Chapitre 6 - Techniques géospatiales et de télédétection pour le suivi de l’état et des dynamiques des terres agricoles périurbaines

Ce chapitre est le résultat d’un travail d’équipe incluant tous les auteurs. Il est né des suites d’un atelier franco-australien qui s’est tenu à Sydney en octobre 2013. Introduction Depuis toujours, les sociétés humaines modifient l’environnement dans lequel elles évoluent. Des villes de plus en plus étendues, à l’échelle mondiale, accueillent actuellement plus de 50 % de la population mondiale, et ce pourcentage continue..

Les terres agricoles face à l’urbanisation

La perte de terres agricoles liées à l’urbanisation constitue l’une des facettes de la consommation des terres. Commencé dans les années 1970, ce phénomène — essentiellement dû à l’étalement urbain — prend des proportions jusque-là inégalées. Les conséquences de ces processus d’artificialisation sont multiples et portent à la fois sur la production et sur la sécurité alimentaire ainsi que sur la perte de biodiversité. Ces processus interrogent aussi les formes de solidarité territoriale entre les villes et les espaces péri-urbains et ruraux. Issu d’une collaboration scientifique lancée au début des années 2010 entre l’Université de technologie de Sydney (University of Technology Sydney, UTS) et l’Institut national de recherche en sciences et technologies pour l’environnement et l’agriculture (Irstea), cet ouvrage aborde des points clés de la problématique de la consommation des terres en se focalisant sur les terres agricoles en France et en Australie. Plutôt que d’offrir une analyse comparative approfondie de la planification des terres agricoles périurbaines entre les deux pays, il propose une exploration des « boîtes à outils » de l’ingénierie territoriale développées et mobilisées pour faire face à l’enjeu de la perte de terres agricoles liée à l’urbanisation. Il offre également un « arrêt sur image » dans un panorama de champs de recherche en pleine évolution, autant du point de vue théorique que méthodologique

Analysis of the interaction of nitrogen application and stripe rust infection in wheat using 'in situ' proximal and remote sensing techniques

The project dealt with modelling the interaction of nitrogen nutrition and stripe rust (yellow rust) incidence in wheat using spectral reflectance characteristics at different spatial scales as observed by ground based sensors, airborne and satellite data. Experimental plots, with different levels of N application, variety and seed treatment for stripe rust disease, were set up in crop seasons 2006 and 2007. Temporal ground based multispectral data were collected using Crop Circle ACS-210 (Holland Scientific Inc., NE, USA) and the GreenSeeker model 505 (Ntech Industries Inc., CA, USA). Hyperspectral data were collected using USB 2000 (Ocean Optics, FL, USA). This ground based data were analysed in relation to airborne data collected using an airborne multispectral sensor, UNEBiRD (UNE, Armidale). Multispectral and hyperspectral vegetation indices (VIs) derived from the two years of data were analysed in relation to the occurrence of N deficiency, disease incidence, LAI, chlorophyll content, biomass and yield in wheat. Further, applicability of these VI based models at a higher spatial scale was examined employing multispectral (Landsat 5TM ) and hyperspectral (EO1 Hyperion) satellite data acquired over commercial wheat paddocks in northern NSW, Australia. Analysis of agronomic data confirmed the expected outcomes of a positive correlation between N application and yield up to a certain rate of N application, with further addition of N causing yield to plateau or subsequently decrease. This study also confirmed that there was significant positive correlations between N application and stripe rust severity

Effect of stripe rust on the yield response of wheat to nitrogen

AbstractNitrogen (N) is the most important fertiliser element determining the productivity of wheat. N nutrition is known to affect the level of stripe rust infection, with higher N associated with increased disease severity. Stripe rust, caused by Puccinia striiformis f. sp. tritici, is a major yield-limiting disease of wheat in Australia. This paper describes experiments designed to investigate the agronomic response to the interaction of various levels of N application and stripe rust severity in wheat varieties differing in response. Experimental plots were established in crop seasons 2006 and 2007 on the Liverpool Plains of northern NSW, Australia. Yield, biomass, grain protein content (GPC) and harvest index (HI) data were recorded. Increased rates of N increased the severity of stripe rust during grain filling. N application also increased yield and GPC in all varieties in both years. Stripe rust reduced the yield of the rust-susceptible wheat varieties, and GPC and proportion of added N recovered in the grain were also reduced in one year but not the other. It was evident from our experiment that stripe rust caused yield loss accompanied by either no change or reduction in GPC, indicating that the total amount of N entering the grain was reduced by stripe rust. The effects of stripe rust on N yield are most likely associated with reduced uptake of N during grain filling

