90 research outputs found
Coupled canopy-atmosphere modelling for radiance-based estimation of vegetation properties
Vegetation is an important component of the Earth’s biosphere and therefore plays a crucial role in the carbon exchange of terrestrial ecosystems. Vegetation variables, such as leaf area index (LAI) and leaf chlorophyll content (Cab), can be monitored at global scale using remote sensing (RS). There are two main categories of approaches for estimating the vegetation variables from RS data: empirical and physically-based approaches. Physically-based approaches are more widely applicable because they rely on radiative transfer (RT) models, which can be adapted to the observation conditions and to the observed vegetation. For estimating the vegetation variables, however, the RT model has to be inverted, and this inversion is usually an ill-posed and under-determined problem. Several regularization methods have been proposed to allow finding stable and unique solutions: model coupling, using multi-angular data, using a priori information, as well as applying spatial or temporal constraints. Traditionally, radiance data measured at top-of the atmosphere (TOA) are pre-processed to top-of-canopy (TOC) reflectances. Corrections for atmospheric effects, and, if needed, for adjacency, directional, or topographic effects are usually applied sequentially and independently. Physically, however, these effects are inter-related, and each correction introduces errors. These errors propagate to the TOC reflectance data, which are used to invert the canopy RT model. The performance of the TOC approach is therefore limited by the errors introduced in the data during the pre-processing steps. This thesis proposes to minimize these errors by directly using measured TOA radiance data. In such a TOA approach, the atmospheric RT model, which is normally inverted to perform the atmospheric correction, is coupled to the canopy RT model. The coupled canopy-atmosphere model is inverted directly using the measured radiance data. Adjacency, directional and topographic effects can then be included in the coupled RT model. The same regularization methods as used for TOC approaches can be applied to obtain stable and unique estimates. The TOA approach was tested using four case studies based on mono-temporal data. A) The performance of the TOA approach was compared to a TOC approach for three Norway spruce stands in the Czech Republic, using near-nadir Compact High Resolution Imaging Spectrometer (CHRIS) data. The coupled model included canopy directional effects and simulated the CHRIS radiance data with similar accuracy as the canopy model simulated the atmospherically-corrected CHRIS data. Local sensitivity analyses showed that the atmospheric parameters had much less influence on the simulations than the vegetation parameters, and that the sensitivity profiles of the latter were very similar for both TOC and TOA approaches. The dimensionality of the estimation problem was evaluated to be 3 for both approaches. Canopy cover (Cv), fraction of bark material (fB), Cab, and leaf dry matter content (Cdm) were estimated using look-up tables (LUT) with similar accuracy with both approaches. B) Regularization using multi-angular data was tested for the TOA approach, using four angular CHRIS datasets, for the same three stands as used in A). The coupled model provided good simulations for all angles. The dimensionality increased from 3 to 6 when using all four angles. Two LUTs were built for each stand: a 4-variable LUT with fB, Cv, Cdm, and Cab, and a 7-variable LUT where leaf brown pigment concentration (Cs), dissociation factor (D), and tree shape factor (Zeta) were added. The results did not fully match the expectation that the more angles used, the more accurate the estimates become. Although their exploitation remains challenging, multi-angular data have higher potential than mono-angular data at TOA level. C) A Bayesian object-based approach was developed and tested on at-sensor Airborne Prism Experiment (APEX) radiance data for an agricultural area in Switzerland. This approach consists of two steps. First, up to six variables were estimated for each crop field object using a Bayesian optimization algorithm, using a priori information. Second, a LUT was built for each object with only LAI and Cab as free variables, thus spatially constraining the values of all other variables to the values obtained in the first step. The Bayesian object-based approach estimated LAI more accurately than a LUT with a Bayesian cost function approach. This case study relied on extensive field data allowing defining the objects and a priori data. D) The Bayesian object-based approach proposed in C) was applied to a simulated TOA Sentinel-2 scene, covering the area around Zurich, Switzerland. The simulated scene was mosaicked using seven APEX flight lines, which allowed including all spatial and spectral characteristics of Sentinel-2. Automatic multi-resolution segmentation and classification of the vegetated objects in four levels of brightness in the visible domain enabled defining the objects and a priori data without field data, allowing successful implementation of the Bayesian object-based approach. The research conducted in this thesis contributes to the improvement of the use of regularization methods in ill-posed RT model inversions. Three major areas were identified for further research: 1) inclusion of adjacency and topography effects in the coupled model, 2) addition of temporal constraints in the inversion, and 3) better inclusion of observation and model uncertainties in the cost function. The TOA approach proposed here will facilitate the exploitation of multi-angular, multi-temporal and multi-sensor data, leading to more accurate RS vegetation products. These higher quality products will support many vegetation-related applications.</p
Assimilation of remote sensing into crop growth models: Current status and perspectives
Timely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as
ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial
scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to
simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of
blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth
models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and
crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods
are shown to be derived from taking different assumptions in solving for the a posteriori probability density
function (pdf) using Bayes’ rule. This treatment allows us to provide some recommendation on the choice of DA
method for particular applications. We comment on current computational challenges in scaling DA applications
to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending
different observations, as well as facilitating different approaches to crop growth models. We have illustrated
this review with a large number of examples from the literature
Retrieving spectral and biophysical parameters of land vegetation by the Earth Observation Land Data Assimilation System
In this thesis, a new methodology for retrieval of land spectral and biophysical parameters from optical remote sensing data has been designed and used. The result of the work was a physically based methodology for Fraction of Photosynthetically Active Radiation (FAPAR) and Leaf Area Index (LAI) retrievals, simulation of hyper-spectral information and estimation of associated uncertainties. The presented methodology is based on the generic Earth Observation-Land Data Assimilation System (EO-LDAS). In the course of the work it was found that EO-LDAS can be used for daily estimation of FAPAR and associated uncertainties without any in-situ information and when the number of available observations is low. The results were in line with the field measurements with r2 varying from 0.84 to 0.92 and Root Mean Square Error (RMSE) from 0.11 to 0.16. This was the highest rate among compared products (Two Stream Inversion Package - JRC-TIP, Medium Resolution Imaging Spectrometer - MERIS FR and Moderate Resolution Imaging Spectro-radiometer - MODIS MCD15). It was shown, that using MISR information, EO-LDAS temporal regularization and generic dynamic prior, it was possible to stabilize results of the retrieval and to obtain better results than MERIS FAPAR or JRC-TIP MISR. In addition, inclusion of generic static and dynamic prior information, decreases posterior uncertainties and can increase accuracies compared to in-situ data. The results showed that proper estimation of LAI and soil parameters were sufficient to simulate a hyper-spectral signal between 400 and 1000 nm with acceptable precision: best RMSE is equal to 0.03 for real data and less than 0.008 for synthetic data. This implies that in case of the given experimental set-up, LAI and soil parameters are the major mechanisms controlling spectral variations in the visible and near infrared regions
Crop Disease Detection Using Remote Sensing Image Analysis
Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops
An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa
The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards:
• regional, continental and global sharing of ecological data,
• thorough integration of complementing monitoring technologies including DNA-barcoding,
• sophisticated pattern recognition by deep learning,
• advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling,
• decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018
The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
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