68 research outputs found

    Analisis Pola Konversi Lahan Sawah dan Struktur Hubungan Penyebab dan Pencegahannya (Studi Kasus Kabupaten Subang, Provinsi Jawa Barat)

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    The extent of land use and land cover (LULC) of paddy field in Subang Regency has decreased because of the conversion into non-paddy field. The objectives of this research were to review the spatial pattern of LULC change, to analyze the cause and to identify the anticipation strategy of paddy field conversion. The analysis used the Landsat data of 1999, 2004, 2009, and 2014 which were interpreted by supervise technique. The interpretation result was compared with the existing LULC and was examined by Kappa methode. This research focuses on the spatial pattern of LULC change, integrated with the Interpretative Structural Modeling (ISM) to review the cause and the anticipation strategy of the conversion. The results revealed that the paddy field was converted into plantation, built-up area, and dryland agriculture. The ISM result revealed that the conversion causes were: (1) increasing the farmer economic needs, (2) increasing the built-up area, (3) increasing the selling price of land and (4) decreasing the farming motivation. To anticipate the conversion, several priorities are needed, namely (1) rehabilitation of the irrigation infrastructures and regulation of the spatial planning, (2) tighten the conversion permit and maximization of the abandoned land, and (3) giving the incentive and disincentive for the farmers, land consolidation, and establishment of corporate farming

    Land Use Change Detection and Urban Sprawl Monitoring in Metropolitan Area of Jakarta (Jabodetabek) from 2001 to 2015

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    Being 13th largest city in the world makes Jakarta as a fascinating city in South East Asia. Its surrounding regions are included in a particular metropolitan area called “Jabodetabek”. Population growth in this metropolitan area about 10 million only in 15 years from 2000 to 2015. Consequently, loss of vegetation and agricultural land, less water resources, increasing demand for housing and transportation infrastructure as the effect of this ever-growing population take place. This phenomenon can be detected using Landsat satellites images. The settlement or urban area in Jabodetabek shows a huge increase in percentage from 2001 to 2015, so much that the urban area is the dominant land cover and reaches up to 61 percent of Jabodetabek in year 2015. Moreover settlement density in Jabodetabek (ring zones 25 to 45 km from central city) shows an increase of more than 20% urban areas in year 2015. Furthermore, the result of compactness reveals that this urban expansion in Jabodetabek was spread out from 2001 to 2008 and became more compacted by 2015

    Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids

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    Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L(-1) and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L(-1) and less than 600 NTU, respectively and used rather than using whole dataset (R(2) = 0.93 versus 0.88 for turbidity and R(2) = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R(2) = 0.66) was better than with the MR approach (R(2) = 0.58), as expected due to the nonlinear nature of the transformation model

    Analisis Citra Satelit Multitemporal Untuk Kajian Perubahan Penggunaan Lahan Di Kota Surabaya, Kabupaten Gresik

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    Perubahan lahan penggunaan lahanyang cepat telah terjadi di Kota Surabaya, Kabuapten Gresik dan Sidoarjo selama satu dekade terakhir. Proses Perubahan penggunaan lahan menunjukkan tidak ada tanda-tanda perlambatan yang disebabkan oleh percepatan industrialisasi dan urbanisasi. Dalampaper ini, dinamika Perubahan penggunaan lahandiinvestigasi dengan menggunakan analisis citra penginderaan jauh multitemporal, Cellular Automata (CA) Markov dan analisis pola spasial. Studi ini menunjukkan bahwa integrasi analisis multitemporal dan deteksi Perubahan pasca klasifikasi merupakan pendekatan yang efektif untuk menganalisis arah, kecepatan, dan pola spasial dari Perubahan penggunaan lahan. Integrasi kedua metode tersebut dengan CA Markov menjadi sangat bermanfaat dalam proyeksi dan analisis proses Perubahan penggunaan lahan tahun 1994-2012. Hasil menunjukkan bahwa pemukiman meningkat 0,84% per tahun dari total area, terutama disebabkan oleh konversi dari lahan pertanian dan lahan budidayaperairan

    Independent Smallholder Oil Palm Expansion and Its Impact On Deforestation: Case Study in Kampar District, Riau Province, Indonesia

