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Mr. Aryo Condro
Tropical Biodiversity Conservation Program, Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680, Indonesia

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0 Hydrology
0 Remote Sensing
0 Ecology and Conservation
0 GIS and Geospatial technology
0 Ecology and Biodiversity

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Journal article
Published: 15 February 2021 in Biology
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Indonesia has a large number of primate diversity where a majority of the species are threatened. In addition, climate change is conservation issues that biodiversity may likely face in the future, particularly among primates. Thus, species-distribution modeling was useful for conservation planning. Herein, we present protected areas (PA) recommendations with high nature-conservation importance based on species-richness changes. We performed maximum entropy (Maxent) to retrieve species distribution of 51 primate species across Indonesia. We calculated species-richness change and range shifts to determine the priority of PA for primates under mitigation and worst-case scenarios by 2050. The results suggest that the models have an excellent performance based on seven different metrics. Current primate distributions occupied 65% of terrestrial landscape. However, our results indicate that 30 species of primates in Indonesia are likely to be extinct by 2050. Future primate species richness would be also expected to decline with the alpha diversity ranging from one to four species per 1 km2. Based on our results, we recommend 54 and 27 PA in Indonesia to be considered as the habitat-restoration priority and refugia, respectively. We conclude that species-distribution modeling approach along with the categorical species richness is effectively applicable for assessing primate biodiversity patterns.

ACS Style

Aryo Condro; Lilik Prasetyo; Siti Rushayati; I Santikayasa; Entang Iskandar. Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate. Biology 2021, 10, 154 .

AMA Style

Aryo Condro, Lilik Prasetyo, Siti Rushayati, I Santikayasa, Entang Iskandar. Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate. Biology. 2021; 10 (2):154.

Chicago/Turabian Style

Aryo Condro; Lilik Prasetyo; Siti Rushayati; I Santikayasa; Entang Iskandar. 2021. "Predicting Hotspots and Prioritizing Protected Areas for Endangered Primate Species in Indonesia under Changing Climate." Biology 10, no. 2: 154.

Journal article
Published: 08 October 2020 in Land
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Indonesia has the most favorable climates for agriculture because of its location in the tropical climatic zones. The country has several commodities to support economics growth that are driven by key export commodities—e.g., oil palm, rubber, paddy, cacao, and coffee. Thus, identifying the main commodities in Indonesia using spatially-explicit tools is essential to understand the precise productivity derived from the agricultural sectors. Many previous studies have used predictions developed using binary maps of general crop cover. Here, we present national commodity maps for Indonesia based on remote sensing data using Google Earth Engine. We evaluated a machine learning algorithm—i.e., Random Forest to parameterize how the area in commodity varied in Indonesia. We used various predictors to estimate the productivity of various commodities based on multispectral satellite imageries (36 predictors) at 30-meters spatial resolution. The national commodity map has a relatively high accuracy, with an overall accuracy of about 95% and Kappa coefficient of about 0.90. The results suggest that the oil palm plantation was the highest commodity product that occupied the largest land of Indonesia. However, this study also showed that the land area in rubber, rice paddies, and cacao commodities was underestimated due to its lack of training samples. Improvement in training data collection for each commodity should be done to increase the accuracy of the commodity maps. The commodity data can be viewed online (website can be found in the end of conclusions). This data can further provide significant information related to the agricultural sectors to investigate food provisioning, particularly in Indonesia.

ACS Style

Aryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land 2020, 9, 377 .

AMA Style

Aryo Condro, Yudi Setiawan, Lilik Prasetyo, Rahmat Pramulya, Lasriama Siahaan. Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform. Land. 2020; 9 (10):377.

Chicago/Turabian Style

Aryo Condro; Yudi Setiawan; Lilik Prasetyo; Rahmat Pramulya; Lasriama Siahaan. 2020. "Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform." Land 9, no. 10: 377.

