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Special Issue "Remote Sensing of Vegetation Structure and Dynamics"

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

Deadline for manuscript submissions: closed (29 May 2016)

Special Issue Editors

Guest Editor
Dr. Sangram Ganguly

NASA Ames Research Center and Bay Area Environmental Research Institute, Bldg. 19, Suite 2031, NASA Ames Research Center, Moffett Field, CA 94035, USA
Website | E-Mail
Phone: +1 (617) 319 6249
Interests: radiative transfer theory; advanced remote sensing techniques for carbon modeling and vegetation structure; climate modeling; high performance computing and cloud computing; machine learning and data science; large-scale image processing and signal processing
Guest Editor
Dr. Compton Tucker

NASA Goddard Space Flight Center, Mail Code: 610.9, Greenbelt, MD 20771, USA
Website | E-Mail
Phone: +1 (301) 614 6644
Interests: earth systems research; advanced remote sensing techniques for vegetation monitoring and dynamics; climate modeling; long-term data records for vegetation dynamics; famine early warning systems; crop yield monitoring and forecasting

Special Issue Information

Dear Colleagues,

Monitoring of vegetation structure and functioning is critical to modeling terrestrial material and energy cycles, ecosystem productivity, and land use/land cover dynamics within the general context of climate change. Satellite remote sensing is ideally suited for vegetation monitoring as it provides multi-decadal observations at a range of spatio-temporal scales. The advances in remote sensing, both in theory and instrumentation, have paved the way for better understanding the partitioning of radiative energy between the Earth’s surface and atmosphere. Moreover, there is a pressing need in devising methodologies and techniques for creating consistent long-term data records for vegetation monitoring from multiple satellite sensors. A large body of work exists on methodologies and algorithms for continental-to-global vegetation monitoring through the use of derived metrics and indices that characterize and/ or provide cues to vegetation photosynthetic activity. Legacy optical passive sensors like the MODIS Aqua/Terra, AVHRR and the Landsat have been providing vital information on mapping extents of vegetation activity and dynamics at a global scale, and the use of such information will always be critical in bridging the gap between vegetation-climate feedbacks and in quantifying the net carbon flux for future climate warming scenarios. This Special Issue is focused on advancing the knowledge base in remote sensing techniques for vegetation structure and dynamics and its application to a wide variety of pressing topics like carbon sequestration, forest degradation and afforestation, vegetation-climate feedbacks, crop production, etc.

We would like to invite you to submit articles about your recent research with respect to the following topics.

  • Optical Remote Sensing of vegetation structure (e.g., LAI/ FPAR, Canopy Height): Methods and evaluations and future missions (e.g., Sentinel-2)
  • Remote Sensing of vegetation dynamics: Methods and evaluations.
  • Lidar Remote Sensing of vegetation structure: Methods and evaluations and future missions (e.g., ICESat-2).
  • Radar Remote Sensing of vegetation structure: Methods and evaluations.
  • Very High Resolution Remote Sensing of vegetation structure (e.g., Worldview, NAIP, High Resolution Airborne Lidar, etc.): Methods and evaluations.
  • Application of new sensors/algorithms to biomass/carbon dynamics estimation.
  • Remote Sensing of crop yield and crop growth monitoring.
  • Revisiting known trends in vegetation growth (e.g., northern hemisphere trends in vegetation, seasonality in Amazonian rainforests, etc.): Continental-to-global scales.
  • Comparison and evaluation of different remote sensing methods.
  • Improvement and evaluation of input data needed for the retrieval of vegetation structural parameters (e.g. canopy height and biomass).
  • Remote Sensing of forest disturbance, degradation and regrowth.
  • Multi-sensor data fusion for long-term vegetation monitoring (e.g., AVHRR-to-MODIS-to-Landsat, Landsat-to-Sentinel-2, etc.).
  • Review articles covering one or more of these topics are also welcome.

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Sangram Ganguly
Dr. Compton Tucker
Guest Editors

Manuscript Submission Information

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. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind 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). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (38 papers)

