MY PAPERs by mr nice guy
Minimnya informasi ilmiah dan kurangnya perhatian terhadap status mangrove di kawasan sungai Kemb... more Minimnya informasi ilmiah dan kurangnya perhatian terhadap status mangrove di kawasan sungai Kembung membuat penelitian ini penting dilakukan. Empat seri data Landsat, perekaman tahun 1996, 2002, 2010, dan 2013 digunakan untuk memetakan mangrove dan mendeteksi perubahannya. Teknik klasifikasi hibrida, yang merupakan gabungan klasifikasi berbasis obyek dengan algoritma klasifikasi random forest, digunakan dalam analisis perubahan tutupan mangrove. Hasil menunjukkan bahwa berdasarkan klasifikasi hibrida, tutupan lahan mangrove dapat dideteksi dengan memuaskan pada tingkat akurasi 82,6-88,4%. Analisis perubahan tutupan lahan menyatakan bahwa luas mangrove di Sungai Kembung relatif stabil dan dalam jangka waktu pengamatan tersebut, mangrove yang berubah menjadi penutup lahan lainnya sebesar 197,2 hektar, bertambah seluas 251,1 hektar dan yang tidak mengalami perubahan seluas 2904,9 hektar. Perubahan mangrove umumnya diakibatkan oleh faktor antropogenik seperti penanaman mangrove, penebangan, perubahan alih fungsi mangrove menjadi jalan, tanggul, permukiman, tambak udang dan pertumbuhan alami. Diperlukan perhatian yang serius dari berbagai pihak untuk mempertahankan keberadaan ekosistem mangrove di Sungai Kembung.
The objective of this research was to evaluate the accuracy of random forest classification rule ... more The objective of this research was to evaluate the accuracy of random forest classification rule using object based image analysis (OBIA) application (eCognition Developer) and the results were compared with common pixel-based classification algorithm (maximum likelihood/ML) for mangrove land cover mapping in Kembung River, Bengkalis Island, Indonesia. Seven data input model derived from Landsat 5TM bands, ALOS PALSAR FBD, and spectral transformations (NDVI, NDWI, NDBI) were examined by both classifiers. Feature objects statistical parameters were selected and implemented on random forest classifier. Overall accuracy (OA) as well as user and producer accuracies and Kappa statistic were used to compare classification results. Our results showed that the more data model used produced higher overall accuracy and kappa statistics for RF classifier. For each data input model, random forest classifier has higher overall accuracy than maximum likelihood. The best mangrove discrimination in RF classifier was achieved when the combination of Landsat 5 TM, SAR, and spectral transformation were used, while in ML classifier, the best mangrove discrimination was achieved when the combination of Landsat 5 TM and ALOS PALSAR was used. The overall accuracy achieved by RF classifier was 81.1% and 0.76 for Kappa statistic. Meanwhile, for ML classifier, the overall accuracy achieved was 77.7% and 0.71 for Kappa statistic.
Penginderaan jauh didefmisikan sebagai ilmu dan seni untuk memperoleh informasi tentang suatu obj... more Penginderaan jauh didefmisikan sebagai ilmu dan seni untuk memperoleh informasi tentang suatu objek atau fenomena melalui analisis data yang diperoleh dengan suatu alat tanpa kontak langsung dengan objek, daerah atau fenomena yang dikaji (Lillesand dan Kiefer, 1990). Sistem penginderaan jauh terdiri dari lima komponen dasar, yaitu sumber tenaga, atmosfer, interaksi antara tenaga dengan benda di muka bumi, sensor, dan sistem pengolahan data dan berbagai penggunaannya.
Abstrak Perairan estuaria Sungai Musi merupakan daerah penangkapan ikan yang potensial di Propins... more Abstrak Perairan estuaria Sungai Musi merupakan daerah penangkapan ikan yang potensial di Propinsi Sumatera Selatan, sehingga di wilayah ini terjadi aktifitas penangkapan yang cukup padat. Akibatnya terjadi tekanan yang cukup besar terhadap sumberdaya ikan dikawasan ini. Fungsi ekologi estuaria sebagai spawning ground dan nursery ground mulai mengalami gangguan akibat intensitas penangkapan yang besar. Beberapa alat tangkap ikan yang tidak selektif beroperasi dalam jumlah banyak sehingga menyebabkan penurunan terhadap stok ikan. Akibatnya konflik pemanfaatan ruang sering terjadi antar sesama nelayan atau pengguna lainnya. Seperti konflik jaring trawl dengan gill net dan pancing rawai dan konflik nelayan tuguk dengan pengemudi kapal. Untuk menyelesaikan konflik diatas dan menjaga kelestarian sumberdaya ikan di estuaria sungai Musi, beberapa langkah pengendalian yang direkomendasikan untuk dilakukan adalah: melakukan pengaturan penangkapan ikan, melakukan sosialisasi peraturan perikanan kepada masyarakat, meningkatkan kapasitas kelembagaan pengelolaan perikanan, pelarangan penggunaan alat tangkap tertentu, dan memberikan bantuan modal usaha kepada nelayan. Kata Kunci : estuaria sungai Musi, pengendalian, sumberdaya ikan Abstract : Resources Controlling of Fish in Musi River Estuary. By : Estuary of Musi river is a potential fishing areas in South Sumatra Province, so that in this region occur fairly heavy fishing activity. The consequence is a large pressure on fish resources of this region. The ecological functions of estuaries as nursery ground and spawning ground, begin to experience problems due to the large fishing intensity. Some fishing gear that does not selectively operate in large quantities resulted in a decline of fish stocks. As the result spatial use conflicts often occur between fishermen or other user, as examples is the conflict between trawl fishing with gill nets and longline operator and the operator of driver tuguk ship. To resolve the above conflict and preserve the fish resources in the estuary of