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The air temperature changes in the Palu MW7.5 earthquake in Indonesia on 28 September 2018 were analyzed, based on the additive tectonic stress caused by celestial tidal-generating forces (ATSCTF) and air temperature data from the National Center for Environmental Prediction (NCEP). This paper explored the variation characteristics of three-dimensional stratified of air temperature caused by seismic activity and the coupling relationship between air temperature changes and the tidal force. The background information for air temperature calculation was obtained from the tidal force changes, and the air temperature increment method was used to study the temperature evolution process of different periods in the study area. The results found that the tidal force acting on the critical state earthquake faults may be an important external factor inducing earthquakes, and there was indeed a significant air temperature increase anomaly during the Palu MW7.5 earthquake. The paper also summarized the abnormal characteristics of air temperature caused by seismic activity: the air temperature closer to the land's surface has greater anomaly amplitude and a wider anomaly area than that of the upper air. [ABSTRACT FROM AUTHOR]

Segmentation and visualization of three-dimensional digital cultural heritage are important analytical tools for the intuitive understanding of content. In this paper, we propose a semantic segmentation and visualization framework that automatically classifies carved items (people, buildings, plants, etc.) in cultural heritage reliefs. We also apply our method to the bas-reliefs of Borobudur Temple, a UNESCO World Heritage Site in Indonesia. The difficulty in relief segmentation lies in the fact that the boundaries of each carved item are formed by indistinct soft edges, i.e., edges with low curvature. This unfavorable relief feature leads the conventional methods to fail to extract soft edges, whether they are three-dimensional methods classifying a three-dimensional scanned point cloud or two-dimensional methods classifying pixels in a drawn image. To solve this problem, we propose a deep-learning-based soft edge enhanced network to extract the semantic labels of each carved item from multichannel images that are projected from the three-dimensional point clouds of the reliefs. The soft edges in the reliefs can be clearly extracted using our novel opacity-based edge highlighting method. By mapping the extracted semantic labels into three-dimensional points of the relief data, the proposed method provides comprehensive three-dimensional semantic segmentation results of the Borobudur reliefs. [ABSTRACT FROM AUTHOR]

Sago palm tree, known as Metroxylon Sagu Rottb, is one of the priority commodities in Indonesia. Based on our previous research, the potential habitat of the plant has been decreasing. On the other hand, while the use of remote sensing is now widely developed, it is rarely applied for detection and classification purposes, specifically in Indonesia. Considering the potential use of the plant, local farmers identify the harvest time by using human inspection, i.e., by identifying the bloom of the flower. Therefore, this study aims to detect sago palms based on their physical morphology from Unmanned Aerial Vehicle (UAV) RGB imagery. Specifically, this paper endeavors to apply the transfer learning approach using three deep pre-trained networks in sago palm tree detection, namely, SqueezeNet, AlexNet, and ResNet-50. The dataset was collected from nine different groups of plants based on the dominant physical features, i.e., leaves, flowers, fruits, and trunks by using a UAV. Typical classes of plants are randomly selected, like coconut and oil palm trees. As a result, the experiment shows that the ResNet-50 model becomes a preferred base model for sago palm classifiers, with a precision of 75%, 78%, and 83% for sago flowers (SF), sago leaves (SL), and sago trunk (ST), respectively. Generally, all of the models perform well for coconut trees, but they still tend to perform less effectively for sago palm and oil palm detection, which is explained by the similarity of the physical appearance of these two palms. Therefore, based our findings, we recommend improving the optimized parameters, thereby providing more varied sago datasets with the same substituted layers designed in this study. [ABSTRACT FROM AUTHOR]

Individual tree counting (ITC) is a popular topic in the remote sensing application field. The number and planting density of trees are significant for estimating the yield and for futher planing, etc. Although existing studies have already achieved great performance on tree detection with satellite imagery, the quality is often negatively affected by clouds and heavy fog, which limits the application of high-frequency inventory. Nowadays, with ultra high spatial resolution and convenient usage, Unmanned Aerial Vehicles (UAVs) have become promising tools for obtaining statistics from plantations. However, for large scale areas, a UAV cannot capture the whole region of interest in one photo session. In this paper, a real-time orthophoto mosaicing-based tree counting framework is proposed to detect trees using sequential aerial images, which is very effective for fast detection of large areas. Firstly, to guarantee the speed and accuracy, a multi-planar assumption constrained graph optimization algorithm is proposed to estimate the camera pose and generate orthophoto mosaicing simultaneously. Secondly, to avoid time-consuming box or mask annotations, a point supervised method is designed for tree counting task, which greatly speeds up the entire workflow. We demonstrate the effectiveness of our method by performing extensive experiments on oil-palm and acacia trees. To avoid the delay between data acquisition and processing, the proposed framework algorithm is embedded into the UAV for completing tree counting tasks, which also reduces the quantity of data transmission from the UAV system to the ground station. We evaluate the proposed pipeline using sequential UAV images captured in Indonesia. The proposed pipeline achieves an F1-score of 98.2% for acacia tree detection and 96.3% for oil-palm tree detection with online orthophoto mosaicing generation. [ABSTRACT FROM AUTHOR]