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Research

Open AccessArticle Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data
Remote Sens. 2016, 8(11), 961; doi:10.3390/rs8110961
Received: 27 June 2016 / Revised: 4 November 2016 / Accepted: 7 November 2016 / Published: 19 November 2016
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Abstract
Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC),
[...] Read more.
Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%–12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%–33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements
Remote Sens. 2016, 8(11), 895; doi:10.3390/rs8110895
Received: 15 April 2016 / Revised: 18 October 2016 / Accepted: 24 October 2016 / Published: 29 October 2016
Cited by 1 | PDF Full-text (6636 KB) | HTML Full-text | XML Full-text
Abstract
A global, monthly averaged time series of Sun-induced Fluorescence (SiF), spanning January 2007 to June 2015, was derived from Metop-A Global Ozone Monitoring Experiment 2 (GOME-2) spectral measurements. Far-red SiF was retrieved using the filling-in of deep solar Fraunhofer lines and atmospheric absorption
[...] Read more.
A global, monthly averaged time series of Sun-induced Fluorescence (SiF), spanning January 2007 to June 2015, was derived from Metop-A Global Ozone Monitoring Experiment 2 (GOME-2) spectral measurements. Far-red SiF was retrieved using the filling-in of deep solar Fraunhofer lines and atmospheric absorption bands based on the general methodology described by Joiner et al, AMT, 2013. A Principal Component (PC) analysis of spectra over non-vegetated areas was performed to describe the effects of atmospheric absorption. Our implementation (SiF KNMI) is an independent algorithm and differs from the latest implementation of Joiner et al, AMT, 2013 (SiF NASA, v26), because we used desert reference areas for determining PCs (as opposed to cloudy ocean and some desert) and a wider fit window that covers water vapour and oxygen absorption bands (as opposed to only Fraunhofer lines). As a consequence, more PCs were needed (35 as opposed to 12). The two time series (SiF KNMI and SiF NASA, v26) correlate well (overall R of 0.78) except for tropical rain forests. Sensitivity experiments suggest the strong impact of the water vapour absorption band on retrieved SiF values. Furthermore, we evaluated the SiF time series with Gross Primary Productivity (GPP) derived from twelve flux towers in Australia. Correlations for individual towers range from 0.37 to 0.84. They are particularly high for managed biome types. In the de-seasonalized Australian SiF time series, the break of the Millennium Drought during local summer of 2010/2011 is clearly observed. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements
Remote Sens. 2016, 8(10), 872; doi:10.3390/rs8100872
Received: 28 April 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 22 October 2016
PDF Full-text (3971 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass
[...] Read more.
Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI–biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Dynamics of Fractional Vegetation Coverage and Its Relationship with Climate and Human Activities in Inner Mongolia, China
Remote Sens. 2016, 8(9), 776; doi:10.3390/rs8090776
Received: 28 June 2016 / Revised: 13 September 2016 / Accepted: 15 September 2016 / Published: 20 September 2016
Cited by 1 | PDF Full-text (5218 KB) | HTML Full-text | XML Full-text
Abstract
Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity
[...] Read more.
Long-term remote sensing normalized difference vegetation index (NDVI) datasets have been widely used in monitoring vegetation changes. In this study, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset was used as the data source, and the dimidiate pixel model, intensity analysis, and residual analysis were used to analyze the changes of vegetation coverage in Inner Mongolia—from 1982 to 2010—and their relationships with climate and human activities. This study also explored vegetation changes in Inner Mongolia with respect to natural factors and human activities. The results showed that the estimated vegetation coverage exhibited a high correlation (0.836) with the actual measured values. The increased vegetation coverage area (49.2% of the total area) was larger than the decreased area (43.3%) from the 1980s to the 1990s, whereas the decreased area (57.1%) was larger than the increased area (35.6%) from the 1990s to the early 21st century. This finding indicates that vegetation growth in the 1990s was better than that in the other two decades. Intensity analysis revealed that changes in the average annual rate from the 1990s to the early 21st century were relatively faster than those in the 1980s–1990s. During the 1980s–1990s, the gain of high vegetation coverage areas was active, and the loss was dormant; in contrast, the gain and loss of low vegetation coverage areas were both dormant. In the 1990s to the early 21st century, the gains of high and low vegetation coverage areas were both dormant, whereas the losses were active. During the study period, areas of low vegetation coverage were converted into ones with higher coverage, and areas of high vegetation coverage were converted into ones with lower coverage. The vegetation coverage exhibited a good correlation (R2 = 0.60) with precipitation, and the positively correlated area was larger than the negatively correlated area. Human activities not only promote the vegetation coverage, but also have a destructive effect on vegetation, and the promotion effect during 1982 to 2000 was larger than from 2001 to 2010, while, the destructive effect was larger from 2000 to 2010. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR
Remote Sens. 2016, 8(9), 766; doi:10.3390/rs8090766
Received: 28 April 2016 / Accepted: 12 September 2016 / Published: 17 September 2016
Cited by 1 | PDF Full-text (3057 KB) | HTML Full-text | XML Full-text
Abstract
Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical
[...] Read more.