Journal article
Published: 25 January 2020 in Journal for Nature Conservation
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Javan deer (Rusa timorensis) is a protected species in Indonesia and considered to be vulnerable under IUCN list. Nevertheless, its native geographic distribution remains unclear, and the impact of abiotic and biotic factors on this species are mostly unknown. We predicted the potential range of Javan deer in Java and Bali Islands using ten environmental variables, occurrence data of native (76 before 1965, and 653 after 1965) and introduced populations (559), and MaxEnt modelling. We evaluated the effects of habitat loss due to current land use, ecosystem availability, and importance of Indonesian protected areas into the models. Our predictive map significantly improved the IUCN assessment and described for the first time the spread of Javan deer out of its native range within Indonesia. The model of environmental suitability estimated a potential of 3,784.43 km2 natural occurrence in Java and Bali and 36,352.61 km2 for introduced populations in protected areas of West Nusa Tenggara to Papua. The most critical environmental predictors for both populations are the mean annual precipitation and the conservation status of land. Then, 45.66 % of the distribution of native populations overlaps with protected areas, 18.96 % with production forests, 11.07 % with non-protected areas, 10.10 % with limited production forests and 4.20 % with industrial oil palm plantations. Only 22.88 % of the distribution of introduced populations overlaps with protected areas. Our study provides reliable information on places where conservation efforts must be prioritized, both inside and outside the protected area network, to safeguard one of the remaining Indonesian large deer.

ACS Style

Dede Aulia Rahman; Aryo Adhi Condro; Puji Rianti; Burhanuddin Masy’Ud; Stéphane Aulagnier; Gono Semiadi. Geographical analysis of the Javan deer distribution in Indonesia and priorities for landscape conservation. Journal for Nature Conservation 2020, 54, 125795 .

AMA Style

Dede Aulia Rahman, Aryo Adhi Condro, Puji Rianti, Burhanuddin Masy’Ud, Stéphane Aulagnier, Gono Semiadi. Geographical analysis of the Javan deer distribution in Indonesia and priorities for landscape conservation. Journal for Nature Conservation. 2020; 54 ():125795.

Chicago/Turabian Style

Dede Aulia Rahman; Aryo Adhi Condro; Puji Rianti; Burhanuddin Masy’Ud; Stéphane Aulagnier; Gono Semiadi. 2020. "Geographical analysis of the Javan deer distribution in Indonesia and priorities for landscape conservation." Journal for Nature Conservation 54, no. : 125795.

Conference paper
Published: 24 December 2019 in Sixth International Symposium on LAPAN-IPB Satellite
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Primates, the closest living biological relatives with human, play the important roles in the livelihoods, human-health, and ecosystem services. In the Anthropocene, populations of 75% of primate species are decreasing globally – due to cultivation activities, logging harvesting, hunting, and climate change. In this study, we focus on Bornean orangutan (Pongo pygmaeus) as the global conservation icons. Hence, understanding Bornean orangutan’s distribution dynamics is crucial regarding to conservation and climate mitigation strategies. The objectives of this study are: (1) to predict current and future spatial distribution of orangutan in Borneo using pessimistic climate model and land cover projection as well; (2) to identify spatial dynamics of Bornean orangutan distribution due to climate and land cover change in 2030. Species distribution modelling of baseline and future scenario was performed using logistic regression model. Land cover categories and climate parameters (i.e. annual temperature and precipitation) were used for model predictors. Presence points of observed primate species were retrieved from Ministry of Environment and Forestry Indonesia (MoEF). We used WorldClim v2.0 annual temperature and precipitation data for the baseline and CMIP5 MIROC-ESM model RCP8.5 2030 for the future climate scenario. We performed cellular automata algorithm to retrieve 2030 projected land-use for the future. Distance to road and distance to selected important land covers were used for transition potential modelling of land cover projection. Generally, the prediction shows that suitable habitat of Bornean orangutan will decrease in 2030. However, we found the gain of suitable area of Bornean orangutan. Findings of this study should support the identification of priority conservation area of Bornean orangutan for the future and wildlife corridor management planning.

ACS Style

Aryo Condro; Lilik B. Prasetyo; Siti B. Rushayati. Short-term projection of Bornean orangutan spatial distribution based on climate and land cover change scenario. Sixth International Symposium on LAPAN-IPB Satellite 2019, 11372, 113721B .

AMA Style

Aryo Condro, Lilik B. Prasetyo, Siti B. Rushayati. Short-term projection of Bornean orangutan spatial distribution based on climate and land cover change scenario. Sixth International Symposium on LAPAN-IPB Satellite. 2019; 11372 ():113721B.

Chicago/Turabian Style

Aryo Condro; Lilik B. Prasetyo; Siti B. Rushayati. 2019. "Short-term projection of Bornean orangutan spatial distribution based on climate and land cover change scenario." Sixth International Symposium on LAPAN-IPB Satellite 11372, no. : 113721B.

