Land Cover Mapping using Digital Earth Australia

This study establishes the use of the Earth Observation Data for Ecosystem Monitoring (EODESM) to generate land cover and change classifications based on the United Nations Food and Agriculture Organisation (FAO) Land Cover Classification System (LCCS) and environmental variables (EVs) available within, or accessible from, Geoscience Australia’s (GA) Digital Earth Australia (DEA). Classifications representing the LCCS Level 3 taxonomy (8 categories representing semi-(natural) and/or cultivated/managed vegetation or natural or artificial bare or water bodies) were generated for two time periods and across four test sites located in the Australian states of QueenslandandNewSouthWales. Thiswasachievedbyprogressivelyandhierarchicallycombining existing time-static layers relating to (a) the extent of artificial surfaces (urban, water) and agriculture and (b) annual summaries of EVs relating to the extent of vegetation (fractional cover) and water (hydroperiod, intertidal area, mangroves) generated through DEA. More detailed classifications that integrated information on, for example, forest structure (based on vegetation cover (%) and height (m); time-static for 2009) and hydroperiod (months), were subsequently produced for each time-step. The overall accuracies of the land cover classifications were dependent upon those reported for the individual input layers, with these ranging from 80% (for cultivated, urban and artificial water) to over95%(forhydroperiodandfractionalcover).Thechangesidentifiedincludemangrovediebackin the southeastern Gulf of Carpentaria and reduced dam water levels and an associated expansion of vegetation in Lake Ross, Burdekin. The extent of detected changes corresponded with those observed using time-series of RapidEye data (2014 to 2016; for the Gulf of Carpentaria) and Google Earth imagery (2009–2016 for Lake Ross). This use case demonstrates the capacity and a conceptual framework to implement EODESM within DEA and provides countries using the Open Data Cube (ODC) environment with the opportunity to routinely generate land cover maps from Landsat or Sentinel-1/2 data, at least annually, using a consistent and internationally recognised taxonomy

Establishment of experimental catchments to quantify water use by different vegetation typesEstabelecimento de microbacias experimentais para quantificar uso de água por diferentes tipos de vegetação

AbstractThis paper describes the procedure developed to select representative experimental catchments to quantify water use and water use efficiency of different vegetation types. We combined freely available information such as LANDSAT satellite images, digital elevation model, Google Earth images, streamflow and rainfall historical data, and soils and vegetation data to select the experimental catchments. Once these catchments were indentified, sub-catchments with different vegetation were delineated, which were used to define the distribution and installation of the instrumentation. Forty eight catchments and respective sub-catchments of Tasmania in Australia were analyzed and only two catchments were identified as ideal for the purposes of quantifying and comparing the historic and current water use by Eucalyptus plantation, native forest and pasture. We choose one catchment and created four experimental and instrumented sub-catchments with the following predominant land use: Eucalyptus nitens, native forest and pasture. ResumoO presente artigo descreve o procedimento desenvolvido para selecionar microbacias representativas para quantificar o uso de água e a eficiência do uso de água de diferentes tipos de vegetação. Foram combinadas informações disponíveis sem custo tais como: imagens de satélite LANDSAT, modelo digital do terreno, imagens de Google Earth, escoamento superficial e dados históricos de precipitação pluviométrica, tipos de solos e cobertura vegetal para a seleção de microbacias experimentais. Uma vez identificadas as microbacias, sub-microbacias com diferente tipos de vegetação foram delineadas, as quais foram usadas para definir a distribuição e instalação de instrumentação. Foram analisadas quarenta e oito microbacias e respectivas sub-microbacias na Tasmânia na Austrália e somente duas microbacias foram identificadas como ideais para o propósito de quantificar e comparar o histórico e atual uso de água por plantações de eucalipto, floresta nativa e pastagem. Ao final foi escolhida uma microbacia e quatro sub-microbacias experimentais instrumentadas com o uso de solo predominantemente de Eucalyptus nitens, floresta nativa e pastagem

Mobile Field Data Collection for Post Bushfire Analysis and African Farmers

Part 2: Information Systems, Information Modelling and SemanticsInternational audienceIn recent years CSIRO has been trialling field data collection using mobile devices such as phones and tablets. Two recent tools that have been developed by CSIRO are the CSIRO Surveyor (Post Bushfire House Surveyor) and DroidFarmer. Challenges tackled include mapping field documents to mobile data through QR (Quick Response) codes, rapid input of survey data, accurate capture of GPS locations and offline operation. Throughout this paper we detail the design choices made for these systems. We give details of how well field data collection was performed and discuss our planned future developments in this space

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results s..

Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR

Variations in vertical and horizontal forest structure are often difficult to quantify as field-based methods are labour intensive and passive optical remote sensing techniques are limited in their capacity to distinguish structural changes occurring below the top of the canopy. In this study the capacity of small footprint (0.19 cm), discrete return, densely spaced (0.7 hits/m?2), multiple return, Light Detection and Ranging (LiDAR) technology, to measure foliage height and to estimate several stand and canopy structure attributes is investigated. The study focused on six Douglas-fir [Pseudotsuga menziesii spp. menziesii (Mirb.) Franco] and western hemlock [Tsuga heterophylla (Raf.) Sarg.] stands located on the east coast of Vancouver Island, British Columbia, Canada, with each stand representing a different structural stage of stand development for forests within this biogeoclimatic zone. Tree height, crown dimensions, cover, and vertical foliage distributions were measured in 20 m × 20 m plots and correlated to the LiDAR data. Foliage profiles were then fitted, using the Weibull probability density function, to the field measured crown dimensions, vertical foliage density distributions and the LiDAR data at each plot. A modified canopy volume approach, based on methods developed for full waveform LiDAR observations, was developed and used to examine the vertical and horizontal variation in stand structure. Results indicate that measured stand attributes such as mean stand height, and basal area were significantly correlated with LiDAR estimates (r 2 = 0.85, P < 0.001, SE = 1.8 m and r 2 = 0.65, P < 0.05, SE = 14.8 m2 ha?1, respectively). Significant relationships were also found between the LiDAR data and the field estimated vertical foliage profiles indicating that models of vertical foliage distribution may be robust and transferable between both field and LiDAR datasets. This study demonstrates that small footprint, discrete return, LiDAR observations can provide quantitative information on stand and tree height, as well as information on foliage profiles, which can be successfully modelled, providing detailed descriptions of canopy structure

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)