Forests historically associated with frequent fire have changed dramatically due to fire suppression and past harvesting over the last century. The buildup of ladder fuels, which carry fire from the surface of the forest floor to tree crowns, is one of the critical changes, and it has contributed to uncharacteristically large and severe fires. The abundance of ladder fuels makes it difficult to return these forests to their natural fire regime or to meet management objectives. Despite the importance of ladder fuels, methods for quantifying them are limited and imprecise. LiDAR (Light Detection and Ranging), a form of active remote sensing, is able to estimate many aspects of forest structure across a landscape. This study investigates a new method for quantifying ladder fuel in the field (using photographs with a calibration banner) and remotely (using LiDAR data). We apply these new techniques in the Klamath Mountains of Northern California to predict ladder fuel levels across the study area. Our results demonstrate a new utility of LiDAR data to identify fire hazard and areas in need of fuels reduction. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield
Remote Sens. 2016, 8(9), 747; doi:10.3390/rs8090747
Received: 1 July 2016 / Revised: 16 August 2016 / Accepted: 30 August 2016 / Published: 9 September 2016
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Abstract
Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping.
[...] Read more.
Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km 2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets
Remote Sens. 2016, 8(9), 733; doi:10.3390/rs8090733
Received: 30 April 2016 / Revised: 12 August 2016 / Accepted: 29 August 2016 / Published: 6 September 2016
Cited by 2 | PDF Full-text (58537 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM)
[...] Read more.
Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June–August) of 2012 from 48 1 m × 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m × 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy ( R 2 = 0.75 , R M S E = 9.94 ) than the single image upscaling ( R 2 = 0.63 , R M S E = 12.05 ) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model ( R 2 = 0.61 , R M S E = 13.78 ) than only using the original 48 plots for the phenology model ( R 2 = 0.23 , R M S E = 17.49 ). We believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data
Remote Sens. 2016, 8(9), 700; doi:10.3390/rs8090700
Received: 31 May 2016 / Revised: 28 July 2016 / Accepted: 15 August 2016 / Published: 25 August 2016
Cited by 3 | PDF Full-text (1506 KB) | HTML Full-text | XML Full-text
Abstract
In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest
[...] Read more.
In this study, four models describing the interferometric coherence of the forest vegetation layer are proposed and compared with the TanDEM-X data. Our focus is on developing tools for hemiboreal forest height estimation from single-pol interferometric SAR measurements, suitable for wide area forest mapping with limited a priori information. The multi-temporal set of 19 TanDEM-X interferometric pairs and the 90th percentile forest height maps are derived from Airborne LiDAR Scanning (ALS), covering an area of 2211 ha of forests over Estonia. Three semi-empirical models along with the Random Volume over Ground (RVoG) model are examined for applicable parameter ranges and model performance under various conditions for over 3000 forest stands. This study shows that all four models performed well in describing the relationship between forest height and interferometric coherence. Use of an advanced model with multiple parameters is not always justified when modeling the volume decorrelation in the boreal and hemiboreal forests. The proposed set of semi-empirical models, show higher robustness compared to a more advanced RVoG model under a range of seasonal and environmental conditions during data acquisition. We also examine the dynamic range of parameters that different models can take and propose optimal conditions for forest stand height inversion for operationally-feasible scenarios. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?
Remote Sens. 2016, 8(8), 668; doi:10.3390/rs8080668
Received: 23 April 2016 / Revised: 26 July 2016 / Accepted: 10 August 2016 / Published: 18 August 2016
Cited by 4 | PDF Full-text (5703 KB) | HTML Full-text | XML Full-text
Abstract
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed
[...] Read more.
Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory
Remote Sens. 2016, 8(8), 653; doi:10.3390/rs8080653
Received: 30 April 2016 / Revised: 5 August 2016 / Accepted: 8 August 2016 / Published: 12 August 2016
Cited by 1 | PDF Full-text (15991 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and
[...] Read more.
The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree- or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO2 stocks and changes to comply with the UN-REDD policies. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods
Remote Sens. 2016, 8(8), 632; doi:10.3390/rs8080632
Received: 17 May 2016 / Revised: 20 July 2016 / Accepted: 27 July 2016 / Published: 30 July 2016
Cited by 1 | PDF Full-text (5259 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was
[...] Read more.
Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m2/m2 and 0.23 m2/m2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m2/m2), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m2/m2 and 0.35 m2/m2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m2/m2 to 0.28 m2/m2 for PLSR and from 0.33 m2/m2 to 0.28 m2/m2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m2/m2 to 0.32 m2/m2). Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Rapid Assessments of Amazon Forest Structure and Biomass Using Small Unmanned Aerial Systems
Remote Sens. 2016, 8(8), 615; doi:10.3390/rs8080615
Received: 14 May 2016 / Revised: 15 July 2016 / Accepted: 21 July 2016 / Published: 25 July 2016
Cited by 7 | PDF Full-text (6589 KB) | HTML Full-text | XML Full-text
Abstract
Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive
