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"Rainfall interception losses were monitored for 12 months and related to vegetation and rainfall characteristics at the Wanariset Sangai on the upper reaches of Mentaya river. The rainfall interception losses were quantified based on the records of 55 selected rainfall events in the unlogged forest and of 95 rainfall events in the logged-over area. In the unlogged forest, the total amount of rainfall interception loss was 251 mm or about 11% while in the logged forest, it was 219 mm or 6%. It was also simulated by the Gash’s original and revised models. Both models adequately predicted total interception loss over a long period, resulting in estimates of the interception loss deviated by 6-14% for both forests."

"Rainfall interception losses were monitored for six months and related to vegetation and rainfall characteristics at the BPK-ODA research site in the upper stream of Mentaya river, Central Kalimantan. The rainfall interception losses were quantified based in the records of 52 selected rainfall events within the range of 8.5-135.5 mm. Over 6 months in one hectare pristine tropical rainforest, 217 mm (11%) of 1990 mm gross rainfall occurring in the research was loss to the atmosphere. Canopy through fall equaled 1745 mm (87.7%) of gross rainfall whereas stem flow represented 28 mm (1.3%). These findings are comparable to interception studies carried out in Sabah and Amazonian rainforest."

"Amerika Seriitat telah menjadi pemeran utama dalamindustri kayu. Kondisi tersebut telah menjadi beban bagipemerintah Amerika Serikat oleh karena meningkatanyatekanan dari para ahii di bidang lingkungan, yang lebihmendukung dilaksanakannya reboisasi dibandingkandeboisasi. Pergeseran peran dari pengekspor kayuutama menjadi pengimpor kayu, telah menyebabkanpasar nasional Amerika Serikat mengalami kerugian,seperti meningkatnya harga produk kayu. Konflik antaraAmerika Serikat dengan Kanada berkaitan denganpraduk kayu telah menarik banyak perhatian dari parastakeholder, dan telah membuat Pemerintahan Bushmeresmikan saatu kebijakan perkayuan yang didalamnya diatur mengenai pembaukaan hutan kembaiidan program hutan sehat. Artikel ini mencoba menbahasmengenai situasi seputar kebijakan Amerika Serikatmengenai praduksi kayu."

"The main effect of logging is to create an artificial disturbance regime resulting in canopy gaps and disturbance to soil, seedlings and the forest understory. The effects of logging on the physical environment of a lowland dipterocarp forest in Central Kalimantan are being investigated. A range of approaches are being applied to characterize these impacts. The effect of logging on cnopy cover, seedling density and soil disturbance are recorded to produce low resolution maps of the plots before and after logging. More detailed information about changes in canopy structure is determined using image analysis of hemispherical canopy photographs. "

"Ketika pemerintah membentuk Provinsi Kalimantan Tengah berdasarkan Undang-Undang Darurat Nomor 10 Tahun 1957, Pahandut yang dipilih sebagai ibu kota belum siap melaksanakan fungsinya tersebut karena belum dibangunnya sarana dan prasarana yang representatif. Sambil menunggu pembangunan di Pahandut, untuk sementara waktu, ibu kota berkedudukan di Banjarmasin. Pada 1959, kedudukan Pemerintah Daerah Kalimantan Tengah dari Banjarmasin pindah ke Palangkaraya, yaitu nama baru Pahandut. Sesuai dengan kedudukannya sebagai ibu kota Provinsi Kalimantan Tengah, maka pemerintah daerah segera melakukan pembenahan Palangkaraya, dimulai dari pemekaran wilayah administrasi, pembentukan kecamatan, dan penataan kampung-kampung. Sesuai Undang-Undang Nomor 1 Tahun 1957, Palangkaraya merupakan kotapraja administratif, yaitu kota yang berfungsi sebagai pusat pemerintahan wilayah atau daerah tertentu. Oleh karena keinginan untuk mengatur rumah tangganya sendiri secara mandiri, pemerintah daerah Kotapraja Administratif Palangkaraya berusaha meningkatkan statusnya menjadi kotapraja otonom. Usaha ini terwujud pada 1965 dengan dikeluarkannya Undang-Undang Nomor 5 tahun 1965.

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When the government of Republic of Indonesia established the Province of Central Borneo by Emergency Law No. 10 Year 1957, Pahandut chosen as the capital is not ready to perform its function as the construction of facilities and infrastructure has not representative. While waiting for construction in Pahandut, for a time, the capital located in Banjarmasin. In 1959, the seat of the local government of Central Borneo moved to Palangkaraya, the new name for Pahandut. In keeping with its position as the capital of Province of Central Borneo, the local government immediately make city reform of Palangkaraya, starting from the expansion of administrative region, the formation of the district, and the arrangement of the villages. As enumurated under Law No. 1 Year 1957, Palangkaraya is administrative municipality, i.e. the city who serves as the administrative center of the region. Because of the people aspiration to set up their own household independently, Administrative Municipality of Palangkaraya try to improve the status to a autonomous municipality. This effort was realized in 1965 under Law No. 5 Year 1965."

