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Satellite images provide an accurate, continuous, and synoptic view of seamless global extent. Within the fields of remote sensing and image processing, land surface change detection (CD) has been amongst the most discussed topics. This article reviews advances in bitemporal and multitemporal two-dimensional CD with a focus on multispectral images. In addition, it reviews some CD techniques used for synthetic aperture radar (SAR). The importance of data selection and preprocessing for CD provides a starting point for the discussion. CD techniques are, then, grouped based on the change analysis products they can generate to assist users in identifying suitable procedures for their applications. The discussion allows users to estimate the resources needed for analysis and interpretation, while selecting the most suitable technique for generating the desired information such as binary changes, direction or magnitude of changes, “from-to” information of changes, probability of changes, temporal pattern, and prediction of changes. The review shows that essential and innovative improvements are being made in analytical processes for multispectral images. Advantages, limitations, challenges, and opportunities are identified for understanding the context of improvements, and this will guide the future development of bitemporal and multitemporal CD methods and techniques for understanding land cover dynamics.

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As a region developed, the increase of population increase the quality and quantity of life standard and facilities required, which cause land use changes.The objectives of the study were: (1) to observe the changing pattern of land use of Bekasi city in 2003 and 2010, (2) to identify the land use inconsistencies based on space allocation in the Regional Spatial Plan (RTRW) period of 2000-2010, (3) to identify the regional development of Bekasi city in 2003 and 2006, and (4) to determine the factors influencing the land use change. The methods used include spatial analysis, inconcistency, skalogram, and multiple regression analyses. The built up area of Bekasi in years of 2003-2010 had increased significantly. It correlated to development of education facilities, industrial area, disordered and ordered settlements from 10,188 ha (47.5%) to 12,061 ha (55.8%). Inconsistence of empirical land use to land allocation of Bekasi was 301 ha in 2003 increased to 377 ha in 2010. The greatest proportion of the inconsistence to Regional Spatial Plan in 2003 and 2010 occurred on land allocated for city garden which factually were used as built up area and agricultural land. In 2003, most of villages possessed 3 rd hierarchy (48%), and in 2006 they were mostly 2 nd hierarchy (46%). The Factors significantly influenced land use change in Bekasi were allocation for built up area, allocation for agriculture and for city park/forest, hectarage of paddy field, mixed garden, dryland and open space in 2003, distance to another town or suburban, number of additional educational, health, and social facilities, distance to the center of social facilities, to the civic, to the center of economic facilities and population growth, respectively.

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This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for post-classification change analysis. The accuracy of change detection was iteratively evaluated based on thresholds composed of mean and a range constant of standard deviation. Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected texture features. Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN), Bagged Multivariate Adaptive Regression Spline (MAB), Regularised Random Forest (RFG), Original Random Forest (RFO), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGB). The best accuracy was 98.8%, which resulted from thresholding MAD variate-2 with constants at 1.7. The highest improvement of classification accuracy was obtained by amending the grey level co-occurrence matrix (GLCM) texture. The identification of variable importance (VI) confirmed that selected GLCM textures (mean and variance of HH or HV) were equally superior, while the contribution of index and decomposition were negligible. The best model produced similar classification accuracy at about 90% for both years 2007 and 2010. Tree-based algorithms including RFO, RFG and XGB were more robust than SVM and ENN. Subtle changes indicated by binary change analysis were somewhat hidden in post-classification analysis. Reclassification by combining all important variables and adding five classes to include subtle changes assisted by Google Earth yielded an accuracy of 82%.

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Kepulauan Meranti Regency is a district which (formerly part of) Bengkalis. It was established after Law No. 12, dated January 16, 2009. Since the district is newly developed agricultural development policy is still unavailable. Many land related aspects should be considered in order to have more effective and efficient land utilization. This research aims: (1) to know potential commodity of Kepulauan Meranti Regency, (2) to analyze land suitability for various agricultural commodities, (3) to review and analyze the availability of potential land to develop various agricultural commodities, and (4) to recommend the direction of agricultural commodities development. Results showed that suitable and available land for development of agricultural commodities is about 108,277 ha. The District Merbau is suitable and available for sago (plantation), cassava (food crops), guava (fruits) and great chili, cayenne pepper and cucumber (vegetables). Pulau Merbau is suitable and available for rubber and nut (plantation). Rangsang is suitable and available for sapodilla and pineapple (fruits). Rangsang Barat is suitable and available for coffee and nut (plantation), banana, papaya and mangosteen (fruits). Tebing Tinggi is suitable and available for sweet potatoes and corn (food crops) and great chili, cayenne pepper, chickpea and cucumber (vegetables). Tebing Tinggi Barat is suitable and available for sago (plantation), sweet potatoes (food crops) and papaya, breadfruit and pineapple, while District Tebing Tinggi Timur is mostly suitable for sago plantation. ABSTRAKKabupaten Kepulauan Meranti merupakan kabupaten hasil pemekaran dari Kabupaten Bengkalis. Dasar hukum berdirinya Kabupaten Kepulauan Meranti adalah Undang-undang Nomor 12 Tahun 2009, tanggal 16 Januari 2009. Mengingat usia kabupaten ini yang relatif muda, arahan pengembangan komoditas pertanian belum tersedia secara lengkap. Banyak aspek lahan yang harus diperhatikan agar pemanfaatan atau penggunaan lahan untuk pengembangan komoditas wilayah lebih efektif dan berdaya guna. Tujuan penelitian ini adalah: (1) Mengetahui komoditas unggulan di Kabupaten Kepulauan Meranti, (2) Menganalisis kesesuaian lahan untuk pengembangan berbagai komoditas pertanian, (3) Mengkaji dan menganalisis ketersediaan lahan yang berpotensi pengembangan untuk berbagai komoditas pertanian, dan (4) Menyusun arahan pengembangan komoditas pertanian. Hasil penelitian menunjukkan luas lahan sesuai dan tersedia di Kabupaten Kepulauan Meranti untuk pengembangan komoditas unggulan sebesar 108,277 ha. Komoditas yang dapat dikembangkan tiap kecamatan adalah sebagai berikut: Kecamatan Merbau adalah sagu (tanaman perkebunan), ketela pohon (tanaman pangan), jambu biji (buah-buahan) dan cabe besar, cabe rawit dan ketimun (sayur-sayuran); Pulau Merbau adalah karet dan pinang (tanaman perkebunan); Rangsang adalah sawo dan nenas (buah-buahan). Rangsang Barat adalah kopi dan pinang (tanaman perkebunan), pisang, pepaya dan manggis (buah-buahan); Tebing Tinggi adalah ketela rambat dan jagung (tanaman pangan) dan c...