35 research outputs found
Presentation: Analysis Of Changes In Health Of Coastal Mangrove Forest On The East Coast Of Lampung
No full textThis material presented in ACHOST 202
Kesehatan hutan: penilaian kesehatan hutan menggunakan teknik forest health monitoring
No full textxiii, 101 hlm. : ilus. ; tab. ; 25 cm
SIMPANAN KARBON SEBAGAI SALAH SATU INDIKATOR KESEHATAN HUTAN PADA HUTAN RAKYAT (Studi Kasus di Hutan Rakyat Kelurahan Pinang Jaya, Kecamatan Kemiling, Kota Bandar Lampung, Provinsi Lampung)
No full textGlobal climate change and forest health are currently two things that need to be studied more deeply. Forests store carbon, including in the community forests. A healthy forest can perform its function properly including as a carbon sinker as well as a carbon repository. This study aims to analyze carbon storage as an indicator of forest health in community forests in Pinang Jaya Village. The data were collected by using cluster plots based on the method of Forest Health Monitoring (FHM), totaling 15 units. The measurement method used is destructive and non-destructive. The result showed that the average carbon storage in community forests in Pinang Jaya Village is 54.59 tC/ha. The largest contribution to carbon storage was AGB with a percentage of 95.71%, followed by necromass at 4.23%, and 0.05% litter and understorey. Based on the results of the analysis, carbon storage can perform as an indicator of forest health in the community forests of Pinang Jaya Village with the bad, medium, and good categories. The plot clusters are in the Good category (70.61 tC/ha - 83.66 tC/ha), namely cluster plots 13, Moderate categories (57.55 tC/ha - 70.60 tC/ha), namely cluster plots 4 and 14, and the Bad category (44.49 tC/ha - 57.54 tC/ha), namely cluster plots 1-3, 5-12.15, with the percentage of each category of 7%, 13%, and 80%
The Trend in Types of Tree Damage in Mangrove Forest Management Areas, East Lampung Regency
No full textDamage to mangrove trees is one of them influenced by the types of tree damage. The more types of tree damage to a single mangrove tree, the worse the health level of the mangrove tree will be. This study aims to obtain damage index trend values and types of tree damage in the east coast mangrove forest area, East Lampung Regency. Measurement of the types of damage to mangrove trees was carried out three times using the Forest Health Monitoring (FHM) method in two FHM cluster plots for each mangrove forest area. Assessment of damage to mangrove trees uses the Damage Index formula. The results showed that the trend index values for mangrove tree damage were 9.3 and 4.5 (Resort Kuala Penet, Way Kambas National Park), 9.2 and 19.8 (Margasari, Labuhan Maringgai), and 6.2. and 9.8 (Purworejo, Pasir Sakti) with the dominant types of damage being open wounds (code 03) 343 damage, damaged foliage/shoots (code 24) 240 damage, and broken/dead (code 22) 158 damage. Thus, the trend of damage index and types of damage to mangrove trees in the east coast mangrove forest area of East Lampung Regency has increased
Klasifikasi Skala Kerapatan dan Transparansi Tajuk Jenis Daun Jarum dengan VGG16: Classification of Density and Transparency Scales of Needle Leaf Types with VGG16
No full textArtikel ini membahas penggunaan deep learning, khususnya arsitektur Convolutional Neural Network (CNN) VGG16, untuk mengklasifikasikan tingkat kerapatan dan transparansi tajuk pada pohon jenis daun jarum. Penelitian ini mengumpulkan gambar dari empat jenis pohon daun jarum: araucaria heterophylla, pinus merkusii, cupressus retusa, dan shorea javanica, masing-masing dengan sepuluh tingkat kerapatan dan transparansi yang berbeda. Setiap jenis memiliki 1000 gambar yang telah di-label. Proses preprocessing melibatkan perubahan ukuran, dan augmentasi gambar. Data dibagi menjadi data training (70%), data validation (10%), dan data testing (20%). Model deep learning yang digunakan adalah VGG16 dengan hyperparameter yang telah ditentukan. Hasil pelatihan model menunjukkan bahwa VGG16 berhasil mengklasifikasikan pohon daun jarum dengan tingkat akurasi yang baik. Hasil akurasi mencapai 90.00% untuk pinus merkusii, 92.00% untuk araucaria heterophylla, 96.00% untuk cupressus retusa, dan bahkan 99.00% untuk shorea javanica. Hasil evaluasi juga mencakup precision, recall, dan F1-score untuk setiap kelas kerapatan dan transparansi. Kesalahan prediksi terutama terjadi pada kelas dengan tingkat kesamaan visual yang tinggi antar gambar. Penelitian ini membuktikan bahwa teknologi deep learning dapat digunakan untuk mengklasifikasikan tingkat kerapatan dan transparansi tajuk pada pohon daun jarum. Hasilnya dapat digunakan dalam pemantauan kesehatan hutan, membantu pemerintah dan organisasi terkait dalam pengelolaan hutan yang berkelanjutan
Implementasi Metode CNN Computer Vision Dalam Identifikasi Tipe Kerusakan Pohon Berbasis FHM
No full textIdentifikasi tipe kerusakan pohon pada Forest Health Monitoring hingga saat ini masih bersifat manual, yaitu menggunakan penglihatan manusia dalam penerapannya. Teknologi Informasi yang kini berkembang pesat dapat di rasakan hingga ke berbagai media penerapan ilmu pengetahuan, dengan demikian terciptalah salah satu solusi dalam memecahkan masalah penelitian kasus identifikasi tipe kerusakan pohon yaitu dengan penggunaan metode computer vision dalam upaya memudahkan pekerjaan dalam ilmu kehutanan. Tujuan penelitian ini adalah untuk menerapkan computer vision dalam mengidentifikasi tipe kerusakan pohon berbasis Forest Health Monitoring. Tahapan penelitian yang dilakukan dalam penelitian ini adalah pengumpulan dataset, proses preprocessing, pembagian dataset, pelatihan model, pengujian model dan terakhir adalah evaluasi model. Hasil penelitian ini berupa model (prototype) identifikasi tipe kerusakan pohon dalam 4 kategori yaitu, LeNet-5 Colab, LeNet-5 Tesla, MobileNet Colab, dan MobileNet Tesla. Persentase identifikasi model bervariasi, dimana pada kelas tertentu model dapat mengidentifikasi dengan benar dan dikelas lainnya masih ada beberapa identifikasi model yang kurang optimal, disebabkan oleh kemiripan beberapa bentuk dataset dalam segi visual komputer. Penelitian penerapan computer vision dalam identifikasi kerusakan pohon berbasis Forest Health Monitoring berhasil dilakukan dengan menghasilkan dua model (prototype) dalam identifikasi kerusakan pohon yang nilai akurasinya mencapai angka 89.99% pada model LeNet-5 dan 99.06% pada model MobileNet dengan tools yang digunakan adalah Jupyter notebook pada Nvidia Tesla K20 (offline) dan Google Colab (online)