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Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive forest plot networks to estimate aboveground carbon density (ACD). Manned aircraft are only cost-effective when applied to large areas (>100,000 ha), while plot networks are most effective for total C stock estimation across large areas, not for quantifying spatially-explicit variation. We sought to develop an effective method for frequent and accurate ACD estimation at intermediate scales (100–100,000 ha) that would be sensitive to small-scale disturbance. Using small UAVs, we collected imagery of 516 ha of lowland forest in the Peruvian Amazon. We then used a structure-from-motion (SFM) approach to create a 3D model of forest canopy. Comparing SFM- and airborne Light Detection and Ranging (LiDAR)-derived estimates of canopy height and ACD, we found that SFM estimates of top-of-canopy height (TCH) and ACD were highly correlated with previous LiDAR estimates (r = 0.86–0.93 and r = 0.73–0.94 for TCH and ACD, respectively, at 0.1–4 ha grain sizes), with r = 0.92 for ACD determination at the 1 ha scale, despite SFM and LiDAR measurements being separated by two years in a dynamic forest. SFM and LiDAR estimates of mean TCH and mean ACD were highly similar, differing by only 0.4% and 0.04%, respectively, within mature forest. The technique allows inexpensive, near-real-time monitoring of ACD for ecological studies, payment for ecosystem services (PES) ventures, such as reducing emissions from deforestation and forest degradation (REDD+), forestry enterprises, and governance. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Airborne S-Band SAR for Forest Biophysical Retrieval in Temperate Mixed Forests of the UK
Remote Sens. 2016, 8(7), 609; doi:10.3390/rs8070609
Received: 19 April 2016 / Revised: 4 July 2016 / Accepted: 19 July 2016 / Published: 20 July 2016
Cited by 2 | PDF Full-text (3595 KB) | HTML Full-text | XML Full-text
Abstract
Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due
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Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6–23 m), diameter at breast-height (7–42 cm), basal area (0.2–56 m2/ha), stem density (20–350 trees/ha) and woody biomass density (31–520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r2) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle range. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images
Remote Sens. 2016, 8(7), 610; doi:10.3390/rs8070610
Received: 22 April 2016 / Revised: 29 June 2016 / Accepted: 13 July 2016 / Published: 20 July 2016
Cited by 1 | PDF Full-text (4944 KB) | HTML Full-text | XML Full-text
Abstract
Knowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end
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Knowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end of the campaign and only providing results for some key states. In this work, a new dynamical framework is proposed to provide real-time estimates in a continuous range of states, for which NDVI images are combined with a prediction model in an optimal way using a particle filter. The methodology is tested over a set of 8 to 13 rice parcels during 2008–2013, achieving a high determination factor R 2 = 0.93 ( n = 379 ) for the complete phenological range. This method is also used to predict the end of season date, obtaining a high accuracy with an anticipation of around 40–60 days. Among the key advantages of this approach, phenology is estimated each time a new observation is available, hence enabling the potential detection of anomalies in real-time during the cultivation. In addition, the estimation procedure is robust in the case of noisy observations, and it is not limited to a few phenological stages. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data
Remote Sens. 2016, 8(7), 595; doi:10.3390/rs8070595
Received: 23 May 2016 / Revised: 28 June 2016 / Accepted: 7 July 2016 / Published: 13 July 2016
Cited by 4 | PDF Full-text (8984 KB) | HTML Full-text | XML Full-text
Abstract
Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially
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Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986–2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007–2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R2 of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar
Remote Sens. 2016, 8(7), 577; doi:10.3390/rs8070577
Received: 11 April 2016 / Revised: 29 June 2016 / Accepted: 4 July 2016 / Published: 8 July 2016
Cited by 1 | PDF Full-text (3873 KB) | HTML Full-text | XML Full-text
Abstract
Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover
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Assessments of forest cover, forest carbon stocks and carbon emissions from deforestation and degradation are increasingly important components of sustainable resource management, for combating biodiversity loss and in climate mitigation policies. Satellite remote sensing provides the only means for mapping global forest cover regularly. However, forest classification with optical data is limited by its insensitivity to three-dimensional canopy structure and cloud cover obscuring many forest regions. Synthetic Aperture Radar (SAR) sensors are increasingly being used to mitigate these problems, mainly in the L-, C- and X-band domains of the electromagnetic spectrum. S-band has not been systematically studied for this purpose. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest characterisation. The Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model is utilised to understand the scattering mechanisms in forest canopies at S-band. The MIMICS-I model reveals strong S-band backscatter sensitivity to the forest canopy in comparison to soil characteristics across all polarisations and incidence angles. Airborne S-band SAR imagery over the temperate mixed forest of Savernake Forest in southern England is analysed for its information content. Based on the modelling results, S-band HH- and VV-polarisation radar backscatter and the Radar Forest Degradation Index (RFDI) are used in a forest/non-forest Maximum Likelihood classification at a spatial resolution of 6 m (70% overall accuracy, κ = 0.41) and 20 m (63% overall accuracy, κ = 0.27). The conclusion is that S-band SAR such as from NovaSAR-S is likely to be suitable for monitoring forest cover and its changes. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Analyses of Impact of Needle Surface Properties on Estimation of Needle Absorption Spectrum: Case Study with Coniferous Needle and Shoot Samples