Urban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels. [ABSTRACT FROM AUTHOR]

On 22 December 2018, volcano Anak Krakatau, located in Indonesia, erupted and experienced a major lateral collapse. The triggered tsunami killed at least 437 people by the 13-m-high tide. Traditional optical imagery plays a great role in monitoring volcanic activities, but it is susceptible to cloud and fog interference and has low temporal resolution. Synthetic aperture radar (SAR) imagery can monitor volcanic activities at a high temporal resolution, and it is immune to the influence of clouds. In this paper, we propose an automatic method to accurately extract the volcano boundary from SAR images by combining multi-polarized water enhancement and the Nobuyuki Otsu (OTSU) method. We extract the area change of the volcano in 2018–2019 from Sentinel-1 images and ALOS-2 imagesThe area change and evolution are verified and analyzed by combing the results from SAR and optical data. The results show that the southeastern part of the volcano expanded significantly after the eruption, and the western part experienced collapse and recovery. The volcano morphology change experienced a slow-fast-slow process in the two years. [ABSTRACT FROM AUTHOR]

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The normalized differential open-area index (NDOAI) computed from 8-day VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, the VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, the VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 70%. [ABSTRACT FROM AUTHOR]

The Earth Observation Satellite Global Change Observation Mission—Climate (GCOM)-C (SHIKISAI in Japanese), carrying a second-generation global imager (SGLI), was launched in 2017 by the Japan Aerospace Exploration Agency. The SGLI performs wide-swath multi-spectral measurements in 19 channels, from near-ultraviolet to thermal infrared (IR), including the red (674 nm; PL1 channel) and near-IR (869 nm; PL2 channel) polarization channels. This work aimed to demonstrate the advantages of SGLI, particularly the significance of simultaneous off-nadir polarized and nadir multi-spectral observations. The PL1 and PL2 channels were tilted at 45° for the off-nadir measurements, whereas the other channels took a straight downward view for the nadir measurements. As a result, the SGLI provided two-directional total radiance data at two wavelengths (674 and 869 nm) that were included in both off-nadir and nadir observations. Using these bidirectional data, an algorithm was applied to derive the altitude of the aerosol plume. Furthermore, because of the significance of the simultaneous observation of polarized and non-polarized light, the sensitivity difference between the radiance and polarized radiance was demonstrated. Severe wildfire events in Indonesia and California were considered as examples of specific applications. Herein, we present the results of our analysis of optically thick biomass-burning aerosol events. The results of the satellite-based analysis were compared with those of a chemical transport model. Exploring the SGLI's unique capability and continuous 5-year global record paves the way for advanced data exploitation from future satellite missions as a number of multi-directional polarization sensors are programmed to fly in the late 2020s. [ABSTRACT FROM AUTHOR]

The short-term prediction of earthquakes is an essential issue connected with human life protection and related social and economic matters. Recent papers have provided some evidence of the link between the lithosphere, lower atmosphere, and ionosphere, even though with marginal statistical evidence. The basic coupling is hypothesized as being via the atmospheric gravity wave (AGW)/acoustic wave (AW) channel. In this paper we analyze a scenario of the low latitude earthquake (Mw = 6.9) which occurred in Indonesia on 5 August 2018, through a multi-instrumental approach, using ground and satellites high quality data. As a result, we derive a new analytical lithospheric–atmospheric–ionospheric–magnetospheric coupling model with the aim to provide quantitative indicators to interpret the observations around 6 h before and at the moment of the earthquake occurrence. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM's estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations. [ABSTRACT FROM AUTHOR]

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas. [ABSTRACT FROM AUTHOR]

We report here on the observation and offline detection of the weak tsunamis generated by earthquakes near Indonesia on 11 April 2012 using radar systems and tide gauges on the coasts of Sumatra and the Andaman Islands. This work extends the previous observations of the much stronger 2011 Japan tsunami. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends primarily on the bathymetry: the wider the shallow continental shelf, the greater this time. The weak Indonesia tsunamis were detected successfully in spite of the narrow shallow-water shelf offshore from the radar systems. Larger tsunamis could obviously be detected further from the coast. This paper provides further confirmation that radar is an important tool to aid in tsunami observation and warning. [ABSTRACT FROM AUTHOR]

