Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of (Formula presented.) m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.</p

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

Errors related to the automatized satellite-based change detection of boreal forests in Finland

Highlights • Forest changes were automatically modelled from multitemporal Sentinel-2 images. • Errors were evaluated based on visually interpreted VHR images. • Extraction of clear-cuts was accurate whereas thinnings had more variation. • Image quality and translucent clouds had most significant effect on errors. • Results were regarded applicable for operational change monitoring.The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency

Energy from biomass : Assessing sustainability by geoinformation technology

Publisher Copyright: © 2021 Austrian Acedemy of Sciences Press. All rights reserved.Forest Flux https://www.forestflux.eu/ will renew forestry value-added services in Earth Observation (EO) by creating and piloting cloud-based services for committed users on forest carbon assimilation and structural variable prediction. Forest Flux exploits the explosive increase of high-resolution EO data from the Copernicus program and developments of cloud computing technology. It implements a world-first service platform for high-resolution maps of traditional forestry variables together with forest carbon fluxes. Forest Flux will allow the users to improve the profitability of forest management while taking care of ecological sustainability. The Forest Flux services are implemented on the Forestry Thematic Exploitation cloud platform https://f-tep.com/. In 2020, nearly 700 thematic maps on forest stand and carbon flux variables were delivered to nine specific users in a form that was applicable to their operational forest management systems. The last project year 2021 focuses on map product refinement and improving user services, which eventually lead to operational service concepts.Peer reviewe

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

HUBUNGAN TINGKAT PENGETAHUAN, SIKAP, DAN MOTIVASI DENGAN PERILAKU CUCI TANGAN PAKAI SABUN (CTPS) PADA SISWA SEKOLAH DASAR NEGERI TRIDADI, SLEMAN, DIY

Latar Belakang: Cuci tangan pakai sabun merupakan salah satu tindakan sanitasi dengan membersihkan tangan dan jari-jemari menggunakan air dan sabun untuk menjadi bersih serta dapat mencegah terjadinya penyakit. Cuci tangan pakai sabun merupakan indikator dari program Perilaku Hidup Bersih dan Sehat (PHBS) di sekolah. Kebiasaan cuci tangan penting untuk diajarkan sejak dini karena anak-anak merupakan calon agen perubahan untuk lingkungan sekitarnya. Salah satu faktor yang mempengaruhi terbentuknya perilaku cuci tangan adalah pengetahuan, sikap, motivasi. Oleh karena itu, penelitian ini bertujuan untuk mengetahui hubungan antara tingkat pengetahuan, sikap, dan motivasi dengan perilaku cuci tangan pakai sabun (CTPS) pada siswa SDN Tridadi, Sleman, DIY. Metode: Jenis penelitian kuantitatif menggunakan metode analitik observasional dengan pendekatan cross sectional. Sampel dalam penelitian ini adalah siswa kelas 4 dan 5 SDN Tridadi sebanyak 46 responden menggunakan teknik total sampling. Instrumen penelitian menggunakan kuesioner. Analisi data menggunakan analisis univariat dan bivariat yaitu uji chi square. Hasil: Hasil penelitian menunjukkan 65,2% siswa memiliki pengetahuan tinggi 60,9% siswa memiliki sikap tinggi. 56,5% siswa memiliki motivasi tinggi. Serta 54,3% siswa memiliki perilaku cuci tangan pakai sabun baik. Hasil uji statistik dengan analisis Chi Square menunjukkan ada hubungan antara tingkat pengetahuan (P= 0,047), sikap (P= 0,001), dan motivasi (P= 0,044) dengan perilaku CTPS pada siswa SDN Tridadi, Sleman, DIY. Kesimpulan: Berdasarkan hasil penelitian, didapatkan bahwa ada hubungan antara tingkat pengetahuan, sikap, dan motivasi dengan perilaku cuci tangan pakai sabun pada siswa SDN Tridadi, Sleman, DIY

