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1
Nucifera, Fitria, and Sutanto Trijuni Putro. "Deteksi Kerawanan Banjir Genangan Menggunakan Topographic Wetness Index (TWI)." Media Komunikasi Geografi 18, no. 2 (January 5, 2018): 107. http://dx.doi.org/10.23887/mkg.v18i2.12088.
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Flood is the most frequent disaster occured in Indonesia. Flood events result in loss and damage to communities and the environment. Floods are triggered by several factors including hydrometeorological factors, topography, geology, soil and human activities. Topographic factor is one of the flood trigger control factors. Topographic calculation for flood inundation detection can be done by Topographic Wetness Index (TWI) method. The TWI method focuses on topographic conditions of the region, especially the upper slopes and lower slopes to assess the trend of water accumulation in a region. TWI calculations are based on the topography of an area represented by DEM (Digital Elevation Model) data in the form of DTM (Digital Terrain Model). The high value of TWI is associated with high flood vulnerability. Based on the calculation of TWI value, flood-prone areas in Kebumen District include Adimulyo Subdistrict, Puring Subdistrict, Ambal Subdistrict, Rowokele Subdistrict and Buayan Subdistrict.
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Oliveira, Mailson Freire de, Brenda Valeska Ortiz, Guilherme Trimer Morata, Andrés-F. Jiménez, Glauco de Souza Rolim, and Rouverson Pereira da Silva. "Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction." Remote Sensing 14, no. 23 (December 6, 2022): 6171. http://dx.doi.org/10.3390/rs14236171.
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Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.
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Chu, Hone-Jay, Yi-Chin Chen, Muhammad Ali, and Bernhard Höfle. "Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland." International Journal of Environmental Research and Public Health 16, no. 7 (March 28, 2019): 1109. http://dx.doi.org/10.3390/ijerph16071109.
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Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill.
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MA, Jianchao, Guangfa LIN, Youfei CHEN, and Junming CHEN. "The Effect of Terrain Heterogeneity on Topographic Wetness Index." Geo-information Science 13, no. 2 (July 22, 2011): 157–63. http://dx.doi.org/10.3724/sp.j.1047.2011.00157.
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Waga, Katalin, Jukka Malinen, and Timo Tokola. "A Topographic Wetness Index for Forest Road Quality Assessment: An Application in the Lakeland Region of Finland." Forests 11, no. 11 (October 31, 2020): 1165. http://dx.doi.org/10.3390/f11111165.
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Research Highlights: A Topographic Wetness Index calculated using LiDAR-derived elevation models can help in identifying unpaved forest roads that need maintenance. Materials and Methods: Low-pulse LiDAR data were used to calculate a Topographic Wetness Index to predict unpaved forest roads’ quality. Results: The results of this analysis and comparison of road-quality features derived from LiDAR data at resolutions of 1, 10 and 25 m for assessing road quality in the boreal forests of Finnish Lakeland show that the wetness index can predict road quality correctly in up to 70% of cases and up to 86% when combined with other auxiliary GIS-based variables. Conclusions: Road-quality assessments, using airborne LiDAR data, can greatly help forest managers to decide which sections of the ageing road network will benefit the most from maintenance, while reducing the need of field visits.
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Prasad, K., R. Sunilkumar, and B. Sukumar. "Land suitability analysis for agriculture, a case study of Kannur district, Kerala." Geo Eye 7, no. 2 (December 15, 2018): 16–19. http://dx.doi.org/10.53989/bu.ge.v7i2.5.