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    Independent smallholders who manage their own oil palm plantations without receiving technical assistance and agricultural inputs from oil palm estates or government have been  increasing rapidly in Indonesia in recent years. However the magnitude of their impacts on tropical forest deforestation remains largely unevaluated.  The objective  of this study was  to explore the history of land use, and the changes in land cover and status since the onset  of  oil palm plantation activities. The study was conducted from March to April 2016. Surveys  were carried out in 30 ha of independent smallholder oil palm in  Kampar District, Riau Province.  To identify the land status, the Agreed functional forest classification (TGHK) and Provincial land use planning (RTRWP) maps were overlaid on images of the area of independent smallholder oil palm. Landsat images three years before oil palm was established were used to assess forest cover changes.  Furthermore, oil palm smallholders and elders of the local community in the research area  were  interviewed to identify land use prior to oil palm.  Our results showed that, based on land  status, 47% of  the area of independent smallholders’ oil palm derived from logged forest; that is the land  changed in status from forest to oil palm plantation.  The other 53% of oil palm area derived from non-forested land. The land use history before the establishment of independent smallholder oil palm mostly comprised general-purpose field activities and former forest-felling (forest concessions). The land cover  before conversion into oil palm comprised rubber plantation, secondary forest, and shrub cover. From the results of our survey, we conclude that most of the oil palm plantations planted between  1990 and 2002 have their origins not in primary forest, but rather in  degraded secondary forest, former fields, and shrub-land. These results imply that conversion of forest area into oil palm plantations is not the direct cause of deforestation in the tropical forests of Kampar, Riau Province

    The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa.

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    Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Soil organic carbon (SOC) is a vital measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, unprecedented anthropogenic disturbances have significantly altered SOC distribution across the globe, leading to considerable carbon losses. In addition, reliable SOC estimates, particularly over large spatial extents remain a major challenge due to among others limited sample points, quality of simulation data and suitable algorithms. Remote sensing (RS) approaches have emerged as a suitable alternative to field and laboratory SOC determination, especially at large spatial extent. Nevertheless, reliable determination of SOC distribution using RS data requires robust analytical approaches. Compared to linear and classical machine learning (ML) models, deep learning (DL) models offer a considerable improvement in data analysis due to their ability to extract more representative features and identify complex spatial patterns associated with big data. Hence, advancements in remote sensing, proliferation of big data, and deep learning architecture offer great potential for large-scale SOC mapping. However, there is paucity in literature on the application of DL-based remote sensing approaches for SOC prediction. To this end, this study is aimed at exploring DL-based approaches for the remote sensing of SOC stocks distribution across South Africa. The first objective sought to provide a synopsis of the use of traditional neural network (TNN) and DL-based remote sensing of SOC with emphasis on basic concepts, differences, similarities and limitations, while the second objective provided an in-depth review of the history, utility, challenges, and prospects of DL-based remote sensing approaches for mapping SOC. A quantitative evaluation between the use of TNN and DL frameworks was also conducted. Findings show that majority of published literature were conducted in the Northern Hemisphere while Africa have only four publications. Results also reveal that most studies adopted hyperspectral data, particularly spectrometers as compared to multispectral data. In comparison to DL (10%), TNN (90%) models were more commonly utilized in the literature; yet, DL models produced higher median accuracy (93%) than TNN (85%) models. The review concludes by highlighting future opportunities for retrieving SOC from remotely sensed data using DL frameworks. The third objective compared the accuracy of DL—deep neural network (DNN) model and a TNN—artificial neural network (ANN), as well as other popular classical ML models that include random forest (RF) and support vector machine (SVM), for national scale SOC mapping using Sentinel-3 data. With a root mean square error (RMSE) of 10.35 t/ha, the DNN model produced the best results, followed by RF (11.2 t/ha), ANN (11.6 t/ha), and SVM (13.6 t/ha). The DNN's analytical abilities, combined with its capacity to handle large amounts of data is a key advantage over other classical ML models. Having established the superiority of DL models over TNN and other classical models, the fourth objective focused on investigating SOC stocks distribution across South Africa’s major land uses, using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Findings show that grasslands contributed the most to overall SOC stocks (31.36 %), while urban vegetation contributed the least (0.04%). Results also show that commercial (46.06 t/h) and natural (44.34 t/h) forests had better carbon sequestration capacity than other classes. These findings provide an important guideline for managing SOC stocks in South Africa, useful in climate change mitigation by promoting sustainable land-use practices. The fifth objective sought to determine the distribution of SOC within South Africa’s major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep Neural Networks (CAE-DNN). Findings show that the CAE-DNN model (built from 26 selected variables) had the best accuracy of the DNNs examined, with an RMSE of 7.91 t/h. Soil organic carbon stock was also shown to be related to biome coverage, with the grassland (32.38%) and savanna (31.28%) biomes contributing the most to the overall SOC pool in South Africa. forests (44.12 t/h) and the Indian ocean coastal belt (43.05 t/h) biomes, despite having smaller footprints, have the highest SOC sequestration capacity. To increase SOC storage, it is recommended that degraded biomes be restored; however, a balance must be maintained between carbon sequestration capability, biodiversity health, and adequate provision of ecosystem services. The sixth objective sought to project the present SOC stocks in South Africa into the future (i.e. 2050). Soil organic carbon variations generated by projected climate change and land cover were mapped and analysed using a digital soil mapping (DSM) technique combined with space-for-time substitution (SFTS) procedures over South Africa through 2050. The potential SOC stocks variations across South Africa's major land uses were also assessed from current (2021) to future (2050). The first part of the study uses a Deep Neural Network (DNN) to estimate current SOC content (2021), while the second phase uses an average of five WorldClim General Circulation Models to project SOC to the future (2050) under four Shared Socio-economic Pathways (SSPs). Results show a general decline in projected future SOC stocks by 2050, ranging from 4.97 to 5.38 Pg, compared to estimated current stocks of 5.64 Pg. The findings are critical for government and policymakers in assessing the efficacy of current management systems in South Africa. Overall, this study provides a cost-effective framework for national scale mapping of SOC stocks, which is the largest terrestrial carbon pool using advanced DL-based remote sensing approach. These findings are valuable for designing appropriate management strategies to promote carbon uptake, soil quality, and measuring terrestrial ecosystem responses and feedbacks to climate change. This study is also the first DL-based remote sensing of SOC stocks distribution in South Africa

