Estimation of grapevine predawn leaf water potential based on hyperspectral reflectance data in Douro wine region

Hyperspectral data collected through a handheld spectroradiometer (400-1010 nm) were tested for assessing the grapevine predawn leaf water potential (ѱpd) measured by a Scholander chamber in two test sites of Douro wine region. The study was implemented in 2017, being a year with very hot and dry summer, conditions prone to severe water shortage. Three grapevine cultivars, 'Touriga Nacional', 'Touriga Franca' and 'Tinta Barroca' were sampled both in rainfed and irrigated vineyards, with a total of 325 plants assessed in four post-flowering dates. A large set of vegetation indices computed with the hyperspectral data and optimized for the ѱpd values, as well as structural variables, were used as predictors in the model. From a total of 631 possible predictors, four variables were selected based on a stepwise forward procedure and the Wald statistics: irrigation treatment, test site, Anthocyanin Reflectance Index Optimized (ARIopt_656,647) and Normalized Ratio Index (NRI711,700). An ordinal logistic regression model was calibrated using 70 % of the dataset randomly selected and the 30 of the remaining observations where used in model validation. The overall model accuracy obtained with the validation dataset was 73.2 %, with the class of ѱpd corresponding to the high-water deficit presenting a positive prediction value of 79.3 %. The accuracy and operability of this predictive model indicates good perspectives for its use in the monitoring of grapevine water status, and to support the irrigation tasks

A review of strategies, methods and technologies to reduce non-beneficial consumptive water use on farms considering the FAO56 methods

In the past few decades, research has developed a multitude of strategies, methods and technologies to reduce consumptive water use on farms for adaptation to the increasing incidence of water scarcity, agricultural droughts and multi-sectoral competition for water. The adoption of these water-saving practices implies accurate quantification of crop water requirements with the FAO56 crop coefficient approach, under diverse water availability and management practices. This paper critically reviews notions and means for maintaining high levels of water consumed through transpiration, land and water productivity, and for minimizing non-beneficial water consumption at farm level. Literature published on sound and quantified experimentation was used to evaluate water-saving practices related to irrigation methods, irrigation management and scheduling, crop management, remote sensing, plant conditioners, mulching, soil management and micro-climate regulation. Summary tables were developed on the benefits of these practices, their effects on non-beneficial water consumption, crop yields and crop water productivity, and the directions for adjustment of FAO56 crop coefficients when they are adopted. The main message is that on-farm application of these practices can result in water savings to a limited extent (usually<20%) compared to sound conventional practices, however this may translate into large volumes of water at catchment scale. The need to streamline data collection internationally was identified due to the insufficient number of sound field experiments and modelling work on the FAO56 crop water requirements that would allow an improved use of crop coefficients for different field conditions and practices. Optimization is required for the application of some practices that involve a large number of possible combinations (e.g. wetted area in micro-irrigation, row spacing and orientation, plant density, different types of mulching, in-field water harvesting) and for strategies such as deficit irrigation that aim at balancing water productivity, the economics of production, infrastructural and irrigation system requirements. Further research is required on promising technologies such as plant and soil conditioners, and remote sensing applicationsinfo:eu-repo/semantics/publishedVersio

Visualisation of trust and quality information for geospatial dataset selection and use:Drawing trust presentation comparisons with B2C e-Commerce

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. Part of the problem arises from the inconsistent and patchy nature of data quality information, which makes intercomparison very difficult. Over recent years, the production and availability of geospatial data has significantly increased, facilitated by the recent explosion of Web-based catalogues, portals, standards and services, and by initiatives such as INSPIRE and GEOSS. Despite this significant growth in availability of geospatial data and the fact that geospatial datasets can, in many respects, be considered commercial products that are available for purchase online, consumer trust has to date received relatively little attention in the GIS domain. In this paper, we discuss how concepts of trust, trust models, and trust indicators (largely derived from B2C e-Commerce) apply to the GIS domain and to geospatial data selection and use. Our research aim is to support data users in more efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for purpose. To achieve this, we propose a GEO label – a decision support mechanism that visually summarises availability of key geospatial data informational aspects. We also present a Web service that was developed to support generation of dynamic GEO label representations for datasets by combining producer metadata (from standard catalogues or other published locations) with structured user feedback

new technologies for the sustainable management and planning of rural land and environment

New technologies could be adequately introduced for an improved analysis aimed to the sustainable management and planning of the rural land, as well as its environment and landscape. Nowadays, this analysis is easier and more complete through the use of powerful and reliable tools. Several changes can be considered to be as models of territorial development, useful for an appropriate planning of the human interventions in a rural area. Remote sensing techniques could be employed for the monitoring of agricultural land variation, while Geographical Information Systems are excellent tools for landscape modeling and three-dimensional analysis. In this chapter, land-use changes in a rural area located in southern Italy were analyzed by comparing some historical cartographic supports with modern maps, in order to evaluate the morphological and vegetation variations of the agroforestry land during time. Moreover, a landscape analysis was conducted through the implementation of digital terrain models, which were enriched by draping land cover pictures over them. These elaborations finally enabled an evaluation in a scenic way of the aesthetic quality of the agroforestry landscape, allowing a virtual jump back to time periods when digital aerial photography was not yet even possible. This multi-temporal analysis with the support of GIS techniques revealed to have a great potential for assessing and managing landscape diversity and changes of vegetation, as well as for planning sound interventions over the landscape structures

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Will climate change drive alien invasive plants into areas of high protection value? An improved model-based regional assessment to prioritise the management of invasions.

Species distribution models (SDMs) studies suggest that, without control measures, the distribution of many alien invasive plant species (AIS) will increase under climate and land-use changes. Due to limited resources and large areas colonised by invaders, management and monitoring resources must be prioritised. Choices depend on the conservation value of the invaded areas and can be guided by SDM predictions. Here, we use a hierarchical SDM framework, complemented by connectivity analysis of AIS distributions, to evaluate current and future conflicts between AIS and high conservation value areas. We illustrate the framework with three Australian wattle (Acacia) species and patterns of conservation value in Northern Portugal. Results show that protected areas will likely suffer higher pressure from all three Acacia species under future climatic conditions. Due to this higher predicted conflict in protected areas, management might be prioritised for Acacia dealbata and Acacia melanoxylon. Connectivity of AIS suitable areas inside protected areas is currently lower than across the full study area, but this would change under future environmental conditions. Coupled SDM and connectivity analysis can support resource prioritisation for anticipation and monitoring of AIS impacts. However, further tests of this framework over a wide range of regions and organisms are still required before wide application