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Here we present a geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference dataset collected by visual interpretation of very high spatial resolution imagery, in a national-scale crowdsourcing campaign (targeting seven generic LC classes) and a series of expert workshops (targeting seventeen detailed LC classes) in Indonesia. The interpreters were citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. We envisage that the dataset will be relevant for: (1) the LC mapping community (researchers and practitioners), i.e., as reference data for training machine learning algorithms and map accuracy assessment (with appropriate quality-filters applied), and (2) the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of contributions from the crowd/non-experts, demonstrated for LC mapping in Indonesia for the first time to our knowledge, within the context of complementing traditional data collection by expert interpreters.

Pembangunan yang pesat di daerah pesisir menjadikan daerah pesisir beserta jutaan penduduk, infrastruktur, maupun aktivitas ekonomi yang ada memiliki risiko yang meningkat pula terhadap ancaman bahaya pesisir seperti erosi pantai, badai, kenaikan muka air laut dan juga tsunami. Oleh karena itu kesiapsiagaan dan mitigasi menjadi hal penting untuk melindungi dari bencana alam. Dalam hal ini, rekayasa struktur sering kali diimplementasikan dimana dalam proses pembangunannya dapat menyebabkan terganggunya ekosistem laut maupun darat. Alternatif lain yaitu dengan pemanfaatan zona penyangga alami vegetasi pantai. Struktur vegetasi pantai memiliki kemampuan mengurangi gelombang secara langsung dan secara tidak langsung, dapat mengurangi dampak yang ditimbulkan melalui stabilisasi dan pembentukan sedimen. Mangrove sebagai contoh dari vegetasi pantai relatif sering ditemukan di garis depan dalam hal kedudukan atau posisinya terhadap bencana pesisir. Mangrove mampu melemahkan arus gelombang, menangkap sedimen, dan membentuk tanah. Akar dari mangrove dapat menjebak sedimen, manambah tinggi permukaan, dan memberikan perlindungan terhadap kenaikan muka laut. Pada penelitian ini dilakukan studi literatur mengenai peran vegetasi pantai dalam menghadapi berbagai macam ancaman bahaya pesisir. Peran vegetasi pantai tersebut akan diuraikan pada masing-masing bahaya yang dapat mengancam daerah pesisir. Melalui studi ini dapat dilihat bahwa vegetasi pantai dapat memberikan manfaat dalam mengurangi ancaman bahaya pesisir. Oleh karena itu, usaha-usaha dalam melindungi keberadaan vegetasi pantai penting dilakukan karena dapat meminimalisir risiko akibat bahaya yang mengancam.

The development of current massive technology plays an important role in the field of disaster risk reduction and disaster management. In particular, the issue of disruptive technology in which the emerging of new technologies comes with big disruption in many fields. For instance, the development on using satellite imagery big data for disaster management to reduce the disaster risk.The unique characteristics of big data i.e. volume, velocity, and variety stimulate this study to obtain more information on the big data processing and analyze the opportunities and challenges of satellite imagery in the case of disaster risk reduction through literature reviews. The huge amount of data and the ability for real-time analysis provide capabilities on time series analysis and possitive impact on disaster detection, monitoring, and prediction. However, such technology creates disruption due to the change on using and anlysing the data based on cloud environment. Issues on the technology uses, data security might arise. Nonetheless, the use of satellite imagery big data will remain vastly developed and is able to promote the development of satellite imagery technology.

Northern Java coasts of Indonesia is dominated by unconsolidated Holocene alluvial deposits. Anthropogenic stresses on the area has lead to land subsidence at various rate relate to the deposition systems. This paper review reports on land subsidence in the area using the formal phrase as well as terms related to the phenomenons appeared in coastal communities. We found the formal phrase appeared decades after coastal communities has considered the impact without knowing the physical phenomenon. In addition to that, research agencies have been focusing on monitoring while national government bodies focused on mitigation infrastructures. To fill in the gap, we suggest flood risk study will help the efforts to bridge both activities. This will help local and national government to prioritize activities in to overcome negative impact of land subsidence.

The construction of an underwater fiber-optic cable-based tsunami early detection system is planned to be installed in the Makassar Strait with a landing point in West Sulawesi. Land infrastructure such as a Power House (PH), a communication tower, and a Beach Manhole (BMH) is needed to sustain the system's power in order to survive. Therefore, the aim of this scientific paper is to study several alternative landing station locations in Pasangkayu, West Sulawesi and then determine the priority locations. The methods used to achieve these objectives are field observation methods and secondary data analysis based on parameters of land area conditions, environmental conditions, supporting infrastructure, social conditions and licensing systems, and marine activities. These aspects are then assessed to determine the priority of the selected location. The location selected based on the assessment carried out is ALT-02B. In succession, the three priority locations selected from the order of most priority to least priority are ALT-02B, ALT-02A, and ALT-06. This selected priority location is not necessarily the final choice location to be used in the construction of a tsunami early detection system land infrastructure because in the process there are still things to be considered and aspects that are made the main priority for consideration in making decisions.

