Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Philosophical Transactions of the Royal Society B (2014) doi: 10.1098/rstb.2013.0190

Assessing and predicting ecosystem responses to global environmental change and its impacts on human well-being are high priority targets for the scientific community. The potential for synergies between remote sensing science and ecology, especially satellite remote sensing and conservation biology, has been highlighted by many in the past. Yet, the two research communities have only recently begun to coordinate their agendas. Such synchronization is the key to improving the potential for satellite data effectively to support future environmental management decision-making processes. With this themed issue, we aim to illustrate how integrating remote sensing into ecological research promotes a better understanding of the mechanisms shaping current changes in biodiversity patterns and improves conservation efforts. Added benefits include fostering innovation, generating new research directions in both disciplines and the development of new satellite remote sensing products.

Temperature is a main driver for most ecological processes, and temperature time series provide key environmental indicators for various applications and research fields. High spatial and temporal resolutions are crucial for detailed analyses in various fields of research. A disadvantage of temperature data obtained by satellites is the occurrence of gaps that must be reconstructed. Here, we present a new method to reconstruct high-resolution land surface temperature (LST) time series at the continental scale gaining 250-m spatial resolution and four daily values per pixel. Our method constitutes a unique new combination of weighted temporal averaging with statistical modeling and spatial interpolation. This newly developed reconstruction method has been applied to greater Europe, resulting in complete daily coverage for eleven years. To our knowledge, this new reconstructed LST time series exceeds the level of detail of comparable reconstructed LST datasets by several orders of magnitude. Studies on emerging diseases, parasite risk assessment and temperature anomalies can now be performed on the continental scale, maintaining high spatial and temporal detail. We illustrate a series of applications in this paper. Our dataset is available online for download as time aggregated derivatives for direct usage in GIS-based applications (Reconstructed MODIS Land Surface Temperature Dataset - http://gis.cri.fmach.it/eurolst/).

Source: Metz M, Rocchini D, Neteler M. (2014) Surface Temperatures at the Continental Scale: Tracking Changes with Remote Sensing at Unprecedented Detail.Remote Sensing 6(5): 3822-3840. doi: 10.3390/rs6053822

Abstract: In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

A new EU BON acknowledging special issue "Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing" published in the open access journal Remote Sensing provides a collection of  important researchers in the field, as well as the most challenging aspects of the application of remote sensing to study ecosystems. 

From 9 to 11 November in Vienna, Austria the EU H2020 project Detecting changes in essential ecosystem and biodiversity properties – towards a Biosphere Atmosphere Change Index: BACI has organised a special workshop titled "Remote sensing applications related to land use/change" with the aim to facilitate co-design and co-production of knowledge with regard to innovative applications of remote sensing products.

EU BON project partner Duccio Rocchini was among the invited lecturers at the event. His talk titled "Like in a Rubik’s cube: Recomposing Biodiversity Information by Remote Sensing Data" introduced some experience from EU BON.

The overarching objective of BACI is to tap into the unrealized potential of existing and scheduled space-borne Earth observation data streams to detect changes in ecosystem functioning and services that have repercussions for essential biodiversity variables, land use potentials, and land-atmosphere interactions.

The Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente has recently launched an investment programme to strengthen its international academic fields. For 11 pioneering-multidisciplinairy projects a PhD-position is made available, three of them already are filled in. The Department of Natural Resources (NRS) specialises in advanced spatial and temporal analysis and technique development for the environment as well as sustainable agriculture.

Job Description: 

For more information visit the official job offer.

The importance of measuring species diversity as an indicator of ecosystem health has been long recognized and it seems that satellite remote sensing (SRS) has proven to be one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. What is the real potential of SRS and what are the pitfalls that need to be avoided to achieve the full potential of this method is the topic of a new research, published in the journal Remote Sensing in Ecology and Conservation.

The new study, supported by the FP7 funded EU project EU BON takes the assessment of diversity in plant communities as a case study. Showing the difficulties to achieve high results by relying only on field data, the paper discusses the advantages of SRS methods.

"In contrast to field-based methods, SRS allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, it provides repeated measures, thus making it possible to study temporal changes in biodiversity," explains Dr. D. Rocchini from Fondazione Edmund Mach, lead author and WP deputy leader / task leader in EU BON. "In our research we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. "

Traditionally, assessment of biodiversity at local and regional scales relies on the one hand on local diversity, or the so called alpha-diversity, and on the other, on species turnover, or beta-diversity. Only in combination of these two measures can lead to an estimate of the whole diversity of an area.

While the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity could prove to be quite challenging. This is where increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments.

The new research shown the high potential of remote sensing in biodiversity studies while also identifying the challenges underpinning the development of this interdisciplinary field of research.

"Further sensitivity studies on environmental parameters derived from remote sensing for biodiversity mapping need to be undertaken to understand the pitfalls and impacts of different data collection processes and models. Such information, however, is crucial for a continuous global biodiversity analysis and an improved understanding of our current global challenges."concludes Dr. Rocchini.

