A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia

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

Simulating the effect of change in land cover and rainfall in Upper Citarum Watershed: calibration and sensitivity analysis of GenRiver model

Land use and climate change significantly affect watershed hydrological conditions and, hence, influence the effectiveness of a watershed in regulating landscape water balance. Ensuring that land allocation and utilization are managed sustainably is crucial and watershed management must be supported with integrated watershed planning. This study aimed to examine the ability of the Generic Riverflow (GenRiver) model to support watershed planning as a tool to assess the conditions of a watershed’s hydrologcal functions, particularly, to examine to what extent the watershed can buffer the impact of changes in land use and climate. We tested the model on an important watershed in West Java Province — the Upper Citarum — which is a part of one of the Citarum Watershed, a national priority. A process of model calibration and sensitivity analysis was conducted to determine the feasibility of GenRiver in simulating Upper Citarum conditions, in particular, to estimate the water balance at landscape level. The results of the GenRiver simulation using data from 2012–2016 showed that on average 37% of the rainfall in the watershed becomes surface flow (run-off), 7% becomes sub-surface flow and 20% baseflow. Sensitivity analysis was carried out by compiling five land-cover scenarios and three rainfall scenarios that were considered to represent various conditions, including extreme conditions such as 1) the entire area became open (extremely negative); and 2) the entire area became forest (extremely positive). The results of the extremely negative scenario show that a fully degraded condition of the watershed with a dominance of open land has the potential to increase surface runoff up to 70% of rainfall. Meanwhile, improvement of land cover in the watershed by reforestation (extremely positive scenario) would be able to reduce surface runoff by up to 20% of total rainfall. The model calibration and validation evaluation showed that GenRiver performed satisfactorily and was sensitive enough to capture the range of scenarios. Therefore, GenRiver can be used for creating policy-based scenarios that simulate the effect of land-use interventions or restoration programmes in Upper Citarum Watershed and beyond. A soil-erosion module should be added so that an even stronger policy recommendation can be developed

Simulasi Dampak Perubahan Tutupan Lahan dan Iklim di DAS Citarum Hulu dengan Model GenRiver: Kalibrasi model dan analisa sensitivitas

Alih guna lahan dan perubahan iklim merupakan faktor-faktor yang dapat mempengaruhi kondisi hidrologi di suatu Daerah Aliran Sungai (DAS), dan yang dapat mempengaruhi efektifitas fungsi DAS dalam mempertahankan keseimbangan neraca air di tingkat bentang alam. Oleh karena itu pengelolaan DAS yang didukung dengan perencanaan DAS terpadu yang juga mengatur peruntukan dan pemafaatan wilayah sangat diperlukan. Langkah awal dalam perencanaan DAS adalah dengan menilai kondisi fungsi hidrologi DAS tersebut dengat tujuan mengetahui apakah DAS mulai atau telah mengalami degradasi, atau sebaliknya mulai mengalami perbaikan fungsi DAS. Model simulasi seperti model Genriver dapat digunakan menilai kondisi saat ini serta memproyeksikan bagaimana dampak perubahan lahan dan iklim terhadap kondisi hidrologis DAS. Salah satu DAS penting di Jawa Barat adalah DAS Citarum Hulu sebagi bagian dari DAS Citarum yang telah ditetapkan sebagai salah satu DAS prioritas nasional. Kalibrasi model dan analisis sensitivitas menjadi bagian penting untuk mengetahui kelayakan suatu model hidrologi dalam mensimulasikan kondisi DAS, khususnya dalam mengestimasi neraca air di tingkat lansekap. Hasil kalibrasi model GenRiver dengan menggunakan data tahun 2012-2016 menunjukkan bahwa parameterisasi model telah berhasil dan model layak digunakan untuk analisa sensitivitas dan simulasi skenario. Hasil simulasi model, menunjukan bahwa secara rata-rata 37% curah hujan yang jatuh di DAS Citarum Hulu menjadi aliran permukaan (surface flow/run-off), 7% menjadi aliran bawah permukaan (sub-surface flow) dan 20% menjadi aliran dasar (baseflow). Analisa senssitivitas dilakukan dengan menyusun lima skenario tutupan lahan dan tiga skenario curah hujan yang dianggap mewakili berbagai kondisi yang mungkin termasuk kondisi ekstrim: yaitu keseluruhan lahan menjadi area terbuka (ekstrim negatif) dan seluruh lahan menjadi hutan (ekstrim positif). Hasil proyeksi simulasi ekstrim negatif menunjukan bahwa kondisi DAS Citarum Hulu yang terdegradasi dengan dominasi lahan terbuka berpotensi meningkatkan aliran permukaan hingga mencapai 70% dari curah hujan. Sedangkan perbaikan tutupan lahan DAS Citarum hulu dengan reforestasi (skenario ekstrim positif) mampu menurunkan aliran permukaan hingga mencapai 20% dari total curah hujan

Groundwater-Extracting Rice Production in the Rejoso Watershed (Indonesia) Reducing Urban Water Availability: Characterisation and Intervention Priorities

Production landscapes depend on, but also affect, ecosystem services. In the Rejoso watershed (East Java, Indonesia), uncontrolled groundwater use for paddies reduces flow of lowland pressure-driven artesian springs that supply drinking water to urban stakeholders. Analysis of the water balance suggested that the decline by about 30% in spring discharge in the past decades is attributed for 47 and 53%, respectively, to upland degradation and lowland groundwater abstraction. Consequently, current spring restoration efforts support upland agroforestry development while aiming to reduce lowland groundwater wasting. To clarify spatial and social targeting of lowland interventions five clusters (replicable patterns) of lowland paddy farming were distinguished from spatial data on, among other factors, reliance on river versus artesian wells delivering groundwater, use of crop rotation, rice yield, fertiliser rates and intensity of rodent control. A survey of farming households (461 respondents), complemented and verified through in-depth interviews and group discussions, identified opportunities for interventions and associated risks. Changes in artesian well design, allowing outflow control, can support water-saving, sustainable paddy cultivation methods. With rodents as a major yield-reducing factor, solutions likely depend on more synchronized planting calendars and thus on collective action for effectiveness at scale. Interventions based on this design are currently tested

A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia

This collection represents geographically diverse, temporally consistent, and nationally relevant land cover (LC) reference data 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 local citizen scientists (crowd/non-experts) and local LC visual interpretation experts from different regions in the country. This helps to ensure that the LC map products are relevant and can contribute effectively to the actionable information needs of the national and sub-national stakeholders and end users of the LC products within the country. We provide the raw LC reference dataset, as well as a quality-filtered dataset, along with the quality assessment indicators. The dataset is relevant for the LC mapping community, i.e., researchers and practitioners, as reference data for training ML algorithms and for map accuracy assessment (with appropriate quality-filters applied). The dataset is also useful for the citizen science community, i.e., as a sizable empirical dataset to investigate the potential and limitations of 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. The detail description of the data and the data collection methodology can be found in our paper below