Perbandingan Pengolahan DAS Bengkulu Menggunakan NDVI dan Maximum Likelihood

DAS adalah daerah daratan yang merupakan bagian integral dari sungai dan anak-anak sungainya. DAS berfungsi untuk menampung, menyimpan dan mengalirkan air yang berasal dari curah hujan ke danau atau ke laut secara alami, batas di darat adalah pemisah topografi dan batas di laut ke perairan yang masih dipengaruhi oleh aktivitas darat. Pemetaan tutupan lahan di daerah aliran sungai penting untuk memahami masalah yang terjadi di daerah aliran sungai seperti kualitas air yang menurun dan rentan terhadap tanah longsor atau banjir. Dalam studi ini, pemetaan area penutup DAS Rindu Hati dilakukan dari 2014 hingga 2018 dengan menggunakan citra satelit Landsat 8 menggunakan metode Maximum Likelihood dan NDVI. Peta tutupan lahan kemudian diproses dan ditampilkan menggunakan media webGIS. Penelitian ini diharapkan dapat menjadi bahan pertimbangan untuk pengambilan keputusan dalam pengelolaan DAS Rindu Hati di masa depan untuk mendukung lingkungan yang berkelanjutan. Selain itu, penelitian ini menunjukkan bahwa kinerja algoritme kemungkinan maksimum menghasilkan akurasi yang lebih baik (95,81%) daripada hasil yang dihasilkan oleh NDVI (92,85%) untuk proses klasifikasi di DAS Rindu Hati. Pengujian dilakukan ke dalam 100 titik data acak dari hasil klasifikasi dalam kegiatan pemeriksaan lapangan. Kemungkinan maksimum juga menunjukkan waktu pemrosesan yang lebih baik untuk klasifikasi 5 kelas pada nilai rata-rata 0,023 detik daripada algoritme NDVI yang menunjukkan nilai rata-rata 0,031 detik.Kata Kunci: DAS, maximum likelihood, NDVI, remote sensing, webGIS.

Web-GIS mapping for watershed and land cover area in Bengkulu

Naturally, watershed has a function to accomodate and store water from rainfall to the lake or to the sea. It is affected by land activities and shaped as a boundary of topographic land and as a separator from the sea. Managing the area is one of essential steps for further environment assessement, i.e. decreasing water quality and vulnerability to landslides or floods. However, the ability of web-GIS technology to help the management of watershed area in Bengkulu is still unknown. Here, we showed how the process of Web GIS development for managing the watershade in Bengkulu could be achived. This study mapped the 2018 land cover area of Rindu Hati Sub-watershed by utilizing Landsat 8 satellite images using the Maximum Likelihood method. The land cover map was then processed and displayed using webGIS media. The results showed that the system accuracy for ground truth land use model result was 77.4% which could be accepted as a good result. Further assessment of pixel validation could be one of the future research. We anticipated that the results could be a starting point for more sophisticated area cover of Sumatra and Indonesia. Furthermore, this could be a major development on knowledge discovery in environment and ecology

PENINGKATAN KAPASITAS LSM TIGER HEART DALAM MELAKUKAN ANALISIS TUTUPAN LAHAN

Abstrak: Deforestasi yang terjadi di Taman Buru Semidang Bukit Kabu (SBK) Provinsi Bengkulu didominasi oleh alih fungsi lahan menjadi perkebunan. Masifnya pembukaan perkebunan dengan cara penebangan hutan secara liar membuat hutan SBK kehilangan fungsinya sebagai ekosistem serta habitat bagi harimau sumatera. Kegiatan pengabdian pada masyarakat berbasis riset ini bertujuan untuk membantu Lembaga Swadaya Masyarakat, Tiger Heart sebagai mitra dalam pengabdian ini, dalam mendukung upaya konservasi yang menjadi kegiatan utama mereka. Peningkatan softskill kepada lima orang mitra diharapkan dapat memberikan dampak lebih luas bagi penggiat konservasi melalui penyediaan data analisa tutupan lahan. Pelaksanaan pengabdian ini terbagi menjadi tiga tahap yaitu pengumpulan data, pelaksanaan pelatihan, dan evaluasi. Pengumpulan data dilakukan dengan melakukan survey lapangan untuk mengetahui kondisi aktual di wilayah hutan SBK. Hasil pemantauan di lapangan diketahui bahwa para perambah hutan melakukan perubahan wilayah hutan menjadi lahan pertanian yang disebabkan oleh kebutuhan ekonomi, rendahnya pendidikan, serta kurangnya pengetahuan dan kesadaran mengenai batas dan fungsi hutan SBK sebagai habitat harimau sumatera. Setelah itu dilakukan pelatihan bagi mitra untuk menganalisis perubahan tutupan lahan melalui perangkat lunak dan data remote sensing. Berdasarkan empat kriteria pengukuran yang terdiri dari pengalaman penggunaan aplikasi, pengetahuan ketersediaan data, kemampuan analisis, dan kemandirian, terjadi peningkatan rata-rata 45% pada mitra yang telah dilatih.Abstract: The deforestation of Taman Buru Semidang Bukit Kabu (SBK) in Bengkulu Province has been caused by the conversion of land into plantations. Unfortunately, this has resulted in the loss of the SBK forests' role as an ecosystem and habitat for Sumatran tigers due to illegal logging and mass clearing of plantations. This research-based community service initiative aims to support the conservation efforts of Non-Governmental Organizations like Tiger Heart. The objective of our project endeavours to augment the interpersonal abilities of our five partner members and furnish conservationists with comprehensive land cover analysis data. To achieve this, we conducted field surveys to gather data that revealed forest encroachers converted forest areas into agricultural land due to economic needs, low education, and a lack of awareness about the boundaries and functions of SBK forests as Sumatran tiger habitats. This program provided training to our partners on how to analyze land cover changes through remote sensing software and data. The training resulted in an average improvement of 45% in partner's knowledge and skills, measured through application experience, data availability, analytical skills, and independence. Our ultimate goal is to support Tiger Heart's conservation efforts and make a positive impact on the conservation of Sumatran tigers and their habitat.