Subtypes of Acute Ischemic Stroke

Background: To determine the frequency ofvarious subtypes of acute ischemic stroke amongpatients using the TOAST criteria.Methods: In this prospective, cross sectional study156 consecutive stroke patients fulfilling theinclusion criteria were recruited. Information on riskfactors like age, gender, diabetes and hypertensionwas collected. Physical and neurologicalexamination was done and relevant investigationswere reviewed, to classify the subtype of strokeaccording to TOAST criteria. . Risk factors like age,gender, diabetes and hypertension were comparedwith stroke subtypes after stratification using thechi-square test with significance at p &lt; 0.05.Results: Out of the 156 patients with acute ischemicstroke, mean age at presentation was 63.51 years.Among them 75% had hypertension and 48.1% werediabetics. The various subtypes of acute ischemicstroke were Large artery atherosclerosis(35.3%)whichwas the commonest cause. Large arteryatherosclerosis was found to be more common infemales (47.1% vs 25.6%) whereas cardioembolicstrokes were more common in males (29.1% vs17.1%) (p value 0.02). When hypertension anddiabetes was compared with stroke subtypes theresults were statistically insignificant (p value.&gt;0.05).Conclusion: Higher incidence of large artery andcardioembolic disease was found. Preventive effortsagainst the burden of ischemic stroke should focuson risk factor intervention for each patient accordingto subtype rather than ischemic stroke as a whole

Avaruus arjessamme : Avaruustoiminnan yhteiskunnallinen vaikuttavuus (AVARTAVA) loppuraportti

Selvityksen tavoitteena oli muodostaa ajankohtainen kuva siitä, miten avaruustoiminnan mahdollisuuksia hyödynnetään hallitusohjelman tavoitteiden ja muiden yhteiskunnallisten tavoitteiden toteuttamisessa sekä eri hallinnonalojen päätöksenteossa. Päähavainto on, että avaruustoimintaa hyödynnetään laajasti eri hallinnonalojen päätöksenteossa sekä hallitusohjelman ja muiden yhteiskunnallisten tavoitteiden toteuttamisessa, vaikka avaruustoiminnan roolia aina ei välttämättä tiedosteta. Avaruustoiminnan palveluilla, kuten paikkatiedolla ja aikasignaalilla, kaukokartoituksella ja satelliittitietoliikenteellä on merkittäviä sovelluksia viranomaistoiminnassa, esim. pelastuksessa, ympäristövalvonnassa ja turvallisuusviranomaisten toiminnassa, sekä liike-elämässä, erilaisten tietoliikenne- ja energiaverkkojen, kaupankäynnin, kuljetusten ja palveluiden mahdollistajana. Kaiken kaikkiaan avaruustoiminta on kriittistä yhteiskunnan normaalille toiminnalle. Selvitysryhmä suosittaakin tiedostamaan avaruustoiminnan roolin kriittisyyden. Selvityksessä esitetään toimenpiteitä tarvittavan osaamisen ja resurssien varmistamiseksi, jotta avaruustoiminnan palveluita voidaan hyödyntää jatkossa entistä laajemmin.Tämä julkaisu on toteutettu osana valtioneuvoston selvitys- ja tutkimussuunnitelman toimeenpanoa. (tietokayttoon.fi) Julkaisun sisällöstä vastaavat tiedon tuottajat, eikä tekstisisältö välttämättä edusta valtioneuvoston näkemystä

A methodology for implementing a digital twin of the earth’s forests to match the requirements of different user groups

Publisher Copyright: © 2021 GI_Forum.Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.Peer reviewe

Relasphone - Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland ( boreal zone) were in good agreement with reference forest inventory plot data on pine ( R-2 = 0.75, RMSE = 5.33 m(2)/ha), spruce ( R-2 = 0.75, RMSE = 6.73 m(2)/ha) and birch ( R-2 = 0.71, RMSE = 4.98 m(2)/ha), with total relative RMSE ( %) = 29.66%. In Durango, Mexico ( temperate zone), Relasphone stem volume measurements were best for pine ( R-2 = 0.88, RMSE = 32.46 m(3)/ha) and total stem volume ( R-2 = 0.87, RMSE = 35.21 m(3)/ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.Peer reviewe