The aquaculture of Kappaphycus alvarezii (Kappaphycus hereafter) seaweed has rapidly expanded among coastal communities in Indonesia due to its relatively simple farming process, low capital costs and short production cycles. This species is mainly cultivated for its carrageenan content used as a gelling agent in the food industry. To further assist producers in improving cultivation management and providing quantitative information about the yield, a novel approach involving remote sensing techniques was tested. In this study, multispectral images obtained from a drone (Unoccupied Aerial Vehicle, UAV) were processed to estimate the fresh and carrageenan weights of Kappaphycus at a cultivation site in South Sulawesi. The UAV imagery was geometrically and radiometrically corrected, and the resulting orthomosaics were used for detecting and classifying Kappaphycus using a random forest algorithm. The classification results were combined with in situ measurements of Kappaphycus fresh weight and carrageenan content using empirical relations between the area and weight of fresh seaweed/carrageenan. This approach allowed quantifying seaweed biometry and biochemistry at single cultivation lines and cultivation plot scales. Fresh seaweed and carrageenan weights were estimated for different dates within three distinct cultivation cycles, and the daily growth rate for each cycle was derived. Data were upscaled to a small family-scale farm and a large-scale leader farm and compared with previous estimations. To our knowledge, this study provides, for the first time, an estimation of yield at the scale of cultivation lines by exploiting the very high spatial resolution of drone data. Overall, the use of UAV remote sensing proved to be a promising approach for seaweed monitoring, opening the way to precision aquaculture of Kappaphycus. [ABSTRACT FROM AUTHOR]

This study was conducted to identify the fire weather conditions needed to assess future peatland fires under climate change. Recent peatland fires in Indonesia have resulted in globally significant environmental impacts. Nevertheless, diurnal fire weather in the peatlands has not been clarified. The objective of this study was to determine the fire weather conditions needed to assess future peatland fires under climate change. An analysis of fire weather using diurnal weather data during the most active fire period in 2015 showed a strong wind speed of 35.7 km h−1 at 3 p.m. that continued to blow for about two weeks, suggesting that peatland fires in 2015 became very active under these very strong wind conditions. The temperature increase rate (ΔT), the RH decrease rate (ΔRH), and the wind speed increase rate (ΔWS) during morning hours from 6:00 a.m. to 9:00 a.m. were 2.3 °C h−1, −10.3% h−1, and 5.2 (km h−1) h−1 respectively. These weather parameters will be used to predict occurrences of active fires. The results of this report may help to predict fire activity under high temperatures in the future based on predictions of global warming made by other researchers. The rapid air temperature increase rate from morning will be useful for fire forecast in Papua. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms that determine the critical habitat of commercial species is one of the significant challenges in marine science, particularly for species that inhabit the vast ocean worldwide. Previous investigations primarily focused on determining skipjack habitats without considering the feasible size for sustainable fisheries. To define habitat hotspots and movement patterns for decently sized skipjack tuna (≥50 cm) in Indonesia Fisheries Management Area (IFMA) 713, Indonesia, we examined the remote sensing of synoptic sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) measurements with catch data from 2007 to 2016. A new skipjack tuna habitat model was developed based on the link between the key satellite-based environmental data and the best tuna fishery performance using a combination of generalized additive models (GAMs) and kernel density estimates. The findings reveal that feasible skipjack catch sizes were found in approximately 27% of total fishing grounds and were significantly captured in areas with a Chl-a of 0.15–0.28 mg m−3 and an SST of 29.5–31.0 °C, corresponding with an elevated skipjack habitat index (SHI). The habitat hotspots for the commercial skipjack were particularly produced by favorable Chl-a and SST, in association with Chl-a front and anticyclonic and cyclonic eddies, especially in October, which coincided with the highest skipjack catch per unit effort (CPUE). Skipjack distributions were mostly found within 10 km of favorable feeding habitats. They used the hotspot area as an indicator of their dynamics and movement pattern in the environment. The observed CPUEs cross-validated the predicted SHI values, suggesting that the model provided a reliable proxy for defining the potential habitats and the spatial movement of mature skipjack schools. Our findings have global significance for locating ecological hotspots, monitoring sustainable skipjack fisheries, and tracking skipjack migration, especially within the western tropical Pacific. [ABSTRACT FROM AUTHOR]

The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote sensing, which has considerable advantages in terms of repetitive observations over tropical areas. Here, we evaluate threatened bird occurrence from L-band satellite data explaining forest structure in Sumatra, Indonesia. First, we identified L-band parameters with strong correlations with the forest layer structure, defined as forest floor, understory, and canopy layers. Then, we analyzed the correlation between threatened bird occurrence and L-band parameters identified as explaining forest structure. The results reveal that several parameters can represent the layers of forest floor, understory, and canopy. Subsequent statistical analysis elucidated that forest-dependent and threatened bird species exhibit significant positive correlations with the selected L-band parameters explaining forest floor and understory. Our results highlight the potential of applying microwave satellite remote sensing to evaluate bird diversity through forest structure estimation, although a more comprehensive study is needed to strengthen our findings. [ABSTRACT FROM AUTHOR]

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l'Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data. [ABSTRACT FROM AUTHOR]