Resort Based Management Web GIS Towards Cyber Conservation in Indonesia

Biodiversity loss is a global issue and is especially of pressing concern in mega diverse countries, such as Indonesia. To prevent any further catastrophe, the Ministry of Forestry and Environment of Republic of Indonesia has been promoting the resort based management to be implemented in Indonesia to maximize the performance of conservation activity. The lack of data standardization made it hard to organize and manage archipelagic country that consist 17,504 islands with no technology provision in most of them. In this paper we develop a framework of integration mobile-web technology for biodiversity and conservation in Indonesia. We introduced a new framework to maintain the biodiversity and conservation data in Indonesia

Automation Mangrove Identification with Case Based Reasoning Process

Mangroves are ecosystems with unique functions in the environment. Because of its physical properties, mangroves are able to play a role as a wave retardant as well as retaining intrusion and abrasion of the sea. Mangroves themselves have various types of species that are spread throughout Indonesia and not yet widely known to people in general. In identifying the mangrove species itself cannot be done arbitrarily, it requires an expert who truly understands the mangrove species. This research was conducted with the aim of adopting the knowledge of mangrove experts to identify mangrove species into expert systems. The method used is case based reasoning method using the KNN algorithm which is used to calculate the similarity value between cases that will be applied to the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu. This system is built using HTML, CSS, Javascript, Php, and Mysql programming languages and is designed using UML diagrams. The results of this study itself are, it has been successfully applied the case based reasoning method in the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkul

Mining Fire Hotspots Over Nusa Tenggara and Bali Islands

Forest fires are still one of the most common problems in Indonesia. In fact, many of these forest fires origin from human activities, namely fires that are intentionally raised for a purpose such as widening the land to prepare for the planting season in the Nusa Tenggara Island. Forest fire events can be identified by observing hotspot data which are monitored through remote sensing satellites. Hotspot is an area that has a relatively higher surface temperature than the surrounding area based on certain temperature thresholds monitored by remote sensing satellites. The area is represented as a point that has certain coordinates. The actual fires can be monitored by observing the hotspot attribute, namely Confidence, Brightness Temperature and FRP (Fire Radiate Power). To find the similarities of the three mentioned attributes, the clustering process is carried out to make monitoring easier. The objective of this research is to cluster hotspots in the Nusa Tenggara and Bali Islands from year 2013 to 2018 using the K-Means Clustering Method with 28,519 hot spot data. This could be a benefit for the Ministry of Environment and Forestry in Indonesia to identify the priority level of the area to be monitored. By knowing this result, the ministry can use this data for patrol priority management. This research successfully clustered three types of hotspot classes based on the risk of fire with details as follow; High Risk Class contains 12,212 data with ranges of mean values of confidence in the range of 49.3–100%, brightness in the range of 305.1–421.3o K and FRP in the range of 2.5–714.3; Medium Risk contains 12,250 data mean values of confidence with a range of 20.3–74.3%, brightness in the range of 301.06–341.86o K and FRP in the range of 3.6–141.4; and Low Risk contains 4,057 data with a range of mean values of confidence in the range of 0–39.8%, brightness in the range of 300–365.86oK and FRP in the range of 3.5–275.6. All of the clusters were obtained by the implementation of K-Means clustering over the hotspot data and its parameter as mentioned, respectively. The cluster performance showed the confidential value of 88.45% accuracy using 100 hotspot data from 201