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Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 663 Titre : Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia Type de document : Article/Communication Auteurs : Sanjiwana Arjasakusuma, Auteur ; Sandiaga Swahyu Kusuma, Auteur ; Raihan Rafif, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] bande C [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Java (île de) [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] Normalized Difference Built-up Index [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] rizière [Termes descripteurs IGN] série temporelle Résumé : (auteur) The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields. Numéro de notice : A2020-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110663 date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.3390/ijgi9110663 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96346 [article]

L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])  

Public [article]  inGeocarto international > vol 35 n° 12 [01/09/2020] . - pp 1327 - 1342 Titre : L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia Type de document : Article/Communication Auteurs : Bambang H Trisasongko, Auteur ; David J. Paull, Auteur Année de publication : 2020 Article en page(s) : pp 1327 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] arbre hors forêt [Termes descripteurs IGN] bande L [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carbone [Termes descripteurs IGN] données allométriques [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] Java (île de) Résumé : (auteur) This article discusses an experiment on the estimation of rubber tree biomass using L-band Synthetic Aperture Radar (SAR), to support recent efforts to include trees outside forest in global biomass and carbon accounting. We noted that date of acquisition is important, but certainly the selection of allometric equation serving as the reference data was paramount. Similarly, choosing a proper form of fully polarimetric data was instrumental, although this requires validation in different environmental settings. As expected, modern data mining approaches consistently delivered high accuracy. Extreme learning machine yielded the best estimate in terms of R2 (0.98) and RMSE (1.88 Mg/ha); nonetheless, it also delivered a slight negative estimation. In this case, we found that a variant of random forest produced an outcome without any negative estimation. This research suggests that estimated biomass or carbon information from rubber plantations would be an invaluable candidate for the improvement of global biomass data. Numéro de notice : A2020-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1573855 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1573855 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95630 [article]

What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 / Christopher M. Wade in Forests, vol 11 n° 5 (May 2020)  

Public [article]  inForests > vol 11 n° 5 (May 2020) . - 14 p. Titre : What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 Type de document : Article/Communication Auteurs : Christopher M. Wade, Auteur ; Kemen G. Austin, Auteur ; James Cajka, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] aire protégée [Termes descripteurs IGN] Amérique du nord [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] arbuste [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] Congo [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] gaz à effet de serre [Termes descripteurs IGN] habitat forestier [Termes descripteurs IGN] indicateur de service écosystémique [Termes descripteurs IGN] Indonésie [Termes descripteurs IGN] protection des forêts [Termes descripteurs IGN] risque environnemental [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth’s terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly—and increasingly—followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States. Numéro de notice : A2020-655 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f11050539 date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.3390/f11050539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96119 [article]

Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)  

Public [article]  inRemote sensing > vol 12 n° 7 (April 2020) Titre : Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine Type de document : Article/Communication Auteurs : Thuan Sarzynski, Auteur ; Xingli Giam, Auteur ; Luis Carrasco, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Elaeis guineensis [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Sumatra [Termes descripteurs IGN] surveillance agricole [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia. Numéro de notice : A2020-455 Affiliation des auteurs : non IGN Nature : Article DOI : 10.3390/rs12071220 date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.3390/rs12071220 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95554 [article]

Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 40 n°19 (February 2019) . - pp 7439 - 7458 Titre : Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors Type de document : Article/Communication Auteurs : Pauline Perbet, Auteur ; Michelle Fortin, Auteur ; Anouk Ville, Auteur ; Martin Béland, Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 7439 - 7458 Note générale : bibliographie This work was supported by the Natural Sciences and Engineering Research Council of Canada. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse vectorielle [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] défrichement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] Indonésie [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] surveillance forestière Résumé : (auteur) Malaysia and Indonesia have been affected by deforestation caused in great part by the proliferation of oil palm plantations. To survey this loss of forest, several studies have monitored these southeast Asian nations with satellite remote sensing alert systems. The methods used have shown potential for this approach, but they are limited by imagery with coarse spatial resolution, low revisit times, and cloud cover. The objective of this research is to improve near real-time operational deforestation detection by combining three sensors: Sentinel-1, Sentinel-2 and Landsat-8. We used Change Vector Analysis to detect changes between non-affected forest and images under analysis. The results were validated using 166 plots of undisturbed forest and confirmed deforestation events throughout Sabah Malaysian State, and from 70 points from drone pictures in Sumatra, Indonesia. Sentinel-2 and Landsat-8 yielded sufficient results in terms of accuracy (less than 11% of commission and omission error). Sentinel-1 had lower accuracy (14% of commission error and 28% of omission error), probably resulting from geometric distortions and speckle noise. During the high cloud-cover season optical sensors took about twice the time to detect deforestation compared to Sentinel-1 which was not affected by cloud cover. By combining the three sensors, we detected deforestations about 8 days after forest clearing events. Deforestations were only detectable during approximately the first 100 days, before bare soils were often coved by legume crop. Our results indicate that near real-time deforestation detection can reveal most events, but the number of false detections could be improved using a multiple event detection process. Numéro de notice : A2019-321 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1579390 date de publication en ligne : 17/02/2019 En ligne : https://doi.org/10.1080/01431161.2019.1579390 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93295 [article]

Long-term land deformation monitoring using quasi-persistent scatterer (Q-PS) technique observed by sentinel-1A : case study Kelok Sembilan / Pakhrur Razi in Advances in Remote Sensing, vol 7 n° 4 (December 2018)  

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Improving large area population mapping using geotweet densities / Nirav N. Patel in Transactions in GIS, vol 21 n° 2 (April 2017)  

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Evidence of postseismic deformation signal of the 2007 m8.5 Bengkulu earthquake and the 2012 m8.6 Indian ocean earthquake in southern Sumatra, Indonesia, based on GPS data / Satrio Muhammad Alif in Journal of applied geodesy, vol 10 n° 2 (June 2016)  

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A review of forest and tree plantation biomass equations in Indonesia / Kamalakumari Anitha in Annals of Forest Science [en ligne], vol 72 n° 8 (December 2015)  

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Geospatial analysis of urban landscape patterns in three major cities of Southeast Asia / Ronald C. Estoque in Tsukuba geoenvironmental sciences, vol 10 (december 2014) 

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Towards productive landscapes / Jorge Chavez-Tafur (2014)  

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Seeking the truth in Aceh / Anonyme in GEO: Geoconnexion international, vol 11 n° 7 (july - august 2012)

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The potential of spectral mixture analysis to improve the estimation accuracy of tropical forest biomass / T.M. Basuki in Geocarto international, vol 27 n° 4 (July 2012)  

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Long-term consecutive DInSAR for volume change estimation of land deformation / S. Sumantyo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)  

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Signature des séismes dans le champ de pesanteur / Isabelle Panet in XYZ, n° 127 (juin - août 2011)

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