Aerosol robotic network (AERONET) data from Dongsha Island (20.699N, 116.729E) and Taiping Island (35.90N, 3.03W) over the South China Sea (SCS) from January 2018 to December 2020 were used to analyze and discuss the temporal evolution properties of aerosols in the South China Sea. Surrounding AERONET stations (Hong Kong, NSPO, Nha Trang and Singapore) were also used to analyze regional characteristics. High aerosol loads over Dongsha were strongly associated with the anthropogenic fine particle transport from the southeastern coast of China and occasional advection of desert dust from Mongolian areas. The high fine aerosol loading in Taiping originates from the region between Singapore and Indonesia. Compared with other marine islands in the world, SCS was not a pure marine aerosol environment and was affected by terrestrial aerosols. In the Taiping area, aerosol optical depth τ (500 nm) was 0.17 ± 0.13 and the average Ångström exponent α (440–870 nm) was 0.96 ± 0.36. However, that of Dongsha shows the larger values of τ (0.26 ± 0.21) and α (1.1 ± 0.38), indicating that there are large fluctuations in aerosol concentration and size. Aerosol loads in different regions of the SCS due to uneven socioeconomic and complex meteorological systems, such as those of the coastal cities of China, Singapore, and the region between Singapore and Indonesia, contribute to the high optical depth. The special meteorological regime and aerosol source mechanism in the SCS leads to the obvious seasonal cycle of aerosol optical depth and Ångström index. Moreover, the loading variations of aerosols on Dongsha Island and Taiping Island were highly consistent with those of coastal cities around them, suggesting the significant effect of the aerosol in the SCS by the surrounding coastal cities, although the aerosol optical depth in these two places was much lower than that in the surrounding cities. [ABSTRACT FROM AUTHOR]

Tropical peatland in Southeast Asia has undergone rapid degradation and shows large subsidence due to oxidation and peat shrinkage. The measurement of those deformations is thus valuable for evaluating the peat condition and assessing peat restoration. The time series interferometric synthetic aperture radar (TInSAR), especially with the small baseline subsets (SBAS) method, is capable of measuring long-term deformation. However, the dynamic surface scatterers often change in tropical peatland, which degrades the coherent scatterer (CS) distribution density. This article presents a simple and efficient TInSAR approach that enhances the CS density under such dynamic surface scatter variation based on the SBAS method. In the presented approach, a long-time series of single-look complex images is separated into subsets, and deformation estimation is performed for each subset. The effectiveness of this simple solution was investigated by InSAR simulation and validated using SAR observation data. We applied the subset SBAS approach to the three-year Sentinel-1A C-band SAR dataset acquired over tropical peatland in Indonesia. The analyses showed an improved number of CSs for the introduced subset approach. We further introduce the color representation of CS temporal behavior per subset for visual interpretation of scatterer change. [ABSTRACT FROM AUTHOR]

The dry lowland and mangrove forests of Kutai National Park (KNP) in Indonesia provide invaluable ecosystem services to local human populations (>200,000 in number), serve as immense carbon sinks to recapture anthropogenic emissions, and safeguard habitats for thousands of wildlife species including the critically endangered Northeast Bornean orangutan (Pongo pygmaeus morio). With recent reports of ongoing illegal logging and large-scale wildfires within this National Park, we sought to leverage the extensive catalogue and processing power of Google Earth Engine to track the rates and influences of forest loss within KNP over various time periods since 1997. We present estimates of forest loss from the Hansen Global Forest Change v1.9 dataset (2000–2021) which detected a loss of 15% (272 km2) of forest cover within KNP since 2000, half of which (137 km2) coincided with the El Niño-induced wildfires of 2015–2016. Using the MCD64A1 C6.1 MODIS dataset, we found significant spatial overlap between burned area and forest loss detections during the 2015–2016 period but identified considerable omissions in the burned area dataset over smallholder farms within KNP. We discuss the implications of deforestation in areas of primary orangutan habitat and how patterns of forest loss have influenced drought and fire dynamics within KNP. Finally, we compare time-series estimates of precipitation, the ENSO index, burned area, and forest loss to demonstrate that fire risk within KNP depends largely—but not exclusively—on drought severity, and that rates of non-fire (gradual) and fire-related (extreme) forest loss threaten the remaining forests of this National Park. [ABSTRACT FROM AUTHOR]

Spatial approaches, based on the deformation measurement of volcanic domes and crater rims, is key in evaluating the activity of a volcano, such as Merapi Volcano, where associated disaster risk regularly takes lives. Within this framework, this study aims to detect localized topographic change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset from 2012 and one UAV dataset from 2014. The results show that during the period 2012–2014, the crater walls were 100–120 m above the crater floor at its maximum (from the north to the east–southeast sector), while the west and north sectors present a topographic range of 40–80 m. During the period 2012–2014, the evolution of the crater rim around the dome was generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 × 104 m3, corresponding to a maximum −9 m (+/−0.9 m) vertically. The exploded material has partly fallen within the crater, increasing the accumulated loose material while leaving "hollows" where the vents are located, although the potential presence of debris inside these vents made it difficult to determine the exact size of these openings. Despite a measure of the error from the two DEMs, adding a previously published dataset shows further discrepancies, suggesting that there is also a technical need to develop point-cloud technologies for active volcanic craters. [ABSTRACT FROM AUTHOR]