Original Source:

Rocchini, D., Boyd, D. S., Féret, J.-B., Foody, G. M., He, K. S., Lausch, A., Nagendra, H., Wegmann, M., Pettorelli, N. (2016), Satellite remote sensing to monitor species diversity: potential and pitfalls. Remote Sensing in Ecology and Conservation, 2: 25-36. doi: 10.1002/rse2.9

Key in ensuring the effectiveness of conservation efforts and maintaining ecosystem health, measuring biodiversity can benefit greatly when remote sensing data comes into the equation. A new EU BON related paper, published in the journal Ecological Indicators, proposes open source solutions for measuring the important Rao's Q index, when it comes to remote sensing data.

Abstract: 

Measuring biodiversity is a key issue in ecology to guarantee effective indicators of ecosystem health at different spatial and time scales. However, estimating biodiversity from field observations might present difficulties related to costs and time needed. Moreover, a continuous data update for biodiversity monitoring purposes might be prohibitive. From this point of view, remote sensing represents a powerful tool since it allows to cover wide areas in a relatively low amount of time. One of the most common indicators of biodiversity is Shannon's entropy H′, which is strictly related to environmental heterogeneity, and thus to species diversity. However, Shannon's entropy might show drawbacks once applied to remote sensing data, since it considers relative abundances but it does not explicitly account for distances among pixels’ numerical values. In this paper we propose the use of Rao's Q applied to remotely sensed data, providing a straightforward R-package function to calculate it in 2D systems. We will introduce the theoretical rationale behind Rao's index and then provide applied examples based on the proposed R function.

Original Source: 

Rocchini, D., Marcantonio, M., Ricotta, C. (2017). Measuring Rao's Q diversity index rom remote sensing: an open source solution. Ecological Indicators, 72: 234-238. [5years-IF: 3.649] DOI:10.1016/j.ecolind.2016.07.039

36th International Symposium on Remote Sensing of Environment (ISRSE) will take place on May 11-15, 2015 in Berlin, Germany.

This 36th Symposium will represent a major event in the long series of internationally recognized ISRSE meetings. The overall theme of the symposium is the use of Earth Observation systems and related Remote Sensing techniques for understanding and managing the Earth environment and resources.

The event will also feature sessions "Biodiversity and Conservation" aiming to show the developments and potential of remote sensing within biodiversity and conservation science.

Find out more about this session in the brochure attached below or n the event website: www.isrse36.org

All 12 themes for abstract submission are listed in the Technical programme: http://www.isrse36.org/technical-programme/ and under Abstract submission: http://meetingorganizer.copernicus.org/ISRSE36/sessionprogramme and Deadline for Abstracts is latest 9 November 2014.

Registration: http://www.isrse36.org/registration/

The International Symposium "What can Remote Sensing do for the Conservation of Wetlands?" will take place on 23 October 2015 in parallel with the XVI Congreso de la Asociación Española de Teledetección (XVI Meeting of the Spanish Remote Sensing Association) in Seville, Spain.

The Symposium aims to become an interdisciplinary meeting for wetland managers and scientists interested in remote sensing as well as remote sensing experts doing research in wetlands.

Wetlands are fragile and dynamic ecosystems sensitive to changes in climate and land-use, and rich in biodiversity. For centuries they were considered to have little or no value, and most have been drained or transformed. In 1971 the first international convention for the protection of Wetlands, the Ramsar Convention, was signed to promote their conservation and sustainable use. Now it is recognized that wetlands provide fundamental ecosystem services, such as water regulation, filtering and purification, as well as scientific, cultural, and recreational values. Wetlands constitute an extensive array of ecosystems ranging from lakes and rivers to marshes and tidal flats. An increasing number of wetlands have some kind of legal protection, and many wetlands are monitored and actively managed.

For more information on the symposium, please visit the official webpage: http://wetlandssymposium.com/

IEEE / Institute of Electrical and Electronics Engineers Incorporated

IEEE / Institute of Electrical and Electronics Engineers Incorporated

The storage and flux of terrestrial carbon (C) is one of the most uncertain components of the global C budget and detailed quantification of forest […]

The post Remote sensing of a Smithsonian forest with airborne LiDAR appeared first on Smithsonian Insider.

Monitoring and assessment of habitats is essential to evaluate biodiversity policy and improve the condition of valuable ecosystems. A new study has reviewed the value of remote sensing — using information from satellite or airborne imagery — for this purpose, and the authors make a series of key recommendations, including the importance of tailoring remote sensing output for policymakers.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

A tag includes an integrated circuit thereon, including memory, antenna means for RF transmission of data acquired by the integrated circuit, a sensor sensing environmental conditions to which the sensor is subjected, the sensor providing signals based on the environmental conditions to the integrated circuit, a thermistor device of increasing the accuracy of the tag by confining on-board calculation data to a resistance domain and leaving conversion of the data to a temperature domain for an external device once the data is retrieved from the tag's memory.

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Rotch Library - G70.212.I565 2017

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Rotch Library - G70.212.Q45 2018

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Rotch Library - G70.4.D66 2018

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Rotch Library - GA102.4.E4 B54 2018

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Barker Library - G70.4.P48 2019

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