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Special Issue "Earth Observation for Ecosystems Monitoring in Space and Time"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2014)

Special Issue Editor

Guest Editor
Dr. Duccio Rocchini (Website)

GIS and Remote Sensing Unit, Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Via Mach 1, 38010 San Michele all'Adige (TN), Italy
Fax: +39 3491425786
Interests: ecological Informatics; ecological heterogeneity and biodiversity estimate by satellite imagery; Free and Open Source Software for spatial ecology; statistical analysis of spatial and ecological data

Special Issue Information

Dear Colleagues,

The State of the Art

Nowadays, a number of different sensors are available for studying ecosystems from space. Further, ecological theory has been applied to remote sensing data to monitor species dispersal and diversity over space and time. Ecosystem-based models have also been developed to monitor, at a high temporal resolution, Earth surface changes over large areas. The need for high temporal resolution for studying global and local changes needs utilizing techniques other than field-based monitoring. Consequently, remote sensing is critical for ecosystems monitoring.

Rationale for the special issue

Remote sensing and ecosystems monitoring challenges include (i) scale issues, (ii) data gathering and analysis, and (iii) software development. This Special Issue encourages discussion concerning innovative techniques/approaches that are based on remote sensing data, which are used for the study of ecosystems at different spatial and temporal scales. Research scientists and other subject matter experts are encouraged to submit innovative and challenging papers that show advances in following topics:

  • Earth Observation systems for ecosystems monitoring
  • New Earth Observation sensors for ecosystems monitoring
  • Derivation of climate variables for ecosystems monitoring in space and time
  • Ecological Informatics
  • Biodiversity estimate by remote sensing
  • Free and Open Source Software for spatial ecology
  • Time series analysis
  • Statistical analysis of spatial and ecological data

Dr. Duccio Rocchini
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Published Papers (11 papers)

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Editorial

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Open AccessEditorial Earth Observation for Ecosystems Monitoring in Space and Time: A Special Issue in Remote Sensing
Remote Sens. 2015, 7(6), 8102-8106; doi:10.3390/rs70608102
Received: 15 June 2015 / Accepted: 16 June 2015 / Published: 18 June 2015
PDF Full-text (453 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This Editorial introduces the papers published in the special issue “Earth Observation for Ecosystems Monitoring in Space and Time” which includes the most important researchers in the field and the most challenging aspects of the application of remote sensing to study ecosystems. [...] Read more.
This Editorial introduces the papers published in the special issue “Earth Observation for Ecosystems Monitoring in Space and Time” which includes the most important researchers in the field and the most challenging aspects of the application of remote sensing to study ecosystems. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Research