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Land suitability is the fitness of a given type of land for a defined use. The land may be considered in its present condition or after improvements. There are several methods used for land suitability analysis. In the paper, an attempt is made to derive land suitability classes by considering relief, landforms, slope, aspect, topographic wetness index (TWI), soils, soil texture, and erosion-prone areas. The relief map is prepared by digitizing contours from the Survey of India’s topographic maps in 1:50,000 scale. SRTM data also used to derive contours, slope, aspect, and topographic wetness index. The study area is chosen in Kannur district in Kerala State. It is situated in the northern part of Kerala. All these eight parameters were digitized using ArcGIS software. Weighted overlay analysis was done for identifying land suitability for agriculture, and derived into different classes based on values and labeled as most suitable (S1), moderately suitable (S2), marginally suitable (S3), not suitable (NS1), and not suitable (NS2). This analysis will be useful for identifying the main limiting factors for agricultural production and enables decision-makers to develop crop management able to increase land productivity. Keywords: Land suitability; topographic wetness index; Weighted overlay analysis
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Yong, Bin, Li-Liang Ren, Yang Hong, Jonathan J. Gourley, Xi Chen, You-Jing Zhang, Xiao-Li Yang, Zeng-Xin Zhang, and Wei-Guang Wang. "A novel multiple flow direction algorithm for computing the topographic wetness index." Hydrology Research 43, no. 1-2 (February 1, 2012): 135–45. http://dx.doi.org/10.2166/nh.2011.115.
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The topographic wetness index (TWI), frequently used in approximately characterizing the spatial distribution of soil moisture and surface saturation within a watershed, has been widely applied in topography-related geographical processes and hydrological models. However, it is still questionable whether the current algorithms of TWI can adequately model the spatial distribution of topographic characteristics. Based upon the widely-used multiple flow direction approach (MFD), a novel MFD algorithm (NMFD) is proposed for improving the TWI derivation using a Digital Elevation Model (DEM) in this study. Compared with MFD, NMFD improves the mathematical equations of the contributing area and more precisely calculates the effective contour length. Additionally, a varying exponent strategy is adopted to dynamically determine the downslope flow-partition exponent. Finally, a flow-direction tracking method is employed to address grid cells in flat terrain. The NMFD algorithm is first applied to a catchment located upstream of the Hanjiang River in China to demonstrate its accuracy and improvements. Then NMFD is quantitatively evaluated by using four types of artificial mathematical surfaces. The results indicate that the error generated by NMFD is generally lower than that computed by MFD, and NMFD is able to more accurately represent the hydrological similarity of watersheds.
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Kozłowski, Michał, and Jolanta Komisarek. "Influence of terrain attributes on organic carbon stocks distribution in soil toposequences of central Poland." Soil Science Annual 69, no. 4 (December 1, 2018): 215–22. http://dx.doi.org/10.2478/ssa-2018-0022.
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Abstract The paper presents the results of research on the relationship between topography of undulated morainic plateau of postglacial landscape and distribution of organic carbon stocks in soil toposequences. The mean value of the soil organic carbon stocks (SOCS) for Retisols/Luvisols (RT/LV) was statistically lower than for the Phaeozems/Gleysols (PH/GL) but for RT/LV a higher variation of SOCS in comparison to PH/GL was observed. On the basis of Pearson correlation coefficient, the cartographic depth to water (DTW), the topographic wetness index (TWI) and the saga wetness index (SWI) were the most strongly correlated with the SOCS from among 13 analysed topographic attributes. In addition, the DTW was more correlated with SOCS than other topographic variables. Moreover, the DTW based on the channel networks with 2 ha flow initiation thresholds better correlate with SOCS than DTW obtained on the basis of channel networks with 1 ha and 4 ha flow initiation thresholds. Using Stepwise multiple regression analysis (SMLR), we concluded that the topographic attributes controlling the soil water content and slope shape had most impact on SOCS of the undulated morainic plateau of agricultural ecosystem. In this landform, where the RT/LV and PH/GL soil sequences dominate, the SOCS can be estimated by the DTW, TWI and GC (general curvature) with an estimation error of 0.21 kg m−2. In view of the increasing availability of LiDAR data and power of GIS tools, the use of topographic metrics to assess spatial variability of soil properties will play an increasingly important role in the estimation of soil properties.
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Prawiradisastra, Firman. "FLOOD DISASTER HAZARD ASSESSMENT USING TOPOGRAPHIC WETNESS INDEX IN SERANG DISTRICT." Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana 2, no. 1 (November 27, 2018): 21. http://dx.doi.org/10.29122/alami.v2i1.2817.
