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.

Indonesia has experienced one of the world’s greatest dynamic land changes due to forestry and agricultural practices. Understanding the drivers behind these land changes remains challenging, partly because landscape research is spread across many domains and disciplines. We provide a systematic review of 91 studies that identify the causes and land change actors across Sumatra and Kalimantan. Our review shows that oil palm expansion is the most prominent (65 studies) among multiple direct causes of land change. We determined that property rights are the most prominent issue (31 studies) among the multiple underlying causes of land change. Distinct combinations of mainly economic, institutional, political, and social underlying drivers determine land change, rather than single key drivers. Our review also shows that central and district governments as decision-making actors are prominent (69 studies) among multiple land change actors. Our systematic review indicates knowledge gaps that can be filled by clarifying the identification and role of actors in land change.

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.

Forest and land fire in Indonesia have been given much attention since it creates environmental problems every year. Instead of its negative impacts, fire cannot be separated from agricultural system in the tropics. Moreover, under the regulation, the farmer is allowed to use fire for land preparation under 2 hectares. However, fire utilization is prohibited for land preparation in concessionaries. In facts, some companies are utilized fire for economics reason even though some of them are refused to admit. Therefore, it is interesting to know on what is really occur in the field related to the fire occurrence. Objectives of the research are to determine distribution of fire occurrence based on historical hot spot data during 15 years period (2001-2015), and analysis land cover as well as land use trajectories before and after fire occurrence in Jambi Province. Result showed, fire tend to occur in peat land every year, either during El Niño or La Niña period. Land covers before fire occurrence mostly were bush and disturbed secondary forest. It was also revealed that fire was also utilized by companies (oil palm and forest plantation). During period of analysis, on average, 20.67% was converted into forest plantation and 27.06% was converted into palm oil plantation, meanwhile the rest areas (52.27%) were community land area

In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.

Understanding the recovery rate of forest carbon stocks and biodiversity after disturbance, including fire, is vital for developing effective climate-change-mitigation policies and actions. In this study, live and dead carbon stocks aboveground, belowground, and in the soil to a 30 cm depth, as well as tree and shrub species diversity, were measured in a tropical lowland dry forest, 23 years after a fire in 1998, for comparison with adjacent unburned reference forests. The results showed that 23 years since the fire was insufficient, in this case, to recover live forest carbon and plant species diversity, to the level of the reference forests. The total carbon stock, in the recovering 23-year-old forest, was 199 Mg C ha⁻¹ or about 90% of the unburned forest (220 Mg C ha⁻¹), mainly due to the contribution of coarse woody debris and an increase in the 5–10 cm soil horizon’s organic carbon, in the burned forest. The carbon held in the live biomass of the recovering forest (79 Mg C ha⁻¹) was just over half the 146 Mg C ha⁻¹ of the reference forest. Based on a biomass mean annual increment of 6.24 ± 1.59 Mg ha⁻¹ yr⁻¹, about 46 ± 17 years would be required for the aboveground live biomass to recover to equivalence with the reference forest. In total, 176 plant species were recorded in the 23-year post-fire forest, compared with 216 in the unburned reference forest. The pioneer species Macaranga gigantea dominated in the 23-year post-fire forest, which was yet to regain the similar stand structural and compositional elements as those found in the adjacent unburned reference forest.