scholarly journals Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling

2020 ◽  
pp. 485-501 ◽  
Author(s):  
Néstor Fernández ◽  
Simon Ferrier ◽  
Laetitia M. Navarro ◽  
Henrique M. Pereira
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Spatial Scales ◽  
Data Sets ◽  
Biodiversity Monitoring ◽  
Multiple Sources ◽  
Biodiversity Data ◽  
Essential Biodiversity Variables ◽  
Critical Components

AbstractEssential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.

scholarly journals Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 367-374 ◽  
Author(s):  
R. R. Colditz ◽  
R. M. Llamas ◽  
R. A. Ressl
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Time Interval ◽  
Data Sets ◽  
Change Analysis ◽  
Dimensional Error ◽  
Analysis Technique ◽  
Coarse Spatial Resolution

Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth of techniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two important issues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations for successful change detection. We compare annual change detection maps accumulated over five years against direct change detection over that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsat change maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales better detect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales strongly recommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparison with coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowest cost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match between Landsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related to change detection errors in both data sets.

scholarly journals Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

2020 ◽  
Vol 12 (9) ◽  
pp. 1414
Author(s):  
Victoria M. Scholl ◽  
Megan E. Cattau ◽  
Maxwell B. Joseph ◽  
Jennifer K. Balch
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Training Data ◽  
Data Sets ◽  
Airborne Remote Sensing ◽  
Species Classification ◽  
Crown Diameter ◽  
External Data ◽  
Data Products

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

scholarly journals Spatial scales of optical variability in the coastal ocean: Implications for remote sensing and in situ sampling

2016 ◽  
Vol 121 (6) ◽  
pp. 4194-4208 ◽  
Author(s):  
Wesley J. Moses ◽  
Steven G. Ackleson ◽  
Johnathan W. Hair ◽  
Chris A. Hostetler ◽  
W. David Miller
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Coastal Ocean ◽  
Optical Variability

scholarly journals A Review of Approaches for Automated Habitat Mapping and their Potential Added Value for Biodiversity Monitoring Projects

2019 ◽  
Vol 12 (3) ◽  
pp. 53-69 ◽  
Author(s):  
Rob H.G. Jongman ◽  
Caspar A. Mücher ◽  
Robert G.H. Bunce ◽  
Mait Lang ◽  
Kalev Sepp
Keyword(s):  
Remote Sensing ◽  
Added Value ◽  
Biodiversity Monitoring ◽  
Sample Number ◽  
Automated Mapping ◽  
National Monitoring ◽  
Large Numbers ◽  
Reducing Costs ◽  
Remote Sensing Techniques

Abstract Habitats are important indicators of biodiversity in their own right, as well as being linked to species, hence their widespread use in reporting on nature conservation planning and policy. For reporting consistent mapping and monitoring habitat extent and change is important. Remote Sensing techniques are becoming an important tool for this. In this paper we describe four examples of methods of semi-automated mapping using Remote Sensing. Because the most effective way of improving the accuracy of the estimation of habitat area is by increasing the sample number, it is important to develop methods for reducing in situ surveys which are expensive. Remote Sensing has the major advantage of comprehensive coverage and the four examples illustrate the potential of extrapolation from semi-automated habitat classifications. The potential for using these methods at national scales is likely to be limited by the need for validation of the automated images and the subsequent calculation of error terms. Existing major national monitoring programs are described, which still use mainly traditional in situ methods. The selection of relatively small numbers of representative samples from environmental classifications to obtain regional estimates reduces the need for large numbers of in situ survey sites and is therefore discussed. The recent development of the use of drones to acquire detailed imagery to support in situ habitat surveys is also covered. Finally, practical problems linked to the methods described in the paper are considered, as in some cases these will override the theoretical benefits of a particular approach. It is concluded that automated methods can enhance existing monitoring systems and should be considered in any biodiversity monitoring system as they represent an opportunity for reducing costs, if integrated with an in situ program.

scholarly journals Comparison of Continuous In-Situ CO2 Measurements with Co-Located Column-Averaged XCO2 TCCON/Satellite Observations and CarbonTracker Model Over the Zugspitze Region

2019 ◽  
Vol 11 (24) ◽  
pp. 2981 ◽  
Author(s):  
Ye Yuan ◽  
Ralf Sussmann ◽  
Markus Rettinger ◽  
Ludwig Ries ◽  
Hannes Petermeier ◽  
...  
Keyword(s):  
Data Processing ◽  
Spatial Scales ◽  
Measurement Data ◽  
Data Retrieval ◽  
Data Sets ◽  
Air Masses ◽  
Sources And Sinks ◽  
Infrared Measurements ◽  
The Mean