Coastal zones are considered to be highly vulnerable to the effects of climate change, such as erosion, flooding, and storms, including sea level rise (SLR). The effects of rising sea levels endanger several nations, including Indonesia, and it potentially affects the coastal population and natural environment. Quantification is needed to determine the degree of vulnerability experienced by a coast since measuring vulnerability is a fundamental phase towards effective risk reduction. Therefore, the main objective of this research is to identify how vulnerable the coastal zone of Bali Province by develop a Coastal Vulnerability Index (CVI) of areas exposed to the sea-level rise on regional scales using remote sensing and Geographic Information System (GIS) approaches. This study was conducted in Bali Province, Indonesia, which has a beach length of ~640 km, and six parameters were considered in the creation to measure the degree of coastal vulnerability by CVI: geomorphology, shoreline change rate, coastal elevation, sea-level change rate, tidal range, and significant wave height. The different vulnerability parameters were assigned ranks ranging from 1 to 5, with 1 indicating the lowest and 5 indicating the highest vulnerabilities. The study revealed that about 138 km (22%) of the mapped shoreline is classified as being at very high vulnerability and 164 km (26%) of shoreline is at high vulnerability. Of remaining shoreline, 168 km (26%) and 169 km (26%) are at moderate and low risk of coastal vulnerability, respectively. This study outcomes can provide an updated vulnerability map and valuable information for the Bali Province coast, aimed at increasing awareness among decision-makers and related stakeholders for development in mitigation and adaptation strategies. Additionally, the result may be utilized as basic data to build and implement appropriate coastal zone management. [ABSTRACT FROM AUTHOR]

The values (i.e., importance) that humans place on ecosystems are critical for sustainable socioecological management. Recently, the value pluralism approach with instrumental, intrinsic, and relational values using multiple disciplines that integrate qualitative and quantitative methodologies has been encouraged. However, these values have received little attention in environmental management. This study explored the values placed on cultural practices among groups experiencing different land cover transformations (LCTs) under the rapid shrinkage of the Limboto Lake, Gorontalo Province, Indonesia, using questionnaires and time-series Landsat and PlanetScope smallsat constellations (SSCs). The time series of LCTs and questionnaire data were computed, visualized, and analyzed statistically using the chi-square test for comparing the two village groups. Results show SSCs enabled a detailed analysis due to high spatiotemporal resolutions in tropical regions. This observation would help in monitoring natural disasters (floods) caused by the decreased lake's water storing capacity, agricultural damage, locality safety, and environmental protection in shorter cycles. Furthermore, we found that relational values originating from traditional beliefs and practices were the domain values in the land steady-type villages. Hence, integrating the cultural–environmental values of localities with prior spatiotemporal analysis into environmental management policy and implementation processes would be a high-potential alternative for environmental conservation. [ABSTRACT FROM AUTHOR]

Three-dimensional information provided by TanDEM-X interferometric phase and airborne Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) were used to detect differences in vegetation heterogeneity through a disturbance gradient in Indonesia. The range of vegetation types developed as a consequence of fires during the 1997-1998 El Niño. Two-point statistic (wavelet variance and co-variance) was used to assess the dominant spatial frequencies associated with either topographic features or canopy structure. DEMs wavelet spectra were found to be sensitive to canopy structure at short scales (up to 8 m) but increasingly influenced by topographic structures at longer scales. Analysis also indicates that, at short scale, canopy texture is driven by the distribution of heights. Thematic class separation using the Jeffries-Matusita distance (JM) was greater when using the full wavelet signature (LiDAR: 1.29 ¤ JM ¤ 1.39; TanDEM-X: 1.18 ¤ JM ¤ 1.39) compared to using each decomposition scale individually (LiDAR: 0.1 ¤ JM ¤ 1.26; TanDEM-X: 0.1 ¤ JM ¤ 1.1). In some cases, separability with TanDEM-X was similar to the higher resolution LiDAR. The study highlights the potential of 3D information from TanDEM-X and LiDAR DEMs to explore vegetation disturbance history when analyzed using two-point statistics. [ABSTRACT FROM AUTHOR]

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies. [ABSTRACT FROM AUTHOR]

The Landsat-8 satellite of the Landsat Data Continuity Mission was launched by the National Aeronautics and Space Administration (NASA) in April 2013. Just weeks after it entered active service, its sensors observed activity at Paluweh Volcano, Indonesia. Given that the image acquired was in the daytime, its shortwave infrared observations were contaminated with reflected solar radiation; however, those of the satellite's Thermal Infrared Sensor (TIRS) show thermal emission from the volcano's summit and flanks. These emissions detected in sensor's band 10 (10.60-11.19 μm) have here been quantified in terms of radiant power, to confirm reports of the actual volcanic processes operating at the time of image acquisition, and to form an initial assessment of the TIRS in its volcanic observation capabilities. Data from band 11 have been neglected as its data have been shown to be unreliable at the time of writing. At the instant of image acquisition, the thermal emission of the volcano was found to be 345 MW. This value is shown to be on the same order of magnitude as similarly timed NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer thermal observations. Given its unique characteristics, the TIRS shows much potential for providing useful, detailed and accurate volcanic observations in the future. [ABSTRACT FROM AUTHOR]

