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Examples of contour lines manually drawn in different situations. (a,d) a relatively intact peat dome (CK2), (b,e) a drained peat dome with subsidence (CK3), (c,f) a mineral soil area without much topography (CK15). Area codes with location are shown in Figure 4. (a–c) Five kilometer LiDAR strips (in this example simulated from the full coverage DTM shown in Figure 4) and (d–f) adding contour lines. In the background Landsat-8 composite image of 3 August 2015. For each of the examples the same legend color scheme was used but with different elevation intervals.
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Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are a...
Citations
... Vegetation height maps for the study area were created using airborne LiDAR data collected in January 2017, October 2017, August 2018 and July 2020; specifications of LiDAR data collection can be found in Vernimmen et al. 12 . The LiDAR data processing was carried out using PDAL 2.5.6 13 and PointCloudRasterizers 0.2.5 14 . ...
Drainage and deforestation of tropical peat swamp forests (PSF) in Southeast Asia cause carbon emissions and biodiversity loss of global concern. Restoration efforts to mitigate these impacts usually involve peatland rewetting by blocking canals. However, there have been no studies to date of the optimal rewetting approach that will reduce carbon emission whilst also promoting PSF regeneration. Here we present results of a large-scale restoration trial in Sumatra (Indonesia), monitored for 7.5 years. Water levels in a former plantation were raised over an area of 4800 ha by constructing 257 compacted peat dams in canals. We find peat surface subsidence rates in the rewetted restoration area and adjoining PSF to be halved where water tables were raised from ~ − 0.6 m to ~ − 0.3 m, demonstrating the success of rewetting in reducing carbon emission. A total of 57 native PSF tree species were found to spontaneously grow in the most rewetted conditions and in high densities, indicating that forest regrowth is underway. Based on our findings we propose that an effective PSF restoration strategy should follow stepwise rewetting to achieve substantial carbon emission reduction alongside unassisted regrowth of PSF, thereby enabling the peat, forest and canal vegetation to establish a new nature-based ecosystem balance.
... Particularly in coastal areas, peatland subsidence increases the risk of flooding and saltwater intrusion. Much of Malaysia and Indonesia will be at risk of flooding in the near future due to rapid peatland subsidence and sea-level rise [52]. ...
Peatlands are major natural carbon pool in terrestrial ecosystems globally and are essential to a variety of fields, including global ecology, hydrology, and ecosystem services. Under the context of climate change, the management and conservation of peatlands has become a topic of international concern. Nevertheless, few studies have yet systematized the overall international dynamics of existing peatland research. In this study, based on an approach integrating bibliometrics and a literature review, we systematically analyzed peatland research from a literature perspective. Alongside traditional bibliometric analyses (e.g., number of publications, research impact, and hot areas), recent top keywords in peatland research were found, including ‘oil palm’, ‘tropical peatland’, ‘permafrost’, and so on. Furthermore, six hot topics of peatland research were identified: (1) peatland development and the impacts and degradations, (2) the history of peatland development and factors of formation, (3) chemical element contaminants in peatlands, (4) tropical peatlands, (5) peat adsorption and its humic acids, and (6) the influence of peatland conservation on the ecosystem. In addition, this review found that the adverse consequences of peatland degradation in the context of climate change merit greater attention, that peatland-mapping techniques suitable for all regions are lacking, that a unified global assessment of carbon stocks in peatlands urgently needs to be established, spanning all countries, and that a reliable system for assessing peatland-ecosystem services needs to be implemented expeditiously. In this study, we argued that enhanced integration in research will bridge knowledge gaps and facilitate the systematic synthesis of peatlands as complex systems, which is an imperative need.
... The research area is located in the southern part of the Central Kalimantan Region, which includes three districts, i.e., Pulang Pisau Regency, Katingan Regency, and Kotawaringin Timur Regency. The southern part of this region has a diverse topography, such as coastal areas, and peat swamps [30,31]. Research Location is presented in Figure 1. ...
... Liu et al., 2020;Uuemaa et al., 2020). In lowlands, where elevation gradients are often quite small, these new DEM products provide a higher vertical resolution that can capture the subtle variations in elevation (Vernimmen et al., 2019;Yamazaki et al., 2017), which was a significant challenge in the past. With these finer-resolution DEMs, it was possible to represent the topography of lowland regions more accurately, leading to significantly improved soil mapping outcomes. ...