Atmospheric CO2 measurements are important in understanding the global carbon cycle and in studying local sources and sinks. Ground and satellite-based measurements provide information on different temporal and spatial scales. However, the compatibility of such measurements at single sites is still underexplored, and the applicability of consistent data processing routines remains a challenge. In this study, we present an inter-comparison among representative surface and column-averaged CO2 records derived from continuous in-situ measurements, ground-based Fourier transform infrared measurements, satellite measurements, and modeled results over the Mount Zugspitze region of Germany. The mean annual growth rates agree well with around 2.2 ppm yr−1 over a 17-year period (2002–2018), while the mean seasonal amplitudes show distinct differences (surface: 11.7 ppm/column-averaged: 6.6 ppm) due to differing air masses. We were able to demonstrate that, by using consistent data processing routines with proper data retrieval and gap interpolation algorithms, the trend and seasonality can be well extracted from all measurement data sets.

An Introduction to Spatial Data Analysis

2020 ◽  
Author(s):  
Martin Wegmann ◽  
Jakob Schwalb-Willmann ◽  
Stefan Dech
Keyword(s):  
Remote Sensing ◽  
Data Analysis ◽  
Open Source ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Spatial Data Analysis ◽  
Data Sets ◽  
Data Handling ◽  
Book Covers

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as “What is the distance to the border of the protected area?”, “Which points are located close to a road?”, “Which fraction of land cover types exist in my study area?” using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

scholarly journals Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics

2017 ◽  
Vol 18 (3) ◽  
pp. 863-877 ◽  
Author(s):  
Joshua K. Roundy ◽  
Joseph A. Santanello
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Water Cycle ◽  
Spatial Scales ◽  
Complex Nature ◽  
Sufficient Information ◽  
Tropospheric Temperature ◽  
In Situ Data

Abstract Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

scholarly journals How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring

2017 ◽  
Vol 10 ◽  
pp. 43-59 ◽  
Author(s):  
Petteri Vihervaara ◽  
Ari-Pekka Auvinen ◽  
Laura Mononen ◽  
Markus Törmä ◽  
Petri Ahlroth ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Biodiversity Monitoring ◽  
Essential Biodiversity Variables

scholarly journals CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

2016 ◽  
Vol XLI-B8 ◽  
pp. 669-672
Author(s):  
B. A. Johnson ◽  
H. Scheyvens ◽  
H. Samejima ◽  
M. Onoda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Median Number ◽  
Forest Carbon ◽  
Data Sets ◽  
Median Percentage ◽  
Forestry Sector

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.

scholarly journals The Use of Remote Sensing to Enhance Biodiversity Monitoring and Detection: A Critical Challenge for the Twenty-First Century

2020 ◽  
pp. 1-12
Author(s):  
Jeannine Cavender-Bares ◽  
John A. Gamon ◽  
Philip A. Townsend
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Biodiversity Loss ◽  
Assessment Process ◽  
Biodiversity Monitoring ◽  
National Academy Of Sciences ◽  
The Status ◽  
Monitoring Of Biodiversity ◽  
Terrestrial Biodiversity ◽  
Academy Of Sciences

AbstractImproved detection and monitoring of biodiversity is critical at a time when the Earth’s biodiversity loss due to human activities is accelerating at an unprecedented rate. We face the largest loss of biodiversity in human history, a loss which has been called the “sixth mass extinction” (Leakey 1996; Kolbert 2014), given that its magnitude is in proportion to past extinction episodes in Earth history detectable from the fossil record. International efforts to conserve biodiversity (United Nations 2011) and to develop an assessment process to document changes in the status and trends of biodiversity globally through the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Díaz et al. 2015) have raised awareness about the critical need for continuous monitoring of biodiversity at multiple spatial scales across the globe. Biodiversity itself—the variation in life found among ecosystems and organisms at any level of biological organization—cannot practically be observed everywhere. However, if habitats, functional traits, trait diversity, and the spatial turnover of plant functions can be remotely sensed, the potential exists to globally inventory the diversity of habitats and traits associated with terrestrial biodiversity. To face this challenge, there have been recent calls for a global biodiversity monitoring system (Jetz et al. 2016; Proença et al. 2017; The National Academy of Sciences 2017). A central theme of this volume is that remote sensing (RS) will play a key role in such a system.

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