Over the last 18 years, Indonesia has experienced significant deforestation due to the expansion of oil palm and rubber plantations. Accurate land cover maps are essential for policymakers to track and manage land change to support sustainable forest management and investment decisions. An automatic digital processing (ADP) method is currently used to develop land cover change maps for Indonesia, based on optical imaging (Landsat). Such maps produce only forest and non-forest classes, and often oil palm and rubber plantations are misclassified as native forests. To improve accuracy of these land cover maps, this study developed oil palm and rubber plantation discrimination indices using the integration of Landsat-8 and synthetic aperture radar Sentinel-1 images. Sentinel-1 VH and VV difference (>7.5 dB) and VH backscatter intensity were used to discriminate oil palm plantations. A combination of Landsat-8 NDVI, NDMI with Sentinel-1 VV and VH were used to discriminate rubber plantations. The improved map produced four land cover classes: native forest, oil palm plantation, rubber plantation, and non-forest. High-resolution SPOT 6/7 imagery and ground truth data were used for validation of the new classified maps. The map had an overall accuracy of 92%; producer's accuracy for all classes was higher than 90%, except for rubber (65%), and user's accuracy was over 80% for all classes. These results demonstrate that indices developed from a combination of optical and radar images can improve our ability to discriminate between native forest and oil palm and rubber plantations in the tropics. The new mapping method will help to support Indonesia's national forest monitoring system and inform monitoring of plantation expansion. [ABSTRACT FROM AUTHOR]

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia's triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses. [ABSTRACT FROM AUTHOR]

A decomposition scheme was applied to ALOS/PALSAR data obtained from a fast-growing tree plantation in Sumatra, Indonesia to extract tree stem information and then estimate the forest stand volume. The scattering power decomposition of the polarimetric SAR data was performed both with and without a rotation matrix and compared to the following field-measured forest biometric parameters: tree diameter, tree height and stand volume. The analytical results involving the rotation matrix correlated better than those without the rotation matrix even for natural scattering surfaces within the forests. Our primary finding was that all of the decomposition powers from the rotated matrix correlated significantly to the forest biometric parameters when divided by the total power. The surface scattering ratio of the total power markedly decreased with the forest growth, whereas the canopy and double-bounce scattering ratios increased. The observations of the decomposition powers were consistent with the tree growth characteristics. Consequently, we found a significant logarithmic relationship between the decomposition powers and the forest biometric parameters that can potentially be used to estimate the forest stand volume. [ABSTRACT FROM AUTHOR]

Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices. [ABSTRACT FROM AUTHOR]

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity. [ABSTRACT FROM AUTHOR]

Peatlands in Indonesia are one of the primary global storages for terrestrial organic carbon. Poor land management, drainage, and recurrent fires lead to the release of huge amounts of carbon dioxide. Accurate information about the extent of the peatlands and its 3D surface topography is crucial for assessing and quantifying this globally relevant carbon store. To identify the most carbon-rich peatlands—dome-shaped ombrogenous peat—by collecting GPS-based terrain data is almost impossible, as these peatlands are often located in remote areas, frequently flooded, and usually covered by dense tropical forest vegetation. The detection by airborne LiDAR or spaceborne remote sensing in Indonesia is costly and laborious. This study investigated the potential of the ICESat-2/ATLAS LiDAR satellite data to identify and map carbon-rich peatlands. The spaceborne ICESat-2 LiDAR data were compared and correlated with highly accurate field validated digital terrain models (DTM) generated from airborne LiDAR as well as the commercial global WorldDEM DTM dataset. Compared to the airborne DTM, the ICESat-2 LiDAR data produced an R2 of 0.89 and an RMSE of 0.83 m. For the comparison with the WorldDEM DTM, the resulting R2 lay at 0.94 and the RMSE at 0.86 m. We model the peat dome surface from individual peat hydrological units by performing ordinary kriging on ICESat-2 DTM-footprint data. These ICESat-2 based peatland models, compared to a WorldDEM DTM and airborne DTM, produced an R2 of 0.78, 0.84, and 0.94 in Kalimantan and an R2 of 0.69, 0.72, and 0.85 in Sumatra. The RMSE ranged from 0.68 m to 2.68 m. These results demonstrate the potential of ICESat-2 in assessing peat surface topography. Since ICESat-2 will collect more data worldwide in the years to come, it can be used to survey and map carbon-rich tropical peatlands globally and free of charge. [ABSTRACT FROM AUTHOR]

This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia's peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2's resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products. [ABSTRACT FROM AUTHOR]