This PhD thesis focused on Digital Soil Mapping (DSM) field with a particular emphasis on its application in lowland areas. Comprising four distinct studies organized in four chapters, this research endeavor unravels the intricacies of soil mapping accuracy, spatial resolution, machine learning models, and the transferability of DSM models. Lowland regions, which have fewer DSM studies compared with highland regions, come into sharp focus as the ecological significance of these areas for agriculture, urbanization, and environmental resilience is underscored. The systematic review in the first study reveals an escalating interest in DSM for lowlands, indicating a burgeoning appreciation for its potential, driven by advancements in high-resolution Digital Elevation Models (DEMs) and accessible remote sensing data. This study underscores the importance of considering diverse environmental covariates and choosing appropriate DSM approaches, setting the stage for further investigations. The second study employs a range of machine learning models to predict and map soil properties in an agricultural lowland area of Lombardy region, Italy. Insights gleaned from this study lay the groundwork for the application of linear and nonlinear models as well as ensemble machine learning models and highlight the significance of terrain attributes in soil property prediction. In the third study, machine learning techniques, combined with residual kriging, were leveraged to predict the spatial distribution of Soil Organic Carbon (SOC) in an agricultural lowland area of Lombardy region, Italy. The findings elucidate the potential of machine learning with residual kriging in predicting SOC and underscore the importance of terrain attributes in the spatial distribution of SOC, offering tangible implications for soil management. The fourth study ventures into model transferability in DSM, shedding light on the impact of DEM spatial resolution. This critical exploration underscores the need for a thoughtful consideration of spatial resolution in DSM applications and advocates for caution when transferring models to varying resolutions. Recommendations arising from these studies include the integration of additional data sources, advanced machine learning techniques, and the development of improved methods for model transferability. This PhD thesis collectively contributes to advancing the field of Digital Soil Mapping. Its findings have direct implications for sustainable land management, precision agriculture, and environmental impact assessment. The comprehensive insights offered pave the way for future research aimed at enhancing soil mapping accuracy and soil health in lowland areas and beyond.
... The vertical accuracy of DEMs, which provide crucial information for soil mapping, has only recently seen significant improvements with new products like LIDAR-based techniques or satellitebased information such as TerraSAR-X [112,113]. In lowlands, where elevation gradients are often quite small, these new DEM products provide a higher vertical resolution that can capture the subtle variations in elevation [114,115], which was a significant challenge in the past. With these finer-resolution DEMs, it was possible to represent the topography of lowland regions more accurately, leading to significantly improved soil mapping outcomes. ...
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM's pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies.
... Pemanfaatan penginderaan jauh menggunakan Google Earth Engine dapat mempermudah dalam menganalisa kejadian geospasial skala planet seperti meningkatkan kapasitas penampang sungai yang terjadi penumpukan sedimen (Gorelick et al., 2017;Kustamar & Ajiza, 2019). Digital Terrain Model (DTM) yang akurat dan terkini diperlukan untuk memprediksi dan mengelola risiko banjir (Vernimmen et al., 2019). ...
Climate change is constant. The average temperature has risen since the globe was encased in ice millions of years ago. Current climate change is caused by natural events and human behavior in treating and managing the environment. The huge burning of coal, oil, and wood and deforestation caused by economic development has seriously damaged the world's climate. Global climate change may alter peat carbon stored by forest and land fires. Human activities like plantation development, agriculture, and logging have made tropical peatlands more vulnerable to fire. Indonesia has 44 million hectares of tropical peatlands, with 45% and 64% carbon content. This study addresses Palangka Raya's intermittent land surface heating. The Palangka Raya University academic community conducted this research to provide input on climate change and the global environment and to predict a symptom or occurrence that harms society. This project is part of the University of Palangka Raya's Principal Scientific Pattern (PIP): Science and Technology Innovation in Tropical Peat Swamp Areas and River Streams.
