Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images

A large-scale feature selection wrapper is discussed for the classification of high dimensional remote sensing. An efficient implementation is proposed based on intrinsic properties of Gaussian mixtures models and block matrix. The criterion function is split into two parts:one that is updated to test each feature and one that needs to be updated only once per feature selection. This split saved a lot of computation for each test. The algorithm is implemented in C++ and integrated into the Orfeo Toolbox. It has been compared to other classification algorithms on two high dimension remote sensing images. Results show that the approach provides good classification accuracies with low computation time

Above-ground biomass estimation of arable crops using UAV-based SfM photogrammetry

This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto International on 3 dec 2018, available online: http://www.tandfonline.com/10.1080/10106049.2018.1552322Methods of estimating the total amount of above-ground biomass (AGB) in crop fields are generally based on labourious, random, and destructive in situ sampling. This study proposes a methodology for estimating herbaceous crop biomass using conventional optical cameras and structure from motion (SfM) photogrammetry. The proposed method is based on the determination of volumes according to the difference between a digital terrain model (DTM) and digital surface model (DSM) of vegetative cover. A density factor was calibrated based on a subset of destructive random samples to relate the volume and biomass and efficiently quantify the total AGB. In all cases, RMSE Z values less than 0.23 m were obtained for the DTMDSM coupling. Biomass field data confirmed the goodness of fit of the yieldbiomass estimation (R2=0,88 and 1,12 kg/ha) mainly in plots with uniform vegetation coverage. Furthermore, the method was demonstrated to be scalable to multiple platform types and sensorsThis work was supported by the life project “Operation CO2: Integrated Agroforestry Practices and Nature Conservation Against Climate Change - LIFE+ 11 ENV/ES/535” and by Xunta de Galicia under the grant “Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)” Ref. ED431B 2016/030 and Ref. ED341D R2016/023.S

A 17-year assesment of changes in biomass in the south of Chile, using Landsat satellite images

Southern Chile is recognized by different international organizations such as UNESCO (United Nations Educational, Scientific and Cultural Organization), and Conservational International as an ecologically sensitive area. The country of Chile is home to one third of the earth’s remaining temperate forests. Monitoring and assessing land cover changes in these forests is important not only to international organizations but to the people of Chile. The lack of multi-temporal studies that evaluate changes in land cover biomass make this study an important one for increasing awareness of how the evolution of the landscape affects environmental planning and development of legal precedents aimed at protecting this rich ecological habitat. The results of this study revealed important differences between the growth and loss of biomass in the period between 1986 and 2003. In the study area, 40% of areas that traditionally supported row crops and pasture hay were replaced my forestry plantations and herbaceous successional vegetation. This replacement has impacted the Chilean people and their agricultural way of life. The results obtained depict the usefulness of Change Vector Analysis (CVA) as a technique to analyze changes in biomass over time

Evaluation of remote sensing approaches

Megan M Lewis, Davina Whitehttp://archive.nwc.gov.au/library/topic/groundwater/allocating-water-and-maintaining-springs-in-the-great-artesian-basi

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

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

Monitoring and prediction of pasture quality and productivity using planet scope satellite data for sustainable livestock production systems in Colombia

As the population increases, demand for food increases too, which has led to large-scale land conversion to improve livestock production in Colombia. Fulfilling these criteria of increasing demand in a sustainable way is a challenge and remote sensing data provides an accurate method to support this task. In this study, Planet Scope multispectral satellite datasets and coincident field measurements acquired over test fields in the study area (Patía) of September 2018 was used. Fresh and dry weight biomass was calculated and forage quality analyses, crude protein (CP), in vitro dry matter digestibility (IVDMD), Ash and standing biomass dry weight (DM) was carried out in the forage nutritional quality laboratory of International Centre for Tropical Agriculture (CIAT). Field data was related to the remote sensing data using the random forest regression algorithm. R was required for the statistical analysis, to figure out the model performance for IVDMD, CP, Ash and DM. This project also investigated the spatial distribution of livestock which is affected by quality and area of potential forage zones. The R2 values of the regression models were 0.74 for IVDMD, 0.69 for CP, 0.38 for Ash and 0.49 for DM using a predictor combination of vegetation indices, simple ratios and bands