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Coping flood hazard risk needs to be done early on. One way of handling floods from the beginning is to predict flood-prone areas with topographic wetness index method. Serang regency is a fairly frequent area of flooding therefore it is necessary to conduct a study to predict flood-prone areas. The total area of flood-prone areas in Serang Regency is 62,608 Ha based on TWI saga modeling results. The area is dominated by high and low class with 25,050 Ha and 29,741 Ha respectively. While for the middle class of 7,817 ha.
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Kopecký, Martin, and Štěpánka Čížková. "Using topographic wetness index in vegetation ecology: does the algorithm matter?" Applied Vegetation Science 13, no. 4 (September 1, 2010): 450–59. http://dx.doi.org/10.1111/j.1654-109x.2010.01083.x.
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Besnard, A. G., I. La Jeunesse, O. Pays, and J. Secondi. "Topographic wetness index predicts the occurrence of bird species in floodplains." Diversity and Distributions 19, no. 8 (January 30, 2013): 955–63. http://dx.doi.org/10.1111/ddi.12047.
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Long, Linli, Ying Liu, Xiaoyang Chen, Junting Guo, Xinhui Li, Yangnan Guo, Xuyang Zhang, and Shaogang Lei. "Analysis of Spatial Variability and Influencing Factors of Soil Nutrients in Western China: A Case Study of the Daliuta Mining Area." Sustainability 14, no. 5 (February 27, 2022): 2793. http://dx.doi.org/10.3390/su14052793.
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An understanding of the spatial variation and influence factors of soil nutrients in mining areas can provide a reference for land reclamation and ecological restoration. Daliuta was used as the study area. The spatial variability of soil nutrients was analyzed using traditional statistics and geostatistics. The effects of topography, mining history, and soil erosion were discussed. The results indicate that the soil pH of the Daliuta mining area is slightly acidic to slightly alkaline, and the soil organic matter, available nitrogen, available phosphorus, and available potassium belonged to the five levels (very low), six (extremely low), five (extremely low), and four (moderately low), respectively. The soil water and salt content indicated that the soil environment in the mining area is arid and has normal levels of salinity. The organic matter, available nitrogen, available phosphorus, available potassium, and soil salt varied moderately, and the pH did not change much, while the soil water varied strongly. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. The organic matter, pH, and soil salinity are moderately spatially autocorrelated, and the available nitrogen, available phosphorus, available potassium, and soil water are weakly spatially autocorrelated. Each nutrient index had a certain spatial trend effect. The slope, aspect, elevation, and topographic wetness index are the primary topographic factors that control the spatial distribution of soil nutrients. Soil erosion and mining history are also important factors that lead to the spatial variation of soil nutrients.
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Zhao, Liang, Yu Liu, and Yong Luo. "Assessing Hydrological Connectivity Mitigated by Reservoirs, Vegetation Cover, and Climate in Yan River Watershed on the Loess Plateau, China: The Network Approach." Water 12, no. 6 (June 18, 2020): 1742. http://dx.doi.org/10.3390/w12061742.
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Hydrologic connectivity is related to the water-mediated transport of matter, energy, and organisms within or between elements of the hydrologic cycle. It reflects the hydrological consequences caused by topographic, land cover, and climatic factors, and is an important tool to characterize and predict the hydrological responses to climate and landscape change. In the Loess Plateau region, a large number of reservoirs have been constructed to trap sediment and storage water for drinking, irrigation, and industries. The land cover has been significantly reshaped in the past decades. These changes may alter the watershed hydrological connectivity. In this study, we mapped the spatial pattern of hydrological connectivity with consideration of reservoir impedances, mitigation of climate, and land cover in the Yan River watershed on the Loess Plateau by using the network index (NI) approach that is based on topographical wetness index. Three wetness indices were used, i.e., topographical wetness index (TWI), SAGA (System for Automated Geoscientific Analyses) wetness index (WIS), and wetness index adopted aridity index (AI) determined by precipitation and evapotranspiration (WIPE). In addition, the effective catchment area (ECA) was also employed to reveal the connectivity of reservoirs and river networks to water source areas. Results show that ECA of reservoirs and rivers account for 35% and 65%, respectively; the hydrological connectivity to the reservoir was lower than that to the river networks. The normalized hydrological connectivity revealed that the connectivity to river channels maintained the same distribution pattern but with a decreased range after construction of reservoirs. As revealed by comparing the spatial patterns of hydrological connectivity quantified by NI based on WIS and WIPE respectively, vegetation cover patterns had significantly alternated watershed hydrological connectivity. These results imply a decreased volume of flow in river channels after reservoir construction, but with same temporal period of flow dynamic. It is illustrated that the network index (NI) is suitable to quantify the hydrological connectivity and it is dynamic in the context of human intervention and climate change.