... Many tropical peatlands are vast and inaccessible (Dargie et al., 2017;Honorio Coronado et al., 2021), and therefore researchers have explored the use of remote sensing approaches to measure their topography, using both airborne (Vernimmen et al., 2019(Vernimmen et al., , 2020Davenport et al., 2020) and spaceborne platforms (Jaenicke et al., 2008;Ballhorn et al., 2009;Berninger and Siegert, 2020). Some peatlands in Southeast Asia are now covered by discrete-return airborne lidar datasets commissioned by governments or private organizations (Vernimmen et al., 2019). ...
... Many tropical peatlands are vast and inaccessible (Dargie et al., 2017;Honorio Coronado et al., 2021), and therefore researchers have explored the use of remote sensing approaches to measure their topography, using both airborne (Vernimmen et al., 2019(Vernimmen et al., , 2020Davenport et al., 2020) and spaceborne platforms (Jaenicke et al., 2008;Ballhorn et al., 2009;Berninger and Siegert, 2020). Some peatlands in Southeast Asia are now covered by discrete-return airborne lidar datasets commissioned by governments or private organizations (Vernimmen et al., 2019). Discrete-return airborne lidar datasets are created by laser systems mounted on aircraft typically flying at heights of 500 m-1000 m and emitting tens of thousands to hundreds of thousands of infrared pulses per second (Lim et al., 2003;Liu, 2008). ...
Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The mounded shapes of tropical peat domes account for much of the carbon storage in these landscapes, but their subtle topographic relief is difficult to measure. As many of the world's tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA's Global Ecosystem Dynamics Investigation (GEDI) on the International Space Station (ISS) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. We evaluate retrieval of ground elevations derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne lidar dataset covering an area of over 300 km² in the Belait District of Brunei Darussalam on the island of Borneo. Spatial filtering of GEDI L2A version 2, algorithm 1 quality data reduced mean absolute deviations from airborne-lidar-derived ground elevations from 8.35 m to 1.83 m, root-mean-squared error from 15.98 m to 1.97 m, and unbiased root-mean-squared error from 13.62 m to 0.72 m. Similarly, spatial filtering of ATLAS ATL08 version 3 ground photons from strong beams at night reduced mean absolute deviations from 1.51 m to 0.64 m, root-mean-squared error from 3.85 m to 0.77 m, and unbiased root-mean-squared error from 3.54 m to 0.44 m. We conclude that despite sparse ground retrievals, these spaceborne platforms can provide useful data for tropical peatland surface altimetry if postprocessed with a spatial filter.
... These spatial scenarios, however, are only meant to demonstrate a method for calculating the efficiency of implementing a policy. There are other methods that may have resulted in more accurate mapping (Rahman et al., 2017;Vernimmen et al., 2019), but they were not available for this study. ...
Conflicting policies relating to the management of multi-sectoral, multi-level and multi-actor forest uses often result in ineffective policy implementation. Methods for assessing policy coherence, however, are limited and often require an extensive evidence base which is not always available. In Indonesia, this has often led to conflicts between government agencies and other forest stakeholders. Businesses, NGOs and local communities struggle to comply with all of the conflicting or overlapping regulations that relate to the use of forested landscapes. Even if they succeed, the cost of implementation can be excessive. Improved methods for assessing policy coherence could assist governments and other stakeholders to navigate policy complexity and to avoid the potentially high costs of policies that are antagonistic to one another. We propose an audit of policy coherence at the landscape scale as a way of addressing this problem. We test this idea with an experimental policy audit on the Kampar Peninsula, a peat landscape in Pelalawan district, Riau Province, Indonesia. Indonesia has participated in the UN global peat initiative since 2015 and has created a peat protection policy to control the exploitation of peat with regulation No 57/2016. This regulation and the various instruments devolved from it has been a source of confusion and conflict among stakeholders. We applied commonly accepted performance auditing standards to assess the coherence, effectiveness and efficiency of regulations from other sectors and in different jurisdictions with the new peat regulation No 57/2016 and its derivatives. To aid our audit assessment, we overlaid radar and Landsat images to depict delineations of peat protection and cultivation zones according to different legislation. Our audit revealed incoherent mapping of peat protection zones on the Kampar Peninsula, which has led to ineffective and inefficient implementation of policies. We then propose three alternative protection and cultivation scenarios to that proposed by the government. Our results show that any of these alternative scenarios would provide a policy that is not only more coherent, but that also would result in more effective and efficient policy implementation. This policy audit method should have wide potential application for auditing best practice and policy effectiveness in complex landscapes across the globe and should have immediate application in helping to resolve the current issues on the Kampar Peninsular.