Remote Sens. 2016, 8(7), 563; doi:10.3390/rs8070563
Received: 15 April 2016 / Revised: 22 June 2016 / Accepted: 29 June 2016 / Published: 2 July 2016
Cited by 4 | PDF Full-text (6373 KB) | HTML Full-text | XML Full-text
Abstract
Leaf scattering spectrum is the key optical variable that conveys information about leaf absorbing constituents from remote sensing. It cannot be directly measured from space because the radiation scattered from leaves is affected by the 3D canopy structure. In addition, some radiation is
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Leaf scattering spectrum is the key optical variable that conveys information about leaf absorbing constituents from remote sensing. It cannot be directly measured from space because the radiation scattered from leaves is affected by the 3D canopy structure. In addition, some radiation is specularly reflected at the surface of leaves. This portion of reflected radiation is partly polarized, does not interact with pigments inside the leaf and therefore contains no information about its interior. Very little empirical data are available on the spectral and angular scattering properties of leaf surfaces. Whereas canopy-structure effects are well understood, the impact of the leaf surface reflectance on estimation of leaf absorption spectra remains uncertain. This paper presents empirical and theoretical analyses of angular, spectral, and polarimetric measurements of light reflected by needles and shoots of Pinus koraiensis and Picea koraiensis species. Our results suggest that ignoring the leaf surface reflected radiation can result in an inaccurate estimation of the leaf absorption spectrum. Polarization measurements may be useful to account for leaf surface effects because radiation reflected from the leaf surface is partly polarized, whereas that from the leaf interior is not. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?
Remote Sens. 2016, 8(7), 540; doi:10.3390/rs8070540
Received: 22 March 2016 / Revised: 13 June 2016 / Accepted: 14 June 2016 / Published: 24 June 2016
Cited by 4 | PDF Full-text (10673 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this
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Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data. Model performance was evaluated based on cross-validation bias, standard deviation and Root Mean Square Error (RMSE) at the logarithmic and non-logarithmic scales. Both models achieved rather limited performances in wood volume prediction. Nonetheless, model performance increased with red edge indices and texture attributes, which shows that they play an important role in semi-arid regions with sparse vegetation. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs
Remote Sens. 2016, 8(7), 474; doi:10.3390/rs8070474
Received: 4 April 2016 / Revised: 13 May 2016 / Accepted: 23 May 2016 / Published: 23 June 2016
PDF Full-text (9910 KB) | HTML Full-text | XML Full-text
Abstract
The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation
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The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests
Remote Sens. 2016, 8(6), 522; doi:10.3390/rs8060522
Received: 1 May 2016 / Revised: 13 June 2016 / Accepted: 17 June 2016 / Published: 22 June 2016
Cited by 5 | PDF Full-text (2462 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements,
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Synthetic Aperture Radar (SAR) backscatter measurements are sensitive to forest aboveground biomass (AGB), and the observations from space can be used for mapping AGB globally. However, the radar sensitivity saturates at higher AGB values depending on the wavelength and geometry of radar measurements, and is influenced by the structure of the forest and environmental conditions. Here, we examine the sensitivity of SAR at the L-band frequency (~25 cm wavelength) to AGB in order to examine the performance of future joint National Aeronautics and Space Administration, Indian Space Research Organisation NASA-ISRO SAR mission in mapping the AGB of global forests. For SAR data, we use the Phased Array L-Band SAR (PALSAR) backscatter from the Advanced Land Observing Satellite (ALOS) aggregated at a 100-m spatial resolution; and for AGB data, we use more than three million AGB values derived from the Geoscience Laser Altimeter System (GLAS) LiDAR height metrics at about 0.16–0.25 ha footprints across eleven different forest types globally. The results from statistical analysis show that, over all eleven forest types, saturation level of L-band radar at HV polarization on average remains ≥100 Mg·ha−1. Fresh water swamp forests have the lowest saturation with AGB at ~80 Mg·ha−1, while needleleaf forests have the highest saturation at ~250 Mg·ha−1. Swamp forests show a strong backscatter from the vegetation-surface specular reflection due to inundation that requires to be treated separately from those on terra firme. Our results demonstrate that L-Band backscatter relations to AGB can be significantly different depending on forest types and environmental effects, requiring multiple algorithms to map AGB from time series of satellite radar observations globally. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Abiotic Controls on Macroscale Variations of Humid Tropical Forest Height
Remote Sens. 2016, 8(6), 494; doi:10.3390/rs8060494
Received: 31 March 2016 / Revised: 29 May 2016 / Accepted: 7 June 2016 / Published: 14 June 2016
Cited by 2 | PDF Full-text (8046 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic
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Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Evaluation of MODIS LAI/FPAR Product Collection 6. Part 2: Validation and Intercomparison
Remote Sens. 2016, 8(6), 460; doi:10.3390/rs8060460
Received: 4 April 2016 / Revised: 19 May 2016 / Accepted: 25 May 2016 / Published: 30 May 2016
Cited by 7 | PDF Full-text (13833 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables.