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Huang, Huabing, Xi Chen, Xianwei Wang, Xina Wang, and Lin Liu. "A Depression-Based Index to Represent Topographic Control in Urban Pluvial Flooding." Water 11, no. 10 (October 12, 2019): 2115. http://dx.doi.org/10.3390/w11102115.
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Extensive studies have highlighted the roles of rainfall, impervious surfaces, and drainage systems in urban pluvial flooding, whereas topographic control has received limited attention. This study proposes a depression-based index, the Topographic Control Index (TCI), to quantify the function of topography in urban pluvial flooding. The TCI of a depression is derived within its catchment, multiplying the catchment area with the slope, then dividing by the ponding volume of the depression. A case study is demonstrated in Guangzhou, China, using a 0.5 m-resolution Digital Elevation Model (DEM) acquired using Light Detection and Ranging (LiDAR) technology. The results show that the TCI map matches well with flooding records, while the Topographic Wetness Index (TWI) cannot map the frequently flooded areas. The impact of DEM resolution on topographic representation and the stability of TCI values are further investigated. The original 0.5 m-resolution DEM is set as a baseline, and is resampled at resolutions 1, 2, 5, and 10 m. A 1 m resolution has the smallest TCI deviation from those of 0.5 m resolution, and gives the optimal results in terms of striking a balance between computational efficiency and precision of representation. Moreover, the uncertainty in TCI values is likely to increase for small depressions.
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Zádorová, T., D. Žížala, V. Penížek, and Š. Čejková. "Relating extent of colluvial soils to topographic derivatives and soil variables in a Luvisol sub-catchment, Central Bohemia, Czech Republic." Soil and Water Research 9, No. 2 (April 25, 2014): 47–57. http://dx.doi.org/10.17221/57/2013-swr.
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Colluvial soils, resulting from accelerated soil erosion, represent a significant part of the soil cover pattern in agricultural landscapes. Their specific terrain position makes it possible to map them using geostatistics and digital terrain modelling. A study of the relationship between colluvial soil extent and terrain and soil variables was performed at a morphologically diverse study site in a Luvisol soil region in Central Bohemia. Assessment of the specificity of the colluviation process with regard to profile characteristics of Luvisols was another goal of the study. A detailed field survey, statistical analyses, and detailed digital elevation model processing were the main methods utilized in the study. Statistical analysis showed a strong relationship between the occurrence of colluvial soil, various topographic derivatives, and soil organic carbon content. A multiple range test proved that four topographic derivatives significantly distinguish colluvial soil from other soil units and can be then used for colluvial soil delineation. Topographic wetness index was evaluated as the most appropriate terrain predictor. Soil organic carbon content was significantly correlated with five topographic derivatives, most strongly with topographic wetness index (TWI) and plan curvature. Redistribution of the soil material at the study site is intensive but not as significant as in loess regions covered by Chernozem. Soil mass transport is limited mainly to the A horizon; an argic horizon is truncated only at the steepest parts of the slope.
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Ruhoff, Anderson Luis, Nilza Maria Reis Castro, and Alfonso Risso. "Numerical Modelling of the Topographic Wetness Index: An Analysis at Different Scales." International Journal of Geosciences 02, no. 04 (2011): 476–83. http://dx.doi.org/10.4236/ijg.2011.24050.