Jump to: Editorial

Open AccessArticle Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile
Remote Sens. 2015, 7(3), 2692-2714; doi:10.3390/rs70302692
Received: 1 September 2014 / Revised: 18 February 2015 / Accepted: 27 February 2015 / Published: 9 March 2015
Cited by 5 | PDF Full-text (31720 KB) | HTML Full-text | XML Full-text
Abstract
The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation [...] Read more.
The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Assessing Habitat Quality of Forest-Corridors through NDVI Analysis in Dry Tropical Forests of South India: Implications for Conservation
Remote Sens. 2015, 7(2), 1619-1639; doi:10.3390/rs70201619
Received: 21 August 2014 / Revised: 23 December 2014 / Accepted: 12 January 2015 / Published: 4 February 2015
Cited by 1 | PDF Full-text (32619 KB) | HTML Full-text | XML Full-text
Abstract
Most wildlife habitats and migratory routes are extremely threatened due to increasing demands on forestland and forest resources by burgeoning human population. Corridor landscape in Biligiri Rangaswamy Temple Tiger Reserve (BRT) is one among them, subjected to various anthropogenic pressures. Human habitation, [...] Read more.
Most wildlife habitats and migratory routes are extremely threatened due to increasing demands on forestland and forest resources by burgeoning human population. Corridor landscape in Biligiri Rangaswamy Temple Tiger Reserve (BRT) is one among them, subjected to various anthropogenic pressures. Human habitation, intensive farming, coffee plantations, ill-planned infrastructure developments and rapid spreading of invasive plant species Lantana camara, pose a serious threat to wildlife habitat and their migration. Aim of this work is to create detailed NDVI based land change maps and to use them to identify time-series trends in greening and browning in forest corridors in the study area and to identify the drivers that are influencing the observed changes. Over the four decades in BRT, NDVI increased in the core area of the forest and reduced in the fringe areas. The change analysis between 1973 and 2014 shows significant changes; browning due to anthropogenic activities as well as natural processes and greening due to Lantana spread. This indicates that the change processes are complex, involving multiple driving factors, such as socio-economic changes, high population growth, historical forest management practices and policies. Our study suggests that the use of updated and accurate change detection maps will be useful in taking appropriate site specific action-oriented conservation decisions to restore and manage the degraded critical wildlife corridors in human-dominated landscape. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch
Remote Sens. 2014, 6(11), 11579-11606; doi:10.3390/rs61111579
Received: 30 August 2014 / Revised: 29 October 2014 / Accepted: 12 November 2014 / Published: 20 November 2014
Cited by 10 | PDF Full-text (13361 KB) | HTML Full-text | XML Full-text
Abstract
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers [...] Read more.
The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch (CRW) program has developed a daily global 5-km product suite based on satellite observations to monitor thermal stress on coral reefs. These products fulfill requests from coral reef managers and researchers for higher resolution products by taking advantage of new satellites, sensors and algorithms. Improvements of the 5-km products over CRW’s heritage global 50-km products are derived from: (1) the higher resolution and greater data density of NOAA’s next-generation operational daily global 5-km geo-polar blended sea surface temperature (SST) analysis; and (2) implementation of a new SST climatology derived from the Pathfinder SST climate data record. The new products increase near-shore coverage and now allow direct monitoring of 95% of coral reefs and significantly reduce data gaps caused by cloud cover. The 5-km product suite includes SST Anomaly, Coral Bleaching HotSpots, Degree Heating Weeks and Bleaching Alert Area, matching existing CRW products. When compared with the 50-km products and in situ bleaching observations for 2013–2014, the 5-km products identified known thermal stress events and matched bleaching observations. These near reef-scale products significantly advance the ability of coral reef researchers and managers to monitor coral thermal stress in near-real-time. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain
Remote Sens. 2014, 6(11), 11391-11419; doi:10.3390/rs61111391
Received: 22 July 2014 / Revised: 21 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014
Cited by 4 | PDF Full-text (15907 KB) | HTML Full-text | XML Full-text
Abstract
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes [...] Read more.
Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a major identification of Ecosystem Functional Types (EFTs) in Spain to characterize the patterns of ecosystem functional diversity and status, from several functional attributes as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Albedo. For this purpose, several metrics, related to the spatial variability in seasonal and annual patterns (e.g., relative range), have been derived from remote sensing time series of 1 km MODIS over the period 2000–2009. Moreover, precipitation maps from data provided by the AEMet (Agencia Estatal de Meteorología) and the corresponding aridity and humidity indices were also included in the analysis. To create the EFTs, the potential of the joint use of Kohonen’s Self-Organizing Map (SOM) and the k-means clustering algorithm was tested. The EFTs were analyzed using different remote sensing (i.e., Gross Primary Production) and climatic variables. The relationship of the EFTs with existing land cover datasets and climatic data were analyzed through a correspondence analysis (CA). The trained SOM have shown feasible in providing a comprehensive view on the functional attributes patterns and a remarkable potential for the quantification of ecosystem function. The results highlight the potential of this technique to delineate ecosystem functional types as well as to monitor the spatial pattern of the ecosystem status as a reference for changes due to human or climate impacts. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data