"Kebakaran hutan telah mengakibatkan kerugian pada aspek ekologis, sosial dan ekonomi. Indonesia menjadi salah satu negara yang sering terjadi kebakaran hutan. Kebakaran hutan di Indonesia paling signifikan terjadi pada lahan gambut. Untuk mencegah kejadian kebakaran hutan dan mengurangi tingkat kerugian jika terjadi kebakaran hutan, perlu dilakukan pemetaan prediksi potensi kerentanan kebakaran hutan. Dalam beberapa tahun terakhir, metode Convolutional Neural Network (CNN) telah menjadi state-of-the-art algoritma deep learning dan banyak diimplementasikan pada berbagai bidang diantaranya computer vision. Oleh sebab itu, penelitian ini mengusulkan model prediksi spasial kerentanan kebakaran hutan dengan menambahkan data kedalaman gambut yang digabungkan dengan data Topografi, Pengaruh Manusia, Iklim dan Vegetasi. Selanjutnya hasil data fusion dimodelkan menggunakan Alexnet yang berbasis CNN. Model yang diusulkan juga dibandingkan dengan algoritma machine learning yang sudah umum yaitu Random Forest (RF), Gradient Boosting Machine (GBM), dan Support Vector Machine (SVM) dan Deep Neural Network (DNN). Sebagai studi kasus, digunakan data kebakaran hutan di Pulang Pisau, Kalimantan Tengah dari tahun 2014 hingga 2019 dan 11 variabel pendukung kebakaran hutan yang diturunkan dari faktor penyebab kebakaran hutan. Terakhir, performa model diuji dengan menggunakan beberapa metode statistik yaitu uji akurasi, Area Under Curve (AUC) dan Wilcoxon Signed Rank Test (WSRT). Hasilnya modifikasi arsitektur Alexnet menghasilkan performa akurasi paling tinggi yaitu (0,94) dibandingkan dengan RF (0,90), DNN (0,89), GBC (0,89), SVC (0,89). Selain itu dari uji perbedaan statistik menunjukkan bahwa proposed model berbeda dibandingkan model pembandingnya. Hasil tersebut mengungkapkan bahwa CNN mampu mengungguli model yang sudah umum, oleh sebab itu dapat digunakan sebagai alternatif model prediksi indeks kerentanan kebakaran hutan.

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Forest fires have resulted in losses in ecological, social and economic aspects. Indonesia is one of the countries with frequent forest fires. The most significant forest fires in Indonesia occur on peatlands. To prevent forest fires and reduce the level of losses in the event of forest fires, it is necessary to develop prediction map of potential forest fire susceptibilities. In recent years, the Convolutional Neural Network (CNN) method has become a state-of-the-art deep learning algorithm and is widely implemented in various fields including computer vision. Therefore, this study proposes a spatial prediction model for forest fire susceptibility by adding peat depth data combined with topography, human influence, climate and vegetation data. Furthermore, the results of the data fusion are modeled using a modified Alexnet based on CNN. The proposed model is also compared with common machine learning algorithms, namely Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM) and Deep Neural Network (DNN). As a case study used data on forest fires in Pulang Pisau, Central Kalimantan from 2014 to 2019 and 11 variabels supporting forest fires derived from the factors causing forest fires. Finally, the performance of the model was tested using several statistical methods, namely accuracy test, Area Under Curve (AUC) and Wilcoxon Signed Rank Test (WSRT). The result is that the Alexnet architecture modification produces the outstanding accuracy performance (0,94) compared to RF (0,90), DNN (0,89), GBC (0,89), SVC (0,89). In addition, the statistical difference test shows that the proposed model is different from the comparison model. These results conclude that CNN is able to outperform the benchmark model, therefore it can be used as an alternative model for predicting forest fire susceptibility index."

"Kawasan Segara Anakan merupakan habitat mangrove yang masih lengkap berdasarkan formasi vegetasinya. Keberadaan mangrove mempunyai manfaat bagi masyarakat pesisir. Penelitian ini dilaksanakan pada bulan Desember 2010 sampai Januari 2011. Penelitian ini untuk melihat perubahan secara kuantitatif dan spasial, prediksi trend perubahan luasan hutan mangrove, dan tingkat partisipasi masyarakat dalam pelestarian mangrove di Kawasan Segara Anakan, Kelurahan Kotawaru, Cilacap, Jawa Tengah, Indonesia. Metode yang digunakan dalam penelitian yaitu analisis Overlay dan diskriptif kuantitatif dengan pendekatan survei. Analisis statistik yang digunakan adalah Regresi Linear Sederhana dan Berganda. Hasil penelitian menunjukkan terjadi penyusutan luasan hutan mangrove, tahun 1991 (5.900 ha), tahun 2001 (5.200 ha), tahun 2005 (3.900 ha), dan tahun 2010 (3.600 ha). Berdasarkan persamaan linear sederhana, di prediksi luasan hutan di Kawasan Segara Anakan akan menjadi 0 ha pada tahun 2033. Berdasarkan persamaan linear berganda menunjukkan bahwa terdapat hubungan positif antara pendidikan, pendapatan, dan pengetahuan masyarakat terhadap partisipasi dalam pelestarian hutan mangrove di Kelurahan Kotawaru, Cilacap, Jawa Tengah.

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Segara Anakan Region is the habitat of mangrove which is still complete based on vegetation formation. The existence of mangrove has some advantages for the coastal inhabitants. The research has been accomplished in December 2010 - January 2011. The objectives of the research are to find out the quantitative and spatial changes, and the prediction in the change of mangrove area, and is to see the community's participation in conserving the mangrove in Segara Anakan Region, Kotawaru, Cilacap, Central Java, Indonesia. The method used in the research are analysis of overlay, descriptive with Surveys approach. The statistic analysis used is Simple and Multiple Regression Linear.

The research indicated that the mangrove area has decreased, in year 1991 (5.900 ha), 2001 (5.200 ha), 2005 (3.900 ha), and 2010 (3.600 ha). Based on the Simple Linear Equation, the mangrove will be predicted to become 0 ha in 2033. Based on the statistic analyses indicates that there is a positive relation between education, income, community?s knowledge to the community?s participation in conserving mangrove in Kotawaru, Cilacap, Central Java, Indonesia."