    Banda Aceh-The Value of Earth Observation Data in Disaster Recovery and Reconstruction: A Case Study

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    On 26 December 2004, Banda Aceh in Indonesia was at the center of one of the worst natural disasters to affect mankind. Large amounts of international aid poured in to assist in the relief and reconstruction efforts. Amongst this effort, were investments in basic earth observation data from in-situ, airborne and space observations. While the use of this data is assumed to be crucial, few efforts have gone into quantifying the benefits of its acquisition. The objectives of this study were to interview a cross-section of agencies operating in Banda Aceh and across the province of Nanggroe Aceh Darussalam on the use, sources and quality of earth observation data in the relief/reconstruction effort; and to analyze and quantify the value that earth observation data brings to the relief/reconstruction effort based on the survey results and specific examples. Key findings from the interviews point to an overall improvement in the spatial data situation since the tsunami. Problems identified included insufficient training, lack of timely data and sometimes poor spatial resolution. Specific examples of the cost-benefits of earth observation data were typically on the order of millions of dollars and involved large time savings. IIASA is one of 12 partners in the European Union sponsored project "Global Earth Observation/Benefit Estimation: Now, Next and Emerging" (GEO-BENE). Additional GEO-BENE partner countries include Germany, Switzerland, Slovakia, Netherlands, Finland, South Africa and Japan. Within GEO-BENE we are developing methodologies and analytical tools to assess societal benefits of GEO in nine societal benefit areas- one of which is disasters. The tsunami affected province of Nanggroe Aceh Darussalam, and specifically Banda Aceh, has been selected as a case study. Other case studies representing different societal benefit areas include: biodiversity in South Africa, health and climate in Finland, fire in Europe, etc. For more information please refer to: www.geo-bene.eu

    Using Machine Learning in Forestry

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    Advanced technology has increased demands and needs for innovative approaches to apply traditional methods more economically, effectively, fast and easily in forestry, as in other disciplines. Especially recently emerging terms such as forestry informatics, precision forestry, smart forestry, Forestry 4.0, climate-intelligent forestry, digital forestry and forestry big data have started to take place on the agenda of the forestry discipline. As a result, significant increases are observed in the number of academic studies in which modern approaches such as machine learning and recently emerged automatic machine learning (AutoML) are integrated into decision-making processes in forestry. This study aims to increase further the comprehensibility of machine learning algorithms in the Turkish language, to make them widespread, and be considered a resource for researchers interested in their use in forestry. Thus, it was aimed to bring a review article to the national literature that reveals both how machine learning has been used in various forestry activities from the past to the present and its potential for use in the future

    A Brief Review of Machine Learning Algorithms in Forest Fires Science

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    Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power
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