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity

Terrestrial total water storage dynamics of Australia's recent dry and wet events

Australia recently experienced a long-term continental drought ('big dry', 2001-2009) followed by an anomalous wet two-year period ('big wet', 2010-2011). Despite the significance of the two extreme events, continental-wide information regarding the effects of the high and low precipitation conditions on the hydrological components, stress and recovery is not available. In this paper, we use terrestrial total water storage changes (ATWS) from the Gravity Recovery and Climate Experiment (GRACE) and precipitation data from the Tropical Rainfall Measuring Mission (TRMM) spanning from 2002 to 2013, where ATWS represents the main source of water available for human consumption, agriculture and natural ecosystems. We rely on a combination of temporal trend analysis and spatial statistics methods in order to evaluate the terrestrial total water storage (TWS) dynamics and the relationship between TWS and rainfall during the 'big dry' and 'big wet' events. Here we report the occurrence of hydrological cycle intensification during the study period in Australia which exhibited strong spatial variations: the wet areas (the northern and northeast regions) got wetter while the dry areas (the west and interior of the continent) became drier. By contrast, in southeastern Australia TWS changes over time showed sudden extreme responses to both events. Our results constitute a step beyond quantifying droughts/anomalous wet years that rely solely on precipitation data. This work demonstrates the ability of TWS observations as a significant indicator of hydrological system performance during hydroclimatic events and also an important tool for understanding continental-wide and regional spatial and temporal patterns of water availability

Spatial partitioning and temporal evolution of Australia's total water storage under extreme hydroclimatic impacts

Australia experienced one of the worst droughts in history during the early 21st-century (termed the 'big dry'), exerting negative impacts on food production and water supply, with increased forest die-back and bushfires across large areas. Following the 'big dry', one of the largest La Nina events in the past century, in conjunction with an extreme positive excursion of the Southern Annular Mode (SAM), resulted in dramatic increased precipitation from 2010 to 2011 (termed the 'big wet'), causing widespread flooding and a recorded sea level drop. Despite these extreme hydroclimatic impacts, the spatial partitioning and temporal evolution of total water storage across Australia remains unknown. In this study we investigated the spatial-temporal impacts of the recent 'big dry' and 'big wet' events on Australia's water storage dynamics using the total water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) satellites.Results showed widespread, continental-scale decreases in TWS during the 'big dry', resulting in a net loss of 3.89 +/- 0.47 cm (299 km(3)) total water, while the 'big wet' induced a sharp increase in TWS, equivalent to 11.68 +/- 0.52 cm (898 km(3)) of water, or three times the total water loss during the 'big dry'. We found highly variable continental patterns in water resources, involving differences in the direction, magnitude, and duration of TWS responses to drought and wet periods. These responses clustered into three distinct geographic zones that correlated well with the influences from multiple large-scale climate modes. Specifically, a persistent increasing trend in TWS was recorded over northern and northeastern Australia, where the climate is strongly influenced by El Nifio-Southern Oscillation (ENSO). By contrast, western Australia, a region predominantly controlled by the Indian Ocean Dipole (IOD), exhibited a continuous decline in TWS during the 'big dry' and only a subtle increase during the 'big wet', indicating a weak recovery of water storage. Southeastern Australia, influenced by combined ENSO, IOD and SAM interactions, exhibited a pronounced TWS drying trend during the 'big dry' followed by rapid TWS increases during the 'big wet', with complete water storage recoveries. A spatial intensification of the water cycle was further identified, with a wetting trend over wetter regions (northern and northeastern Australia) and a drying trend over drier regions (western Australia). Our results highlight the value of GRACE derived TWSA as an important indicator of hydrological system performance for improved water impact assessments and management of water resources across space and time. (C) 2016 Elsevier Inc. All rights reserved

A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications

Land surface phenology (LSP) characterizes episodes of greening and browning of the vegetated land surface from remote sensing imagery. LSP is of interest for quantification and monitoring of crop yield, wildfire fuel accumulation, vegetation condition, ecosystem response and resilience to climate variability and change. Deriving LSP represents an effort for end users and existing global products may not accommodate conditions in Australia, a country with a dry climate and high rainfall variability. To fill this information gap we developed the Australian LSP Product in contribution to AusCover/Terrestrial Ecosystem Research Network (TERN). We describe the product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface. Our product allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia. We demonstrate the metrics' response to ENSO-driven climate variability. Lastly, we discuss known limitations of the current product and future development plans