the river Musi, a few recomendation that must be addressed several step must doing : fisheries regulations to disseminate to the public, enhance institutional capacity for fisheries management, ban the use of certain fishing gear and provide venture capital assistance to fishermen. Key word : Musi river estuary,controllingt, fish resources Pendahuluan Jenis-jenis ikan yang banyak terdapat diwilayah estuaria antara lain ikan, udang dan moluska. Jenis ikan, udang dan moluska yang terdapat di perairan estuaria meliputi: ikan bawal putih (Pampus argenteus), bawal hitam (Formio niger), bilis (Stolephorus indicus), manyung (Arius thalassinus), kurau (Eleutheronema tetradactylum), tenggiri (Scomberomorus guttatus), kakap putih (Lates Calcalifer), udang (Penaeus sp), cumicumi (Loligo sp), kepiting bakau (Scylla spp), rajungan (Portunus pelagicus), kerang dan siput.
This research in coastal area of Dumai has been completed in September untill Desember 2003. The ... more This research in coastal area of Dumai has been completed in September untill Desember 2003. The aim of this research was to collect basic data of mangrove ecosystem which covered inventory of biodiversity and ecology structure by using primary and secondary data. Primary data of mangrove forest ecosystem has been collected by line transect method while secondary data was collected by study literature and previous study report. The result showed that there were 17 types of primary mangrove and 18 types of secondary mangrove. The main value indeks in every study area indicated that in the study area I and IV, Xylocarpus granatum had high value as 104,53 and 104,22 subsequently. In the study area II, III and V, Rhizophora apiculata had the high value which subsequently were 114,39, 108,81 and 110,19. There were two main extinct spesies of mangrove, which are Scyphiphora hydrophyllaceae (Cingam/Perepat Lanang) and Sonneratia ovata (Kedabu), in which exceptional management is needed for sustainability of these species.
Object Based Image Analysis by mr nice guy
Remote sensing imagery needs to be converted into tangible information which can be utilised in c... more Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA -or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.
Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particu... more Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Pará State, to the Bay of São José in Maranhão.
Construction of the Petrochemical Complex of Rio de Janeiro (COMPERJ) will introduce a new scenar... more Construction of the Petrochemical Complex of Rio de Janeiro (COMPERJ) will introduce a new scenario to the Guapi-Mirim Environmental Protection Area (EPA) in the coming years, since it will require constant environmental monitoring so as to portray its ecological evolution. Therefore, the objective of this paper is to perform a multitemporal analysis of the Guapi-Mirim EPA, using objectbased segmentation and classification techniques applied to IKONOS II images, in order to characterize changes in land use and cover types in the investigated site. Two scenes of the IKONOS II sensor acquired on 2006 and 2008 were chosen for the study. Overall results reveal a regeneration stage for the mangrove ecosystem and a stagnation of the urban area growth within the limits of the Guapi-Mirim EPA.
Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for... more Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA), and support vector machine (SVM) classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863) for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded.
Pixel-based and object-based image analysis approaches for classifying broad land cover classes o... more Pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM). Overall classification accuracies between pixelbased and object-based classifications were not statistically significant (p > 0.05) when the same machine learning algorithms were applied. Using object-based image analysis, there was a statistically significant difference in classification accuracy between maps produced using the DT algorithm compared to maps produced using either RF (p = 0.0116) or SVM algorithms (p = 0.0067). Using pixel-based image analysis, there was no statistically significant difference (p > 0.05) between results produced using different classification algorithms. Classifications based on RF and SVM algorithms provided a more visually adequate depiction of wetland, riparian, and crop land cover types when compared to DT based classifications, using either object-based or pixel-based image analysis. In this study, pixel-based classifications utilized fewer variables (15 vs. 300), achieved similar classification accuracies, and required less time to produce than object-based classifications. Object-based classifications produced a visually appealing generalized appearance of land cover classes. Based exclusively on overall accuracy reports, there was no advantage to preferring one image analysis approach over another for the purposes of mapping broad land cover types in agricultural environments using medium spatial resolution earth observation imagery.
Mangrove Ecology by mr nice guy
Composition: The cover is printed on Aconda 300 gsm which contains 40% recycled fibre and of the ... more Composition: The cover is printed on Aconda 300 gsm which contains 40% recycled fibre and of the Paper: 60% virgin wood fibre of which at least 50% is FSC certified.
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MY PAPERs by mr nice guy
Object Based Image Analysis by mr nice guy
Mangrove Ecology by mr nice guy