Remote Sens. 2014, 6(10), 9802-9828; doi:10.3390/rs6109802
Received: 19 May 2014 / Revised: 19 September 2014 / Accepted: 24 September 2014 / Published: 14 October 2014
Cited by 1 | PDF Full-text (5333 KB) | HTML Full-text | XML Full-text
Abstract
Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, [...] Read more.
Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Open AccessArticle Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales
Remote Sens. 2014, 6(10), 9298-9315; doi:10.3390/rs6109298
Received: 5 May 2014 / Revised: 18 September 2014 / Accepted: 22 September 2014 / Published: 29 September 2014
Cited by 5 | PDF Full-text (2397 KB) | HTML Full-text | XML Full-text
Abstract
We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, [...] Read more.
We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011) with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy) reserve at multiple spatial extents (128, 256 and 512 pixels). We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to detect forest variations over time. Similar procedures can (i) help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii) minimize the bias when comparing pattern metrics at a single extent and (iii) be extended to other landscapes and scales. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models
Remote Sens. 2014, 6(9), 8945-8965; doi:10.3390/rs6098945
Received: 20 May 2014 / Revised: 28 August 2014 / Accepted: 10 September 2014 / Published: 22 September 2014
Cited by 5 | PDF Full-text (11381 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies [...] Read more.
Terrestrial gross primary production (GPP) is the largest global CO2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000–2010 varied from 95.10 to 139.71 Pg C∙yr1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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Open AccessArticle Classification of Grassland Successional Stages Using Airborne Hyperspectral Imagery
Remote Sens. 2014, 6(8), 7732-7761; doi:10.3390/rs6087732
Received: 8 April 2014 / Revised: 30 July 2014 / Accepted: 30 July 2014 / Published: 20 August 2014
Cited by 8 | PDF Full-text (5355 KB) | HTML Full-text | XML Full-text
Abstract
Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between [...] Read more.
Plant communities differ in their species composition, and, thus, also in their functional trait composition, at different stages in the succession from arable fields to grazed grassland. We examine whether aerial hyperspectral (414–2501 nm) remote sensing can be used to discriminate between grazed vegetation belonging to different grassland successional stages. Vascular plant species were recorded in 104.1 m2 plots on the island of Öland (Sweden) and the functional properties of the plant species recorded in the plots were characterized in terms of the ground-cover of grasses, specific leaf area and Ellenberg indicator values. Plots were assigned to three different grassland age-classes, representing 5–15, 16–50 and >50 years of grazing management. Partial least squares discriminant analysis models were used to compare classifications based on aerial hyperspectral data with the age-class classification. The remote sensing data successfully classified the plots into age-classes: the overall classification accuracy was higher for a model based on a pre-selected set of wavebands (85%, Kappa statistic value = 0.77) than one using the full set of wavebands (77%, Kappa statistic value = 0.65). Our results show that nutrient availability and grass cover differences between grassland age-classes are detectable by spectral imaging. These techniques may potentially be used for mapping the spatial distribution of grassland habitats at different successional stages. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Open AccessArticle Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring
Remote Sens. 2014, 6(7), 6163-6182; doi:10.3390/rs6076163
Received: 23 April 2014 / Revised: 30 May 2014 / Accepted: 23 June 2014 / Published: 30 June 2014
Cited by 10 | PDF Full-text (8109 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most [...] Read more.
The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
Open AccessArticle Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model
Remote Sens. 2014, 6(6), 4660-4686; doi:10.3390/rs6064660
Received: 28 March 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 26 May 2014
Cited by 5 | PDF Full-text (461 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the [...] Read more.
Vegetation phenology plays an important role in regulating processes of terrestrial ecosystems. Dynamic ecosystem models (DEMs) require representation of phenology to simulate the exchange of matter and energy between the land and atmosphere. Location-specific parameterization with phenological observations can potentially improve the performance of phenological models embedded in DEMs. As ground-based phenological observations are limited, phenology derived from remote sensing can be used as an alternative to parameterize phenological models. It is important to evaluate to what extent remotely sensed phenological metrics are capturing the phenology observed on the ground. We evaluated six methods based on two vegetation indices (VIs) (i.e., Normalized Difference Vegetation Index and Enhanced Vegetation Index) for retrieving the phenology of temperate forest in the Agro-IBIS model. First, we compared the remotely sensed phenological metrics with observations at Harvard Forest and found that most of the methods have large biases regardless of the VI used. Only two methods for the leaf onset and one method for the leaf offset showed a moderate performance. When remotely sensed phenological metrics were used to parameterize phenological models, the bias is maintained, and errors propagate to predictions of gross primary productivity and net ecosystem production. Our results show that Agro-IBIS has different sensitivities to leaf onset and offset in terms of carbon assimilation, suggesting it might be better to examine the respective impact of leaf onset and offset rather than the overall impact of the growing season length. Full article
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
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