Penilaian Status Kesehatan Hutan Mangrove Di Desa Margasari Kecamatan Labuhan Maringgai Kabupaten Lampung Timur
No full textMangrove forest ecosystems can be interpreted as a unique and distinctive form of ecosystem, so that it is able to provide many benefits, ranging from socio-economic or ecological terms to the surrounding ecosystem. Mangrove forest in Margasari Village is a mangrove forest ecosystem that has physical, economic and ecological potential that needs to be maintained through sustainable forest management. One of the ways to manage mangroves is by monitoring forest health. Forest health monitoring that is applied periodically within a forest type can achieve sustainable forest management achievements so as to support better forest quality and quantity and can be a reference in making the right decisions in mangrove forest management so that the results obtained can be optimal. This study aims to obtain the value of the health status of mangrove forests in East Lampung Regency in order to ensure the sustainability of the forest. The study was conducted using themethod Forest Health Monitoring (FHM). The results of forest health monitoring showed that there were 4 plot clusters with the final value of forest health status in the medium category plot 1 (5.63), cluster plot 2 (3.51) poor category, cluster plot 3 (4.92) poor category, and cluster plot 4 (7.57) in good category. Thus the results of forest health monitoring obtained in the mangrove forest of Margasari Village with an average final value of forest health status of 5.41 which is included in the medium category
ANALISIS SPASIAL POTENSI HUTAN RAKYAT DI KABUPATEN BOGOR
No full textIn order to support the availability of wood raw material and increase the local economy in Bogor, one of the potential that can be developed is a community forest. Although the data and information about community forest areas not yet clearly. Potential data in community forest can get through a spatial approach using remote sensing and geographic information systems. Spatial analysis of the community forest potential was conducted to determine the distribution, extent and type of community forest cover in the district of Bogor. The main data used is Landsat 8 OLI recording of 2015. Processing and analysis of data in this study include, land cover classification, classification of vegetation index and analysis of overlay. Based on the analysis results, community forest area is about 28.351,4 ha spread over 40 districts of Bogor. The largest community forest cover types is a type of agroforestry, and the smallest type of polyculture is the type of monoculture. Based on vegetation index, community forest with a high density is larger than community forest with low density
Tingkat partisipasi masyarakat dalam pengelolaan hutan di HKM Harapan Sentosa KPHL Batutegi
No full textPartsipasi masyarakat dalam pengelolaan hutan sangat penting bagi keberhasilan program Pengelolaan Hutan Berbasis Masyarakat (PHBM). Partisipasi komunitas berdampak positif pada kegiatan dari tahap perencanaan sampai tahap evaluasi, operasi pengelolaan hutan. Penelitian ini bertujuan untuk mengukur sejauh mana keterlibatan masyarakat dalam pengelolaan hutan di HKm Harapan Sentosa. Penentuan responden dilakukan dengan teknik purposive sampling menggunakan key actors yang terdiri dari ketua, pengurus inti serta anggota kelompok di HKm Harapan Sentosa. Pengolahan data tingkat partisipasi ini menggunakan skala likert. Hasil penelitian diperoleh data rata-rata tingkat partisipasi anggota masyarakat gapoktan di HKm Harapan Sentosa termasuk kedalam kategori sedang dengan rentang nilai 40-55. Hal ini menunjukkan bahwa masyarakat Gapoktan belum terlibat langsung dalam setiap kegiatan pengelolaan hutan yang dimulai dari tahap perencanaan sampai dengan tahap pemantauan dan evaluasi
Assessment of Forest Health in Various Forest Types in Lampung Province
No full textIn Lampung Province, awareness of the importance of forest health in achieving sustainable forest management in various types of forests is still low so that forest health problems have not received serious attention so far. This study aims to obtain indicators of forest health assessment and the status of forest health conditions in various types of forests in Lampung Province. This research was carried out in mangrove and community forests in East Lampung District, and protected and conservation forests in Tanggamus District in 2018. The stages of this study consisted of formulating guarantees of forest health indicators, making measuring plots, measuring forest health, processing data, and forest health assessment. The results showed that indicators for assessing the health of forests in mangrove forests are vitality and biodiversity, in community forests are productivity, vitality and site quality, in protected forests are biodiversity, vitality and productivity, and in conservation forests are biodiversity and productivity. The status of health conditions in each cluster of plots in mangrove forest is bad and good, in community forests is good and medium, in protected forests is bad and good, and in conservation forests are bad and good.Keywords: indicator, forest health status, forest types, Lampung Provinc