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The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the 2001–2004 period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements
Remote Sens. 2016, 8(5), 359; doi:10.3390/rs8050359
Received: 7 March 2016 / Revised: 13 April 2016 / Accepted: 20 April 2016 / Published: 26 April 2016
Cited by 12 | PDF Full-text (4901 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of
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As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Discriminating between Native Norway Spruce and Invasive Sitka Spruce—A Comparison of Multitemporal Landsat 8 Imagery, Aerial Images and Airborne Laser Scanner Data
Remote Sens. 2016, 8(5), 363; doi:10.3390/rs8050363
Received: 19 January 2016 / Revised: 15 April 2016 / Accepted: 20 April 2016 / Published: 26 April 2016
Cited by 4 | PDF Full-text (844 KB) | HTML Full-text | XML Full-text
Abstract
Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using
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Invasive species can be considered a threat to biodiversity, and remote sensing has been proposed as a tool for detection and monitoring of invasive species. In this study, we test the ability to discriminate between two tree species of the same genera, using data from Landsat 8 satellite imagery, aerial images, and airborne laser scanning. Ground observations from forest stands dominated by either Norway spruce (Picea abies) or Sitka spruce (Picea sitchensis) were coupled with variables derived from each of the three sets of remote sensing data. Random forest, support vector machine, and logistic regression classification models were fit to the data, and the classification accuracy tested by performing a cross-validation. Classification accuracies were compared for different combinations of remote sensing data and classification methods. The overall classification accuracy varied from 0.53 to 0.79, with the highest accuracy obtained using logistic regression with a combination of data derived from Landsat imagery and aerial images. The corresponding kappa value was 0.58. The contribution to the classification accuracy from using airborne data in addition to Landsat imagery was not substantial in this study. The classification accuracy varied between models using data from individual Landsat images. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Amazon Forests’ Response to Droughts: A Perspective from the MAIAC Product
Remote Sens. 2016, 8(4), 356; doi:10.3390/rs8040356
Received: 4 February 2016 / Revised: 13 April 2016 / Accepted: 20 April 2016 / Published: 23 April 2016
Cited by 3 | PDF Full-text (8508 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Amazon forests experienced two severe droughts at the beginning of the 21st century: one in 2005 and the other in 2010. How Amazon forests responded to these droughts is critical for the future of the Earth’s climate system. It is only possible to
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Amazon forests experienced two severe droughts at the beginning of the 21st century: one in 2005 and the other in 2010. How Amazon forests responded to these droughts is critical for the future of the Earth’s climate system. It is only possible to assess Amazon forests’ response to the droughts in large areal extent through satellite remote sensing. Here, we used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) data to assess Amazon forests’ response to droughts, and compared the results with those from the standard (Collection 5 and Collection 6) MODIS VI data. Overall, the MAIAC data reveal more realistic Amazon forests inter-annual greenness dynamics than the standard MODIS data. Our results from the MAIAC data suggest that: (1) the droughts decreased the greenness (i.e., photosynthetic activity) of Amazon forests; (2) the Amazon wet season precipitation reduction induced by El Niño events could also lead to reduced photosynthetic activity of Amazon forests; and (3) in the subsequent year after the water stresses, the greenness of Amazon forests recovered from the preceding decreases. However, as previous research shows droughts cause Amazon forests to reduce investment in tissue maintenance and defense, it is not clear whether the photosynthesis of Amazon forests will continue to recover after future water stresses, because of the accumulated damages caused by the droughts. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
Remote Sens. 2016, 8(4), 291; doi:10.3390/rs8040291
Received: 14 December 2015 / Revised: 14 March 2016 / Accepted: 21 March 2016 / Published: 29 March 2016
Cited by 5 | PDF Full-text (7472 KB) | HTML Full-text | XML Full-text
Abstract
Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground
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Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Assessing the Impact of Climate Variability on Cropland Productivity in the Canadian Prairies Using Time Series MODIS FAPAR
Remote Sens. 2016, 8(4), 281; doi:10.3390/rs8040281
Received: 4 January 2016 / Revised: 12 March 2016 / Accepted: 21 March 2016 / Published: 28 March 2016
Cited by 1 | PDF Full-text (10247 KB) | HTML Full-text | XML Full-text
Abstract
Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland
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Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland productivity in the Canadian Prairies between 2000 and 2013 based on time series of MODIS (Moderate Resolution Imaging Spectroradiometer) FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) product. Key phenological metrics, including the start (SOS) and end of growing season (EOS), and the cumulative FAPAR (CFAPAR) during the growing season (between SOS and EOS), were extracted and calculated from the FAPAR time series with the Parametric Double Hyperbolic Tangent (PDHT) method. The Mann-Kendall test was employed to assess the trends of cropland productivity and climatic variables, and partial correlation analysis was conducted to explore the potential links between climate variability and cropland productivity. An assessment using crop yield statistical data showed that CFAPAR can be taken as a surrogate of cropland productivity in the Canadian Prairies. Cropland productivity showed an increasing trend in most areas of Canadian Prairies, in general, during the period from 2000 to 2013. Interannual variability in cropland productivity on the Canadian Prairies was influenced positively by rainfall variation and negatively by mean air temperature. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements
Remote Sens. 2016, 8(3), 260; doi:10.3390/rs8030260
Received: 8 January 2016 / Revised: 12 March 2016 / Accepted: 14 March 2016 / Published: 19 March 2016
Cited by 4 | PDF Full-text (3321 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE)
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This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana
Remote Sens. 2016, 8(3), 240; doi:10.3390/rs8030240
Received: 3 November 2015 / Revised: 3 February 2016 / Accepted: 4 March 2016 / Published: 16 March 2016
Cited by 5 | PDF Full-text (7832 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne
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LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory
Remote Sens. 2016, 8(3), 197; doi:10.3390/rs8030197
Received: 2 December 2015 / Revised: 24 January 2016 / Accepted: 22 February 2016 / Published: 27 February 2016
Cited by 1 | PDF Full-text (5050 KB) | HTML Full-text | XML Full-text
Abstract
The scaling effect correction of retrieved parameters is an essential and difficult issue in analysis and application of remote sensing information. Based on fractal theory, this paper developed a scaling transfer model to correct the scaling effect of the leaf area index (LAI)
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The scaling effect correction of retrieved parameters is an essential and difficult issue in analysis and application of remote sensing information. Based on fractal theory, this paper developed a scaling transfer model to correct the scaling effect of the leaf area index (LAI) estimated from coarse spatial resolution image. As the key parameter of the proposed model, the information fractal dimension (D) of the up-scaling pixel was calculated by establishing the double logarithmic linear relationship between D-2 and the normalized difference vegetation index (NDVI) standard deviation (σNDVI) of the up-scaling pixel. Based on the calculated D and the fractal relationship between the exact LAI and the approximated LAI estimated from the coarse resolution pixel, a LAI scaling transfer model was established. Finally, the model accuracy in correcting the scaling effect was discussed. Results indicated that the D increases with increasing σNDVI, and the D-2 was highly linearly correlated with σNDVI on the double logarithmic coordinate axis. The scaling transfer model corrected the scaling effect of LAI with a maximum value of root-mean-square error (RMSE) of 0.011. The maximum absolute correction error (ACE) and relative correction error (RCE) were only 0.108% and 8.56%, respectively. The spatial heterogeneity was the primary cause resulting in the scaling effect and the key influencing factor of correction effect. The results indicated that the developed method based on fractal theory could effectively correct the scaling effect of LAI estimated from the heterogeneous pixels. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Global and Regional Variability and Change in Terrestrial Ecosystems Net Primary Production and NDVI: A Model-Data Comparison
Remote Sens. 2016, 8(3), 177; doi:10.3390/rs8030177
Received: 16 November 2015 / Revised: 27 January 2016 / Accepted: 14 February 2016 / Published: 25 February 2016
Cited by 3 | PDF Full-text (2519 KB) | HTML Full-text | XML Full-text
Abstract
The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model-based NPP estimates (from five models of the TRENDY project) for the
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The net primary productivity (NPP) is commonly used for understanding the dynamics of terrestrial ecosystems and their role in carbon cycle. We used a combination of the most recent NDVI and model-based NPP estimates (from five models of the TRENDY project) for the period 1982–2012, to study the role of terrestrial ecosystems in carbon cycle under the prevailing climate conditions. We found that 80% and 67% of the global land area showed positive NPP and NDVI values, respectively, for this period. The global NPP was estimated to be about 63 Pg C·y−1, with an increase of 0.214 Pg C·y−1·y−1. Similarly, the global mean NDVI was estimated to be 0.33, with an increasing trend of 0.00041 y−1. The spatial patterns of NPP and NDVI demonstrated substantial variability, especially at the regional level, for most part of the globe. However, on temporal scale, both global NPP and NDVI showed a corresponding pattern of increase (decrease) for the duration of this study except for few years (e.g., 1990 and 1995–1998). Generally, the Northern Hemisphere showed stronger NDVI and NPP increasing trends over time compared to the Southern Hemisphere; however, NDVI showed larger trends in Temperate regions while NPP showed larger trends in Boreal regions. Among the five models, the maximum and minimum NPP were produced by JULES (72.4 Pg C·y−1) and LPJ (53.72 Pg C·y−1) models, respectively. At latitudinal level, the NDVI and NPP ranges were ~0.035 y−1 to ~−0.016 y−1 and ~0.10 Pg C·y−1·y−1 to ~−0.047 Pg C·y−1·y−1, respectively. Overall, the results of this study suggest that the modeled NPP generally correspond to the NDVI trends in the temporal dimension. The significant variability in spatial patterns of NPP and NDVI trends points to a need for research to understand the causes of these discrepancies between molded and observed ecosystem dynamics, and the carbon cycle. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images
Remote Sens. 2016, 8(2), 125; doi:10.3390/rs8020125
Received: 20 October 2015 / Revised: 14 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
Cited by 1 | PDF Full-text (4820 KB) | HTML Full-text | XML Full-text
Abstract
Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning.
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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery
Remote Sens. 2016, 8(1), 62; doi:10.3390/rs8010062