The decreasing area of mangroves is an ongoing problem since, between 1980 and 2005, one-third of the world's mangroves were lost. Rehabilitation and restoration strategies are required to address this situation. However, mangroves do not always respond well to these strategies and have high mortality due to several growth limiting parameters. This study developed a land suitability map for new mangrove plantations in different Southeast Asian countries for both current and future climates at a 250-m resolution. Hydrodynamic, geomorphological, climatic, and socio-economic parameters and three representative concentration pathway (RCP) scenarios (RCP 2.6, 4.5, and 8.5) for 2050 and 2070 with two global climate model datasets (the Centre National de Recherches Météorologiques Climate model version 5 [CNRM-CM5.1] and the Model for Interdisciplinary Research on Climate [MIROC5]) were used to predict suitable areas for mangrove planting. An analytical hierarchy process (AHP) was used to determine the level of importance for each parameter. To test the accuracy of the results, the mangrove land suitability analysis were further compared using different weights in every parameter. The sensitivity test using the Wilcoxon test was also carried out to test which variables had changed with the first weight and the AHP weight. The land suitability products from this study were compared with those from previous studies. The differences in land suitability for each country in Southeast Asia in 2050 and 2070 to analyze the differences in each RCP scenario and their effects on the mangrove land suitability were also assessed. Currently, there is 398,000 ha of potentially suitable land for mangrove planting in Southeast Asia, and this study shows that it will increase between now and 2070. Indonesia account for 67.34% of the total land area in the "very suitable" and "suitable" class categories. The RCP 8.5 scenario in 2070, with both the MIROC5 and CNRM-CM5.1 models, resulted in the largest area of a "very suitable" class category for mangrove planting. This study provides information for the migration of mangrove forests to the land, alleviating many drawbacks, especially for ecosystems. [ABSTRACT FROM AUTHOR]

Until 2018, the El Niño–Southern Oscillation (ENSO) was used as an explanation for fires in Indonesia's peatlands. However, when the 2019 fires occurred independently of El Niño, more suitable indicators and methods were required to (a) analyze, (b) evaluate and (c) forecast peatland fires. In this study, we introduced the OLR–MC index—one of the rain-related indices derived from OLR (outgoing longwave radiation) in MC (maritime continent) area in Indonesia. This index showed stronger correlation with active peatland fires than the conventional ENSO index, and is likely to be able to respond to heat and dry weather supposed to be under climate-change conditions. We then analyzed peatland fires in the top six fire years from 2002 to 2018 and showed that peatland fires occurred in three stages—surface fire, shallow peatland fire and deep peatland fire. To explain each stage, we proposed a one-dimensional groundwater level (GWL) prediction model (named as MODEL-0). MODEL-0 predicts GWL from daily rainfall. Analysis using MODEL-0 showed the GWL thresholds for the three fire stages were between -300 mm and -500 mm; peatland fire activities during the three fire stages were dependent on these GWL values. The validity of MODEL-0 was shown by comparison with the measured values of GWL in the top three fire years. [ABSTRACT FROM AUTHOR]

Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (Re), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch–Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (Re) based on a relationship between the in situ GWT and flux-tower-observed Re. Fire emissions are a large direct source of CO2 from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) Re, GPP, FE, and NBE from whole peatlands in the study area (6°N–11°S, 95–141°E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. Re and FE were influenced by El Niño, and the value of the NBE was also high in the El Niño period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of Re with delineations of types of anthropogenic impacts (e.g., drainages and fires). [ABSTRACT FROM AUTHOR]

Indonesia's converted peatland areas have a well-established fire problem, but limited studies have examined the frequency with which they are burning. Here, we quantify fire frequency in Indonesia's two largest peatland regions, Sumatra and Kalimantan, during 2001–2018. We report, annual areas burned, total peatland area affected by fires, amount of recurrent burning and associations with land-use and land-cover (LULC) change. We based these analyses on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua combined burned area and three Landsat-derived LULC maps (1990, 2007, and 2015) and explored relationships between burning and land-cover types. Cumulative areas burned amounted nearly half of the surface areas of Sumatra and Kalimantan but were concentrated in only ~25% of the land areas. Although peatlands cover only 13% of Sumatra and Kalimantan, annual percentage of area burning in these areas was almost five times greater than in non-peatlands (2.8% vs. 0.6%) from 2001 to 2018. Recurrent burning was more prominent in Kalimantan than Sumatra. Average fire-return intervals (FRI) in peatlands of both regions were short, 28 and 45 years for Kalimantan and Sumatra, respectively. On average, forest FRI were less than 50 years. In non-forest areas, Kalimantan had shorter average FRI than Sumatra (13 years vs. 40 years), with ferns/low shrub areas burning most frequently. Our findings highlight the significant influence of LULC change in altering fire regimes. If prevalent rates of burning in Indonesia's peatlands are not greatly reduced, peat swamp forest will disappear from Sumatra and Kalimantan in the coming decades. [ABSTRACT FROM AUTHOR]