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that have not been decomposed. In the dry season, this accumulated biomass called peat, which was originally submerged in water, is exposed to the surface and prone to fire. Once it burns, smouldering peat fires consume biomass 15 times larger than open flame. Peat smouldering fires are very difficult to extinguish. These will continuously occur for weeks to months. The most recommended effort by experts and practitioners of peat smouldering fires is to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology'. Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses (1) the accuracy is very low; (2) having predictive biases in areas with different ecosystem characteristics; (3) it is often difficult to implement because it requires a large and complex number of 'expert rules'; and (4) involving many variables, complex, and heterogeneous data formats. Slowly but surely this method has been replaced by the Machine Learning (ML) method as it is developing in Europe, China, India, Japan, North America and Australia. What about the potential application of ML in the forest and land fires, especially smouldering peat fires in Indonesia? This paper tries to answer this question through the following points: State of the art of machine learning in science and management of forest and land fire Outlook on technology of Disaster Risk Reduction (DRR) in BPPT in the field of forest and land fire and the opportunities of ML implementation Impact based forecasting and machine learning for DRR of forest and land fire This paper recommends a conceptual design: Impact-Based Learning for DRR of Forest, Land Fire and Peat Smouldering

Land use land cover change and forestry play an important role in the global environmental change. Anthropogenic activities in changing the land have caused earth surface change. This change has a role to increase the change of global greenhouse gases in the atmosphere which also causes the increase greenhouse gases emission. Land cover change and forestry are sectors which cause high carbon emission. Therefore, a study in land cover change and estimation of carbon emission becomes important. This study took place in Jambi Province where deforestation has been in a high pace. In 2009 and 2011, the dominant area is dryland agriculture mixed with bush followed by secondary forest, i.e. 25% and 18.6%, respectively (in 2009); and 37.1% and 18.9%, respectively (in 2011). For the secondary forest, the gain was caused by the conversion of dryland agriculture mixed with bush and shrub into secondary forest. The loss of secondary forest is the highest among other forest cover at around 87,765 Ha due to the conversion into bare land and dryland agriculture mixed with bush. Due to land cover change in Jambi Province, the estimation of nett emission in the period of 2009-2011 is 4.8 Mt CO2-eq/year.

Tropical rainforest in Indonesia faces critical issue related to deforestation. Human activities which convert forest cover into non-forest cover has been a major issue. In order to sustain the forest resources, monitoring on deforestation and forest cover prediction is necessary to be done. Remotely sensed data, Landsat images, with acquisition in 1996, 2000, and 2005 are used in this study. In this study area, forest cover decreased around 6 % in the period of 1996 - 2005. For the purpose of forest cover modelling, three model (i.e. Stochastic Markov Model, Cellullar Automata Markov (CA_Markov) Model, dan GEOMOD) were tested. Based upon the Kappa index, GEOMOD performed better with the highest Kappa index. Therefore, GEOMOD is recommended to forecast forest cover.

Tropical lowland rainforests are increasingly threatened by the expansion of agriculture and the extraction of natural resources. In Jambi Province, Indonesia, the interdisciplinary EFForTS project focuses on the ecological and socio-economic dimensions of rainforest conversion to jungle rubber agroforests and monoculture plantations of rubber and oil palm. Our data confirm that rainforest transformation and land use intensification lead to substantial losses in biodiversity and related ecosystem functions, such as decreased above- and below-ground carbon stocks. Owing to rapid step-wise transformation from forests to agroforests to monoculture plantations and renewal of each plantation type every few decades, the converted land use systems are continuously dynamic, thus hampering the adaptation of animal and plant communities. On the other hand, agricultural rainforest transformation systems provide increased income and access to education, especially for migrant smallholders. Jungle rubber and rubber monocultures are associated with higher financial land productivity but lower financial labour productivity compared to oil palm, which influences crop choice: smallholders that are labour-scarce would prefer oil palm while land-scarce smallholders would prefer rubber. Collecting long-term data in an interdisciplinary context enables us to provide decision-makers and stakeholders with scientific insights to facilitate the reconciliation between economic interests and ecological sustainability in tropical agricultural landscapes.

Smallholder-dominated agricultural mosaic landscapes are highlighted as model production systems that deliver both economic and ecological goods in tropical agricultural landscapes, but trade-offs underlying current land-use dynamics are poorly known. Here, using the most comprehensive quantification of land-use change and associated bundles of ecosystem functions, services and economic benefits to date, we show that Indonesian smallholders predominantly choose farm portfolios with high economic productivity but low ecological value. The more profitable oil palm and rubber monocultures replace forests and agroforests critical for maintaining above- and below-ground ecological functions and the diversity of most taxa. Between the monocultures, the higher economic performance of oil palm over rubber comes with the reliance on fertilizer inputs and with increased nutrient leaching losses. Strategies to achieve an ecological-economic balance and a sustainable management of tropical smallholder landscapes must be prioritized to avoid further environmental degradation., Small-scale farmers in Southeast Asia are increasingly turning to monocultures of oil palm and rubber to maximize income. Clough and colleagues demonstrate that this land-use change in Indonesia comes at a cost to a wide array of ecosystem functions and biodiversity.

Specialization in agricultural systems can lead to trade-offs between economic gains and ecosystem functions. We suggest and explore a conceptual framework in which economic gains can be maximized when production activities are specialized at increasingly broader scales (from the household to the village, region or above), particularly when markets for outputs and inputs function well. Conversely,more specialization likely reduces biodiversity and significantly limits ecosystem functions. When agricultural specialization increases and moves to broader scales as a result of improved infrastructure and markets or other drivers, ecosystem functions can also be endangered at broader spatial scales. Policies to improve agricultural incomes may influence the level of specialization at different scales and thus affect the severity of the trade-offs. This paper takes Jambi province in Indonesia, a current hotspot of rubber and oil palm monoculture, as a case study to illustrate these issues.We empirically show that the level of specialization differs across scales with higher specialization at household and village levels and higher diversification towards the province level. We discuss ways to resolve trade-offs between economic gains and ecological costs, including landscape design, targeted policies, and adoption of longterm perspectives. peerReviewed