8 research outputs found
Assessing the performance of random forest regression for estimating canopy height in tropical dry forests
Accurate estimation of forest canopy height is essential for monitoring forest ecosystems and assessing their carbon storage potential. This study evaluates the effectiveness of different remote sensing techniques for estimating forest canopy height in tropical dry forests. Using field data and remote sensing data from airborne lidar and polarimetric synthetic aperture radar (SAR), a random forest (RF) model was developed to estimate canopy height based on different indices. Results show that the normalize difference build-up index (NDBI) has the highest correlation with canopy height, outperforming other indices such as relative vigor index (RVI) and polarimetric vertical and horizontal variables. The RF model with NDBI as input showed a good fit and predictive ability, with low concentration of errors around 0. These findings suggest that NDBI can be a useful tool for accurately estimating forest canopy height in tropical dry forests using remote sensing techniques, providing valuable information for forest management and conservation efforts
Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area
Digital surface model (DSM) has been widely available for mapping and was also sometimes used for mapping vegetation height. The authors conducted a preliminary study to evaluate the potential use of DSMs derived from ASTER, ALOS, and SRTM for estimating vegetation cover density in mountainous area. Â This study used NDVI and SAVI vegetation indices, in addition to forest cover density (FCD) model as references for evaluation. Â A DSM-based volume index (Volindex) concept is introduced, which is the product of the canopy height model (CHM) and the pixel area. CHM was derived from the value difference between the DSM and the reference DEM. The Volindex model was then compared with the NDVI, SAVI and FCD. Â We found that all DSM-based Volindex models are not accurate enough to represent the vegetation cover density, although the ALOS Palsar-based Volindex could reach 41.53% accuracy and was finally used to predict the vegetation cover density
Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion
The fusion of microwave and optical data sets is expected to provide great potential for the
derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating
in twin mode, they can provide an unprecedented data source to build dense spatial and temporal
high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of
the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two
highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open
savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single
time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via
machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa
and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation
(CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of
87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave
infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed
Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic
aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover.
In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover
predictions in open savanna-like environments with heterogeneous regional features. The presented
approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high
spatial resolution
Potential of multi-temporal ALOS-2 PALSAR-2 ScanSAR data for vegetation height estimation in tropical forests of Mexico
Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic modelâs predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the modelâs predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter
Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico
Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
The coastal zone offers among the worldâs most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of landâ and waterârelated applications in coastal zones. Compared to optical satellites, cloudâcover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have allâweather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloudâprone tropical and subâtropical climates. The canopy penetration capability with long radar wavelength enables Lâband SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban
sprawl and climate changeâinduced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne Lâband SAR data for geoscientific
analyses that are relevant for coastal land applications
National farm scale estimates of grass yield from satellite remote sensing
Globally, grasslands are an important source of food for livestock and provide additional ecosystem services such as greenhouse gas (GHG) mitigation through carbon sequestration, habitats for biodiversity, and recreational amenities. Grass is the cheapest source of fodder providing Irish farmers with an economic benefit against international competitors. Hence, to maintain profitability, farmers have to maximize the proportion of grazed grass in cowâs diet or save it as silage.
The overall objective of the current research project was to build a machine-learning model to estimate grass growth nationally using earth observation imagery from the Sentinel 2 satellite constellation and ancillary meteorological data, which are known to influence grass growth. Firstly, the impact of meteorological data and Growing Degree Days (GDD) was assessed for Teagasc Moorepark experimental farm (Fermoy, Co Cork, Ireland). GDD was modified to include Soil Moisture Deficit (SMD), which included the impact of summer drought conditions in 2018. Results demonstrated the importance of GDD for grass growth estimation using ordinary linear regression (OLS). The potential evapotranspiration (PE) 0.65 (r=0.65) and evaporation (r=0.65) were equally significant variables in 2017, while in 2018 the solar radiation had the highest correlation (r=0.43), followed by potential evapotranspiration and evaporation with r of 0.42. The standard and modified GDD were equally significant variables with r of 0.65 in 2017, but both had a reduced correlation in 2018 with modified GDD (0.38, p<0.01) performing slightly better than the standard GDD (0.26, p<0.01) calculation. These models only explained 53% (RMSE of 18.90 kg DM ha-1day-1) and 36% (RMSE of 27.02 kg DM ha-1day-1) of variability in grass growth for 2017 and 2018, respectively.
Considering the importance of meteorological data, an empirical grass model called the Brereton model, previously used for Irish grass growing conditions were tested. Since this model lacks a spatial element, we compared the Brereton model with the previously used machine-learning model ANFIS and Random Forest (RF) with the combination of satellite data and meteorological data for eight Teagasc farms. Overall, the machine-learning algorithms (R2= 0.32 to 0.73 and RMSE=14.65 to 24.76 kg DM ha-1day-1 for the test data) performed better than the Brereton model (range of R2=0.03 to 0.33 and RMSE=41.68 to 82.29 kg DM ha-1day-1). The RF model (with all the variables except rainfall) had the highest accuracy for predicting grass growth rate, with (R2= 0.55, RMSE = 14.65 kg DM ha-1day-1, MSE= 214.79 kg DM ha-1day-1 versus ANFIS with R2 = 0.47, RMSE = 15.95 kg DM ha-1day-1, MSE= 254.40 kg DM ha-1day-1).
