Satellite-based monitoring of biodiversity

2019 ◽  
pp. 20-36
Author(s):  
Nathalie Pettorelli
Keyword(s):  
Remote Sensing ◽  
Genetic Structure ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Species Traits ◽  
Ecosystem Functions ◽  
Multiple Spatial Scales ◽  
Satellite Information ◽  
Monitoring Of Biodiversity ◽  
Composition Structure

This chapter explores how satellite information can help track biodiversity at multiple spatial scales and resolutions. To attempt to capture the various organisational dimensions of biodiversity, the chapter focuses on six classes of variables: genetic structure and composition, species populations, species traits, ecosystem extent and structure, ecosystem composition, and ecosystem functions. From helping map species and ecosystem distribution to supporting the monitoring of ecosystem composition, structure, and functioning, this contribution provides a series of examples that demonstrate how satellite remote sensing can open multiple opportunities for various stakeholders to improve on the management of wildlife. The chapter concludes with a discussion on limitations and possible ways forward.

scholarly journals Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

2021 ◽  
Vol 128 ◽  
pp. 107822
Author(s):  
Megan M. Coffer ◽  
Blake A. Schaeffer ◽  
Wilson B. Salls ◽  
Erin Urquhart ◽  
Keith A. Loftin ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Cyanobacterial Bloom ◽  
The United States ◽  
Multiple Spatial Scales

scholarly journals Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

2014 ◽  
Vol 11 (16) ◽  
pp. 4305-4320 ◽  
Author(s):  
S. T. Klosterman ◽  
K. Hufkens ◽  
J. M. Gray ◽  
E. Melaas ◽  
O. Sonnentag ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Curve Fitting ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Spatial Scales ◽  
Visual Assessment ◽  
Statistical Uncertainty ◽  
Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

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 CLIMATIC VARIABILITY PREDICTION WITH SATELLITE REMOTE SENSING AND METEOROLOGICAL DATA IN THE SOUTH WESTERN NIGERIA

2013 ◽  
Vol 39 (2) ◽  
pp. 59-63
Author(s):  
Ebenezer Yemi Ogunbadewa
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Spatial Scales ◽  
Climatic Variability ◽  
Data Availability ◽  
The South ◽  
Suitable Alternative ◽  
Temporal And Spatial ◽  
Satellite Sensor

Climatic variability affects both seasonal phenological cycles of vegetation and monthly distribution of rainfall in the south western Nigeria. Variations in vegetation biophysical parameters have been known to be a good indicator of climate variability; hence they are used as key inputs into climate change models. However, understanding the response of vegetation to the influence of climate at both temporal and spatial scales have been a major challenge. This is because most climatic data available are derived from ground-based instruments, which are mainly point measurements and are characterized by sparse network of meteorological stations that lacks the spatial coverage required for climate change investigation. Satellite remote sensing instruments can provide a suitable alternative of time-reliable datasets in a more consistent manner at both temporal and spatial scales. The aim of this study is to test the suitability of one year time series datasets obtained from satellite sensor and meteorological stations as a starting point for the development of a climate change model that can be exploited in planning adaptation strategies. Taking into consideration that rainfall is the most variable element of climate in the study area, rainfall data acquired from five meteorological stations for the year 2006 were correlated with changes in Normalized Difference Vegetation Index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite sensor for the same period using a linear regression equation. The results shows that rainfall–NDVI relationship was stronger along the seasonal track with R2 ranging from 0.74 to 0.94, indicating that NDVI seasonal variations can be used as a surrogate data source for monitoring climate change for short and long term scales ranging from regional to global magnitude especially in areas where data availability from ground-based measurements are unreliable.

Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA

2014 ◽  
Vol 143 ◽  
pp. 131-141 ◽  
Author(s):  
Hao Tang ◽  
Matthew Brolly ◽  
Feng Zhao ◽  
Alan H. Strahler ◽  
Crystal L. Schaaf ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
National Forest ◽  
Area Index ◽  
Multiple Spatial Scales

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Model Performance ◽  
Next Generation ◽  
Distribution Models

AbstractBiodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Next Generation ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Conservation Actions

Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON), we assessed the performance of stacked species distribution models (S-SDMs), constructed using satellite remote sensing as covariates and under a Bayesian framework, in order to build the next-generation of biodiversity models. This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from satellite remote sensing represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, not necessarily results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), this kind of biodiversity models do not accurately recover the observed species composition at plot level or ecological scales (NEON plots), however, they do return reasonable predictions at macroecological scales, i.e., mid to high correct assignment of species identities at the scale of NEON sites. Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

scholarly journals Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

2016 ◽  
Author(s):  
Brady S. Hardiman ◽  
Christopher M. Gough ◽  
John R. Butnor ◽  
Gil Bohrer ◽  
Matteo Detto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Canopy Structure ◽  
Structural Data ◽  
Ecosystem Functions ◽  
Stand Age ◽  
Ecosystem Structure ◽  
Root Mass ◽  
Root Structure ◽  
Area Index

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

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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