Received: 27 November 2015 / Revised: 5 January 2016 / Accepted: 8 January 2016 / Published: 14 January 2016
Cited by 2 | PDF Full-text (4045 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover
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Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Tropical Forests of Réunion Island Classified from Airborne Full-Waveform LiDAR Measurements
Remote Sens. 2016, 8(1), 43; doi:10.3390/rs8010043
Received: 3 December 2015 / Revised: 27 December 2015 / Accepted: 29 December 2015 / Published: 7 January 2016
Cited by 1 | PDF Full-text (7078 KB) | HTML Full-text | XML Full-text
Abstract
From an unprecedented experiment using airborne measurements performed over the rich forests of Réunion Island, this paper aims to present a methodology for the classification of diverse tropical forest biomes as retrieved from vertical profiles measured using a full-waveform LiDAR. This objective is
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From an unprecedented experiment using airborne measurements performed over the rich forests of Réunion Island, this paper aims to present a methodology for the classification of diverse tropical forest biomes as retrieved from vertical profiles measured using a full-waveform LiDAR. This objective is met through the retrieval of both the canopy height and the Leaf Area Index (LAI), obtained as an integral of the foliage profile. The campaign involved sites ranging from coastal to rain forest, including tropical montane cloud forest, as found on the Bélouve plateau. The mean values of estimated LAI retrieved from the apparent foliage profile are between ~5 and 8 m2/m2, and the mean canopy height values are ~15 m for both tropical montane cloud and rain forests. Good agreement is found between LiDAR- and MODIS-derived LAI for moderate LAI (~5 m2/m2), but the LAI retrieved from LiDAR is larger than MODIS on thick rain forest sites (~8 against ~6 m2/m2 from MODIS). Regarding the characterization of tropical forest biomes, we show that the rain and montane tropical forests can be well distinguished from planted forests by the use of the parameters directly retrieved from LiDAR measurements. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China
Remote Sens. 2016, 8(1), 29; doi:10.3390/rs8010029
Received: 10 November 2015 / Revised: 17 December 2015 / Accepted: 25 December 2015 / Published: 4 January 2016
Cited by 3 | PDF Full-text (3539 KB) | HTML Full-text | XML Full-text
Abstract
Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The
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Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Distinguishing Early Successional Plant Communities Using Ground-Level Hyperspectral Data
Remote Sens. 2015, 7(12), 16588-16606; doi:10.3390/rs71215850
Received: 25 September 2015 / Revised: 24 November 2015 / Accepted: 27 November 2015 / Published: 8 December 2015
Cited by 3 | PDF Full-text (18420 KB) | HTML Full-text | XML Full-text
Abstract
Abandoned agricultural fields have recently become more abundant in the U.S. and remain susceptible to species invasions after cultivation disturbance. As invasive species become more widespread with increases in anthropogenic activities, we need more effective ways to use limited resources for conservation of
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Abandoned agricultural fields have recently become more abundant in the U.S. and remain susceptible to species invasions after cultivation disturbance. As invasive species become more widespread with increases in anthropogenic activities, we need more effective ways to use limited resources for conservation of native ecosystems. Remote sensing can help us monitor the spread and effects of invasive species, and thus determine the species and locations to target for conservation. To examine this potential, we studied plant communities dominated by exotic invasive plant species in secondary successional fields in northern Virginia using ground-level hyperspectral data. Within these communities, ordination analyses of vegetation surveys revealed differences in species compositions among plots and fields. These differences among communities were also observed in the spectral data. Stepwise multiple linear regression analyses to determine which species influenced the ordination axes revealed that many of the influential species are considered invasive, again underscoring the influence of invasive species on community properties. Stepwise regression analyses also revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from the visible to the near-infrared regions. A discriminant analysis using wavelengths selected with a principal components analysis demonstrated that different plant communities were separable using spectral data. These spectrally observable differences suggest that we can use hyperspectral data to distinguish among invasive-dominated successional plant communities in this region. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data
Remote Sens. 2015, 7(12), 16315-16338; doi:10.3390/rs71215830
Received: 7 October 2015 / Revised: 23 November 2015 / Accepted: 27 November 2015 / Published: 3 December 2015
Cited by 2 | PDF Full-text (5636 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the
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Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R2 = 0.81, rRMSE = 10%), basal area (R2 = 0.68, rRMSE = 20%), and timber volume (R2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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Open AccessArticle Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data
Remote Sens. 2015, 7(12), 15917-15932; doi:10.3390/rs71215811
Received: 5 October 2015 / Revised: 4 November 2015 / Accepted: 19 November 2015 / Published: 27 November 2015
Cited by 6 | PDF Full-text (2358 KB) | HTML Full-text | XML Full-text
Abstract
Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used
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Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications. Full article
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
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