When developing a national model, meteorological data were missing (except precipitation). A different approach was followed, whereby the grass growing season was subdivided (January-June Agmodel 1 and JulyâDecember Agmodel 2). Phenologically, the peak grass growth in Ireland typically occurs in May, with a slow decline in subsequent months. Spring is the most important season for grassland management, where growing conditions can impact the grass supply for the whole year. The national models were developed using Sentinel 2 band metrics, spectral indices (NDVI and NDRE), and rainfall for 179 farms. Data from 2017-2019 was divided into training and testing data (70:30 split), with 2020 data used for independent validation of the final trained model. Test accuracy was higher for Agmodel 1 (R2 = 0.74, RMSE= 15.52 kg DM ha-1day-1) versus Agmodel 2 (R2 = 0.58, RMSE= 13.74 kg DM ha-1day-1). This trained model was used on validation data from 2020, and the results were similar with better performance for Agmodel1 (R2 =0.70) versus Agmodel2 (R2=0.36). The improved spatial resolution of Sentinel 2 and the availability of red-edge bands showed improved results compared with previous work based on coarse resolution satellite imagery
Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms
Forests are one of the major carbon sinks that significantly contribute towards achieving
targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG)
emissions. In order to contribute to regular National Inventory Reporting, and as part of
the on-going development of the Irish national GHG reporting system (CARBWARE),
improvements in characterisation of changes in forest carbon stocks have been
recommended to provide a comprehensive information flow into CARBWARE. The Irish
National Forest Inventory (NFI) is updated once every six years, thus there is a need for
an enhanced forest monitoring system to obtain annual forest updates to support
government agencies and forest management companies in their strategic decision making
and to comply with international GHG reporting standards. Sustainable forest
management is imperative to promote net carbon absorption from forests. Based on the
NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric
CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become
a net emitter of carbon. Disturbances from human induced activities such as clear felling,
thinning and deforestation results in carbon emissions back into the atmosphere. Funded
by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study
focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR)
satellite based sensors for monitoring changes in the small stand forests of Ireland.
Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2
PALSAR-2 sensors have been used to map forest areas and characterise the different
disturbances observed within three different regions of Ireland. Forest mapping and
disturbance characterisation was achieved by combining the machine learning supervised
Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis
(ISODATA) classification techniques. The lack of availability of ground truth data
supported use of this unsupervised approach which forms natural clusters based on their
multi-temporal signatures, with divergence statistics used to select the optimal number of
clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial
where there is a dearth of ground-based information. When applied to the forests, mapped
with an accuracy of up to 97% by RF, the ISODATA technique successfully identified
the unique multi-temporal pattern associated with clear-fells which exhibited a decrease
of 4 to 5 decibels (dB) between the images acquired before and after the event. The
clustering algorithm effectively highlighted the occurrence of other disturbance events
within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas
of tree growth and afforestation.
A highlight of the work is the successful transferability of the algorithm, developed using
ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential
continuity of annual forest monitoring. The higher spatial and radiometric resolutions of
ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to
ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS
PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images.
Moreover, even with some different backscatter characteristics of images acquired in
different seasons, similar signature patterns between the sensors were retrieved that helped
to define the cluster groups, thus demonstrating the robustness of the algorithm and its
successful transferability.
Having proven the potential to monitor forest disturbances, the results from both the
sensors were used to detect deforestation over the time period 2007-2016. Permanent
land-use changes pertaining to conversion of forests to agricultural lands and windfarms
were identified which are important with respect to forest monitoring and carbon reporting
in Ireland.
Overall, this work has presented a viable approach to support forest monitoring operations
in Ireland. By providing disturbance information from SAR, it can supplement projects
working with optical images which are generally limited by cloud cover, particularly in
parts of northern, western and upland Ireland. This approach adds value to ground based
forest monitoring by mapping distinct forests over large areas on an annual basis. This
study has demonstrated the ability to apply the algorithm to three different study areas,
with a vision to operationalise the algorithm on a national scale. The main limitations
experienced in this study were the lack of L-band SAR data availability and reference
datasets. With typically only one image acquired per year, and discrepancies and
omissions existing within reference datasets, understanding the behaviour of certain
cluster groups representing disturbances was challenging. However, this approach has
addressed some issues within the reference datasets, for example locating areas for which
a felling licence was granted but where trees were never cut, by providing detailed
systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B,
P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited
SAR image acquisitions provided more images per year are available, especially during
the summer months