In 1997, the worst forest fire in Indonesia occurred and hit mangrove forest areas including in Sembilang National Park Banyuasin Regency, South Sumatra. Therefore, the Indonesian government keeps in trying to rehabilitate the mangrove forest in Sembilang National Park. This study aimed to identify the mangrove forest changing and to predict on the future year. The situations before and after forest fire were analyzed. This study applied an integrated Markov Chain and Cellular Automata model to identify mangrove forest change in the interval years of 1989–2015 and predict it in 2028. Remote sensing technology is used based on Landsat satellite imagery (1989, 1998, 2002, and 2015). The results showed mangrove forest has decreased around 9.6% from 1989 to 1998 due to forest fire, and has increased by 8.4% between 1998 and 2002, and 2.3% in 2002–2015. Other results show that mangroves area has continued to increase from 2015 to 2028 by 27.4% to 31% (7974.8 ha). It shows that the mangrove ecosystem is periodically changing due to good management by the Indonesian government.
Agricultural crop abandonment negatively impacts local economy and environment since land, as a resource for agriculture, is not optimally utilized. To take necessary actions to rehabilitate abandoned agricultural lands, the identification of the spatial distribution of these lands must be acknowledged. While optical images had previously illustrated potentials in the identification of agricultural land abandonment, tropical areas often suffer cloud coverage problem that limits the availability of the imageries. Therefore, this study was conducted to investigate the potential of ALOS-1 and 2 (Advanced Land Observing Satellite-1 and 2) PALSAR (Phased Array L-band Synthetic Aperture Radar) images for the identification and classification of abandoned agricultural crop areas, namely paddy, rubber and oil palm fields. Distinct crop phenology for paddy and rubber was identified from ALOS-1 PALSAR; nonetheless, oil palm did not demonstrate any useful phenology for discriminating between the abandoned classes. The accuracy obtained for these abandoned lands of paddy, rubber and oil palm was 93.33% ± 0.06%, 78% ± 2.32% and 63.33% ± 1.88%, respectively. This study confirmed that the understanding of crop phenology in relation to image date selection is essential to obtain high accuracy for classifying abandoned and non-abandoned agricultural crops. The finding also portrayed that PALSAR offers a huge advantage for application of vegetation in tropical areas.
Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.
ABSTRAKPenelitian ini adalah kajian awal dari valuasi ekonomi atas manfaat dan jasa ekosistem di pesisir Subang. Faktor-faktor utama dalam valuasi adalah keberadaan ekosistem dan perubahan di pesisir, serta manfaat dan jasa ekosistemnya. Estimasi perubahan garis pantai dihitung berdasarkan tumpang susun data satelit Landsat tahun 1988Landsat tahun , 1996Landsat tahun , 2003Landsat tahun , dan 2016. Estimasi menunjukkan terjadi perubahan garis pantai sebesar 8,17 km sejak tahun 1988-2013. Perubahan tersebut didominasi oleh sedimentasi dan abrasi sepanjang pantai. Sedimentasi tertinggi terjadi di Kecamatan Pusakanagara (869,9 ha) dan Blanakan (725,4 ha), serta abrasi tertinggi terjadi di Kecamatan Legonkulon (885,8 ha). Ekosistem alami yang berubah dan dimanfaatkan secara intensif di pesisir Subang adalah kawasan estuari dan mangrove. Sehingga mengakibatkan penurunan fungsi ekologi pada ekosistem tersebut. Adapun lahan tambak sebagai ekosistem buatan yang menggantikan kawasan mangrove, hanya berfungsi sebagai penyedia kebutuhan pangan. Penelitian ini menunjukkan perlunya valuasi ekonomi atas lahan mangrove versus budidaya tambak di Kecamatan Legonkulon, serta kawasan estuari yang berfungsi sebagai sarana transportasi di Kecamatan Pusakanagara. Kata kunci: garis pantai, ekosistem pesisir, erosi, sedimentasi, Subang ABSTRACTThis research is a preliminary study for economic valuation of coastal services and goods in Subang Regency. Main factors in the valuation are ecological existing, changing, services and goods of coastal ecosystem. Coastline changes estimation was based on the overlay of Landsat satellite image at year of 1988, 1996, 2003, and 2016. Estimation shows since year of 1988 to 2013 there is changing of coastline around 8,17 km. The changes are dominantly caused by coastline sedimentation and abrasion. Higher sedimentation occured in Pusakanagara (869,9 ha) and Blanakan (725,4) Subdistrict, while highest abrasion occured in Legonkulon (885,8 ha) Subdistrict. Estuary and mangrove forests are natural ecosystem that had been used intensively and changing very excessively in this region. These changing causes degradation in functions of these ecosystems. Meanwhile, changing of mangrove forest into aquaculture only provides food. This research shows the necessity in economic valution of mangrove forest versus aquaculture in Legonkulon Subdistrict, and also estuary as transportation function in Pusakanagara Subdistrict.
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