Severe scintillations degrade the satellite signal intensity below the fade margin of satellite receivers thereby resulting in failure of communication, positioning, and navigational services. The performance of satellite receivers is obviously restricted by ionospheric scintillation effects, which may lead to signal degradation primarily due to the refraction, reflection, and scattering of radio signals. Thus, there is a need to develop an ionospheric scintillation detection and mitigation technique for robust satellite signal receivers. Hence, variational mode decomposition (VMD) is proposed. VMD addresses the problem of ionospheric scintillation effects on global navigation satellite system (GNSS) signals by extracting the noise from the radio signals in combination with multifractal detrended fluctuation analysis (MFDFA). MFDFA helps as a criterion designed to detect and distinguish the intrinsic mode functions (IMFs) into noisy (scintillated) and noise-free (non-scintillated) IMF signal components using the MFDFA threshold. The results of the proposed method are promising, reliable, and have the potential to mitigate ionospheric scintillation effects on both the synthetic (simulated) and real GNSS data obtained from Manado station (latitude 1.34° S and longitude 124.82° E), Indonesia. From the results, the effectiveness of VMD-MFDFA over complementary ensemble empirical mode decomposition with MFDFA (CEEMD-MFDFA) is an indication of better performance. [ABSTRACT FROM AUTHOR]

In this study, satellite radar observations are employed to reveal spatiotemporal changes in ground surface height of peatlands that have, and have not, undergone restoration in Central Kalimantan, Indonesia. Our time series analysis of 26 scenes of Advanced Land Observation Satellite-1 (ALOS-1) Phased-Array L-band Synthetic-Aperture Radar (PALSAR) images acquired between 2006 and 2010 suggests that peatland restoration was positively affected by the construction time of dams—the earlier the dam was constructed, the more significant the restoration appears. The results also suggest that the dams resulted in an increase of ground water level, which in turn stopped peat losing height. For peatland areas without restoration, the peatland continuously lost peat height by up to 7.7 cm/yr. InSAR-derived peat height changes allow the investigation of restoration effects over a wide area and can also be used to indirectly assess the relative magnitude and spatial pattern of peatland damage caused by drainage and fires. Such an assessment can provide key information for guiding future restoration activities. [ABSTRACT FROM AUTHOR]

Indonesia's landscape is strongly characterized by degradation and deforestation, which results in carbon release. This makes Indonesia one of the largest carbon sources worldwide. The study at hand, investigates monitoring of canopy height and above-ground biomass (AGB) from space in Indonesian tropical forests. Using data from 2015, the canopy height and AGB were modelled in Kalimantan based on quad-pol Pol-InSAR data from RADARSAT-2 (RS-2) and dual-pol Pol-InSAR data from TerraSAR-X (TS-X). Novel algorithms utilizing the Random Volume over Ground (RVoG) interferometric model and the Random Motion over Ground (RMoG) interferometric model were tested to obtain a more accurate and robust forest parameter estimation during dry weather conditions. As a reference for modelling canopy height and AGB, extensive field inventory as well as LiDAR and drone data collected in Kalimantan were used. The RMoG model-based height inversion algorithm led to more accurate results for canopy height than the RVoG model. Using RS-2 imagery, the independent validation displayed a coefficient of determination (R2) of 0.63 and a slight overestimation for the modelled canopy height. The modelled canopy height from TS-X data achieved an R2 of up to 0.66 and resulted in underestimated modelled canopy height. The resulting AGB estimation based on the modelled canopy height resulted in an R2 of 0.83 for RS-2 data and 0.84 for TS-X data. The results of the different tested images varied since the acquisition parameters and the weather conditions changed during acquisitions. It can be concluded, that not all RS-2 and TS-X data is suitable for modelling canopy height from coherence. The parameters that most affect the canopy height model were identified as the baselines (temporal and perpendicular), HoA (height of ambiguity), incident angle and moist weather conditions, as well as the wavelength. Ascending and descending flight directions did not display influence. Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects. [ABSTRACT FROM AUTHOR]

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R2 = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia. [ABSTRACT FROM AUTHOR]

Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam. [ABSTRACT FROM AUTHOR]

This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets. [ABSTRACT FROM AUTHOR]

Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered “extremely hazardous to health” by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO2, CH4 and CO) and PM2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of “pure” sub-surface peat burning and spreading vegetation fires atop burning peat. PM2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg−1, and for spreading vegetation fires atop burning peat 44 to 61 g·kg−1, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM2.5 emissions factors measured worldwide. Using our peatland CO2, CH4 and CO emissions factors (1779 ± 55 g·kg−1, 238 ± 36 g·kg−1, and 7.8 ± 2.3 g·kg−1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg−1) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EFCH4 produces far less (~3×) methane. However, our mean in situ derived EFPM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg−1) is far higher than current emissions inventories assume, resulting in our total PM2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions’ spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods. [ABSTRACT FROM AUTHOR]