Journal article
Published: 29 April 2019 in Jurnal Meteorologi Klimatologi dan Geofisika
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Agent-Based Modelling (ABM) merupakan sebuah metode yang dapat menjelaskan sistem secara kompleks dengan sebuah agen yang berperan sebagai individu atau objek yang memiliki orientasi dan aksi tertentu dalam mempengaruhi lingkungan model. KATARA merupakan model hidrologi yang dikembangkan berbasis metode ABM tersebut. Tujuan dari penelitian ini adalah membangun model KATARA dan mengaplikasikanya dalam analisis banjir di DAS Ciliwung. Model KATARA ini dibangun dengan resolusi spasial sebesar 100 x 100 m dan dijalankan dalam skala temporal harian. Data yang digunakan dalam penelitian ini terdiri atas data spasial dalam format ASCII (data tutupan lahan dan data model elevasi) dan data tabular dalam format CSV (i.e.parameter tutupan lahan, parameter cuaca, dan parameter sifat tanah). Interaksi permukaan dengan atmosferdiadopsi berdasarkan konsep dari GenRiver yang mampu menjelaskan proses dinamika hidrologi dengan baik(process based). Air hujan yang menjadi limpasan akan berperan sebagai agen dan mengalir sesuai topografi (patch) yang dilewatinya sehingga sebaran spasial air permukaan dapat terdeteksi secara eksplisit (spatially-explicit). Hasil simulasi model KATARA sesuai dengan tujuan utama pembangunan model, yaitu dapat menjelaskan dinamika interaksi antara atmosfer dan permukaan (process based), dan output model dapat dipahami secara spasial (spatially-explicit). Analisis temporal dan spasial pada hasil simulasi model juga mempunyai similaritas yang tinggi dengan data observasi lapang.

ACS Style

Aryo Condro; Ilham Bayu Widagdo. KATARA: MODEL HIDROLOGI BERBASIS AGEN (AGENT- BASED MODELLING) UNTUK ANALISIS BANJIR DI DAS CILIWUNG. Jurnal Meteorologi Klimatologi dan Geofisika 2019, 4, 1 -7.

AMA Style

Aryo Condro, Ilham Bayu Widagdo. KATARA: MODEL HIDROLOGI BERBASIS AGEN (AGENT- BASED MODELLING) UNTUK ANALISIS BANJIR DI DAS CILIWUNG. Jurnal Meteorologi Klimatologi dan Geofisika. 2019; 4 (3):1-7.

Chicago/Turabian Style

Aryo Condro; Ilham Bayu Widagdo. 2019. "KATARA: MODEL HIDROLOGI BERBASIS AGEN (AGENT- BASED MODELLING) UNTUK ANALISIS BANJIR DI DAS CILIWUNG." Jurnal Meteorologi Klimatologi dan Geofisika 4, no. 3: 1-7.

Conference paper
Published: 16 May 2018 in IOP Conference Series: Earth and Environmental Science
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Peatlands are very vulnerable to widespread fires during dry seasons, due to availability of aboveground fuel biomass on the surface and belowground fuel biomass on the sub-surface. Hence, understanding drought propagation occurring within peat layers is crucial with regards to disaster mitigation activities on peatlands. Using a three dimensionally explicit voxel-based model of peatland hydrology, this study predicted drought propagation time lags into sub-surface peat layers after drought events occurrence on the surface of about 1 month during La-Nina and 2.5 months during El-Nino. The study was carried out on a high-conservation-value area of oil palm plantation in West Kalimantan. Validity of the model was evaluated and its applicability for disaster mitigation was discussed. The animations of simulated voxels are available at: goo.gl/HDRMYN (El-Nino 2015 episode) and goo.gl/g1sXPl (La-Nina 2016 episode). The model is available at: goo.gl/RiuMQz.

ACS Style

Aryo Condro; H Pawitan; I Risdiyanto. Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model. IOP Conference Series: Earth and Environmental Science 2018, 149, 012026 .

AMA Style

Aryo Condro, H Pawitan, I Risdiyanto. Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model. IOP Conference Series: Earth and Environmental Science. 2018; 149 (1):012026.

Chicago/Turabian Style

Aryo Condro; H Pawitan; I Risdiyanto. 2018. "Predicting drought propagation within peat layers using a three dimensionally explicit voxel based model." IOP Conference Series: Earth and Environmental Science 149, no. 1: 012026.