity of wheat was monitored by remotely-sensed Normalized Difference Vegetation Index (NDVI). Grain yield was estimated at twenty sampling points at the end of year 2. A flooding event occurred during year 2, which led to significantly lower photosynthetic activity compared to year 1 in extremely vulnerable plots regardless of agronomic practices. Grain yield measurements confirmed that vulnerability was the sole factor significantly affecting yield. The study concludes that food security on vulnerable land can be guaranteed only when precision farming and agroecological practices are coupled with water management techniques that strengthen the resilience of vulnerable soils to floods