... LiDAR retrieval data can be analyzed to generate environmental covariates corresponding to vegetation. To our knowledge, for analysis over study areas greater than a few ten square kilometers, LiDAR has only been applied in DSM for generating finer spatial resolution DEMs (Akumu et al., 2015;Anderson et al., 2006;Greve et al., 2012;Mulder et al., 2011), and in particular, recently for wetland environments (O'Neil et al., 2019;Rapinel et al., 2019;Vernimmen et al., 2019). Environmental covariates for parent material, if specified, tend to be limited to vector files corresponding to surficial geology (Minasny et al., 2019) or mineralogy (Mulder et al., 2011). ...
This study aimed to improve the accuracy in modelling the properties of forest soil by employing a variety of remotely sensed data. In addition to the commonly used multispectral satellite imagery, airborne LiDAR (light detection and ranging) data were exploited to derive detailed vegetation properties and topographic variables. Random forest (RF) and support vector machine (SVM) approaches were applied to classify soil types in terms of texture, calcareous substrate reaction to hydrochloric acid, and ELC (Ecological Land Classification) moisture regime. The developed methods were tested on data acquired over a boreal region (49° - 50° N, 81° - 84° W) with a combined area of 4,085 km² in the Great Clay Belt (GCB) region, Ontario, Canada. Compared with the field-collected data, the overall accuracies and kappa coefficients of the retrieved soil properties were greater than 0.7 and 0.5, respectively. The accuracies attained between the RF and SVM approaches were similar, but in general the highest accuracies were achieved by the RF method. The models developed for the whole GCB regions generated accuracies comparable to those for the three sub-regions. The lowest modelling uncertainties occurred in areas dominated by peatland, whereas the highest modelling uncertainties existed in the regions with dry moisture regime or clayey soil at the surface. The results also showed that environmental covariates corresponding to vegetation were most important in the prediction of soil properties. Specifically, canopy height model (CHM) and gap fraction derived from LiDAR data, were among the most important variables.
The inclusion of LiDAR-derived covariates demonstrated potential, applied in addition with topographic and climatic covariates and optical imagery. CHM pertains to the vertical dimension, and gap fraction relates to the density of the canopy layer, respectively; both covariates that offer supplemental detail that is not necessarily ascertained for the canopy by optical imagery.
... Besides, this area is also located close to Palangkaraya City, the capital of Central Kalimantan Province [20]. The dynamics in the form of land subsidence and uplift, which often change every period of the rainy and dry season, causes difficulties in monitoring [21], [22]. Also, in this area, there is a deep peat dome. ...
DEM is needed for monitoring peatland dynamics. Currently, available free DEMs have low vertical accuracy and are not up to date. Commercial DEMs have high resolution but is expensive. DEM Pleiades is an example of a commercial DEM. One solution to overcome this problem is to use the latest DTM, which has the advantage of being up to date. This study aims to compare the vertical accuracy of the latest DTM with DEM Pleiades on peatlands. The study area is located on the peatlands of the "Palangkaraya-Pulang Pisau" border. This region has relatively flat topography. The latest DTM is extracted from a combination of InSAR ALOS PALSAR/PALSAR-2 and DInSAR Sentinel. The latest DTM is the integration of the DTM master with the latest displacement. The vertical accuracy of the latest DTM needs to be tested on the DEM Pleiades data with a spatial resolution of 0.5 m and field measurement data using GNSS. DEM Pleiades, the latest DTM, and field measurements using the EGM 2008 for the height reference field. The height data on the DEM Pleiades and the latest DTM were extracted and adjusted for 15 field measurement points. The result obtained is the mean height differences between DEM Pleiades and the latest DTM which is ammounting 0.923 m. The mean height differences between DEM Pleaides and field measurements is 0.557 m. The mean height differences between the latest DTM and field measurements is 0.705 m. Furthermore, a longitudinal profile is made according to 15 field measurement points on the DEM Pleiades and the latest DTM. The results obtained are that DEM Pleiades still has more height errors than the latest DTM. The latest DTM can be an alternative to DEM Pleaides for peatlands mapping with relatively flat topography.
