A hierarchical clustering method for land cover change detection and identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Sentinel-2 images for detection of wind damage in forestry

Using of Remote sensing for the sake of Earth Observation is getting more and more popular as the number of satellites that are able to measure electromagnetic radiation with a higher spatial, temporal and radiometric resolution is considerably rising. Of all usage of Earth Observation, detection of disturbances caused by natural catastrophe such as wind, earthquake and fire is highly important. On 12th of August 2017, a storm hit South and South East of Finland, bringing harsh disturbances to the forest area in which Pine and Spruce were the main types of land cover. The study area in this region contained the extent of a sentinel-2 image that covered an area of 100 km by 100 km. Two sentinel-2 images from 11th of August 2017 and 5th of September 2017 were used to measure spectra behavior of existing features before and after storm in the region. Forest use notifications data, by which damaged stands were identified, and forest-stand dataset, with which stands that were not touched by the storm (undamaged stands) were characterized, were used as ground truth data. For change extraction, univariate image differencing was used using six different indices, namely EVI, NDVI, NDMI, SATVI, TCB, and TCG. Two main approaches were taken in this thesis, namely pixelwise and average based, where in the former individual pixels were extracted (from stands) and used for training the models while in the later average of pixels inside each stand was calculated and used for training. Results achieved by average-based showed a better performance in terms of user accuracy and stability of the results than pixelwise approach did

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Mapping forest structure with Gini coefficient using digital aerial photogrammetric data from an unmanned aerial system

Metsän sisäisen rakenteen tutkiminen on oleellista tietoa kartoitettaessa metsien monimuotoisuutta, hiilivarantoja tai sisäisiä muutoksia metsän rakenteessa. Tällä hetkellä kaukokartoitusmenetelmät tarjoavat parhaan menetelmän kerätä yksityiskohtaista tietoa metsän rakenteesta laajoilta alueilta. Metsien kaukokartoituksessa kaksi yleisimmin käytettyä menetelmää ovat lentolaserkeilaus ja ilmakuvaus. Niitä voidaan soveltaa myös metsien rakenteen tutkimiseen vertailemalla esimerkiksi puiden pohjapinta-alojen jakaumia, sillä puiden erikokoisuus liittyy oleellisesti metsän rakenteen käsitteeseen. Jakaumista voidaan muodostaa erilaisia jakaumaindeksejä, joista Gini indeksi on osoittautunut lentolaserkeilaustutkimuksissa parhaaksi puiden erikokoisuutta kuvaavaksi indeksiksi. Tutkimalla kaukokartoitusmenetelmien tarkan korkeustiedon yhteyttä kenttätöillä mitattuihin todellisiin jakaumaindekseihin, voidaan metsän rakennetta mallintaa laajoille alueille. Tässä tutkimuksessa kerättiin fotogrammetrinen kolmiulotteinen pistepilviaineisto miehittämättömällä ilmaaluksella Lammin tutkimusaseman ympäristöstä. Tutkimuksen tarkoituksena oli testata fotogrammetrisen aineiston kyvykkyyttä metsän rakenteen mallintamisessa hyödyntäen Gini-indeksiä. Vertailuaineistona käytettiin Maanmittauslaitoksen lentolaserkeilausaineistoa. Tutkimuksen kenttäaineisto koostui 50 ympyränmuotoisesta tutkimusalasta (säde 5m), joista mitattiin puiden ympärysmitat. Näistä laskettiin puiden pohjapinta-alat ja tutkimusalakohtaiset Gini-indeksiluvut. Molemmista kaukokartoitusaineistoista laskettiin koealoille korkeuteen perustuvia pistepilvimuuttujia, joista parhaat muuttujat valittiin käyttäen hyödyksi automatisoitua muuttujavalintafunktiota. Lineaarista betaregressiota hyödyntäen valituista muuttujajoukoista koostettiin molemmille aineistoille parhaat mahdolliset Gini-indeksi mallit. Lopuksi nämä mallit yleistettiin koko tutkimusalueelle ja kartat ennustetuista gini-indekseistä koostettiin. Luodut mallit suoriutuivat Gini-indeksin ennustamisesta keskivertoisesti. Parhaan fotogrammetrisen mallin suhteellinen ristiinvalidoitu keskivirhe oli 29,8% ja parhaan laserkeilauspohjaisen mallin 27,2%. Lentolaserkeilausmallin selitysaste (0,49) osoittautui myös paremmaksi kuin fotogrammetriapohjaisen mallin (0,39). Lentolaserkeilausmallin laatuluvut olivat hiukan heikompia kuin vastaavissa aiemmissa tutkimuksissa, mikä saattoi johtua osittain ei-optimaalisesta keilausajankohdasta. Fotogrammetrinen mallin käytöstä metsän rakenteen tutkimisessa ei ole aikaisempia tutkimuksia. Tämän tutkielman tulokset puoltavat lentolaserkeilauksen käyttämistä metsän rakenteen kartoittamisessa fotogrammetrian sijaan. Fotogrammetrinen metodi osoittautui edulliseksi ja joustavaksi tavaksi kerätä kolmiulotteista tietoa metsistä, mutta sen kyvyttömyys kerätä informaatiota latvuskerroksen alta huononsi sen suoriutumista.Gathering information on forest structure is vital in estimating forest biodiversity, carbon stocks and temporal changes in standing forests. Currently the only viable method of collecting such information in vast areas is remote sensing (RS). Two commonly used RS methods for acquiring high resolution three dimensional information on forest structure are airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). In quantifying forest structure, the distributions of tree basal areas have been used because the variation in tree sizes is closely linked to the whole concept of forest structure. Retrieving information on these distributions can be done by modelling the relationship of in situ measured distribution indices and the remotely sensed elevation information. One of these distribution indices is the Gini coefficient which has been shown to be a prominent index in describing the forest structure from ALS data. In this study, DAP data was gathered with an unmanned aerial system (UAS) from the vicinity of the Lammi research station with the intention of investigating its suitability on modelling forest structure by using Gini coefficient (GC). Airborne laser scanning data retrieved from the National land survey was used as a comparison dataset. The in situ measured field data consisted of tree circumference measurements from 50 circular plots (r = 5m). From these measurements, the tree basal areas were calculated and the plot level Gini coefficients determined. A comprehensive set of plot level point cloud variables were also calculated from both ALS and DAP point clouds. The most important predictor variables were chosen from the point cloud variables with an automatic exhaustive variable selection function. Then, beta regression modelling was applied to both sets of predictor variables and the best GC models determined. Finally, the models were generalized to the whole study area and GC maps were produced. The resulting GC models for both datasets performed in a mediocre way. The best DAP model had a cross-validated RRMSE of 29.8% and the best ALS model had RRMSE of 27.2%. The coefficient of determination (R2) was also better in the ALS model (0.49) than in the DAP model (0.39). The performance of the ALS model was slightly worse than in previous studies using ALS to predict GC. Part of this might be a repercussion of the non-optimal acquisition time of the ALS dataset. For DAP, there were no previous studies. The results of this study suggest that ALS is a more prominent method in mapping forest structure with GC. The DAP proved to be an inexpensive and flexible method of gathering three dimensional information on forests but it had poor canopy penetration abilities which affected the modelling performance negatively

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

An unsupervised classification-based time series change detection approach for mapping forest disturbance

Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 – 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results.Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) grant entitled Advanced Methods, Education and Training in Hyperspectral Science and Technology (AMETHYST). Financial code: KS-NSERC2 Staenz 40307-4185-800

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management