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Qin, Cheng-Zhi, A.-Xing Zhu, Tao Pei, Bao-Lin Li, Thomas Scholten, Thorsten Behrens, and Cheng-Hu Zhou. "An approach to computing topographic wetness index based on maximum downslope gradient." Precision Agriculture 12, no. 1 (December 22, 2009): 32–43. http://dx.doi.org/10.1007/s11119-009-9152-y.
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JOHN, Kingsley, Isong Abraham Isong, Ndiye Michael Kebonye, Esther Okon Ayito, Prince Chapman Agyeman, and Sunday Marcus Afu. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil." Land 9, no. 12 (December 2, 2020): 487. http://dx.doi.org/10.3390/land9120487.
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Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.
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Bian, Linlong, Assefa M. Melesse, Arturo S. Leon, Vivek Verma, and Zeda Yin. "A Deterministic Topographic Wetland Index Based on LiDAR-Derived DEM for Delineating Open-Water Wetlands." Water 13, no. 18 (September 10, 2021): 2487. http://dx.doi.org/10.3390/w13182487.
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Wetlands play a significant role in flood mitigation. Remote sensing technologies as an efficient and accurate approach have been widely applied to delineate wetlands. Supervised classification is conventionally applied for remote sensing technologies to improve the wetland delineation accuracy. However, performing supervised classification requires preparing the training data, which is also considered time-consuming and prone to human mistakes. This paper presents a deterministic topographic wetland index to delineate wetland inundation areas without performing supervised classification. The classic methods such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Topographic Wetness Index were chosen to compare with the proposed deterministic topographic method on wetland delineation accuracy. The ground truth sample points validated by Google satellite imageries from four different years were used for the assessment of the delineation overall accuracy. The results show that the proposed deterministic topographic wetland index has the highest overall accuracy (98.90%) and Kappa coefficient (0.641) among the selected approaches in this study. The findings of this paper will provide an alternative approach for delineating wetlands rapidly by using solely the LiDAR-derived Digital Elevation Model.
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Berhanu, Belete, and Ethiopia Bisrat. "Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI)." Journal of Natural Resources and Development 8 (September 7, 2018): 91–100. http://dx.doi.org/10.5027/jnrd.v8i0.09.
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Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.
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Ilia, I., D. Rozos, and I. Koumantakis. "Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece." Bulletin of the Geological Society of Greece 47, no. 1 (December 21, 2016): 264. http://dx.doi.org/10.12681/bgsg.10940.
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The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter
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Raduła, Małgorzata W., Tomasz H. Szymura, and Magdalena Szymura. "Topographic wetness index explains soil moisture better than bioindication with Ellenberg’s indicator values." Ecological Indicators 85 (February 2018): 172–79. http://dx.doi.org/10.1016/j.ecolind.2017.10.011.
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Sørensen, R., U. Zinko, and J. Seibert. "On the calculation of the topographic wetness index: evaluation of different methods based on field observations." Hydrology and Earth System Sciences Discussions 2, no. 4 (August 31, 2005): 1807–34. http://dx.doi.org/10.5194/hessd-2-1807-2005.
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Abstract. The topographic wetness index (TWI, ln(a/tanβ)), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables, but we were able to identify the general characteristics of the best methods for different groups of measured variables. The results provide guidelines for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.
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Sørensen, R., U. Zinko, and J. Seibert. "On the calculation of the topographic wetness index: evaluation of different methods based on field observations." Hydrology and Earth System Sciences 10, no. 1 (February 15, 2006): 101–12. http://dx.doi.org/10.5194/hess-10-101-2006.
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Abstract. The topographic wetness index (TWI, ln(a/tanβ)), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables; rather the best methods seemed to be variable and site specific. However, we were able to identify some general characteristics of the best methods for different groups of measured variables. The results provide guiding principles for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.
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Hasanloo, M., P. Pahlavani, and B. Bigdeli. "FLOOD RISK ZONATION USING A MULTI-CRITERIA SPATIAL GROUP FUZZY-AHP DECISION MAKING AND FUZZY OVERLAY ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 455–60. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-455-2019.
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Abstract. In this study, a GIS based approach has been proposed for the flood risk zonation based on a multi-criteria spatial group fuzzy AHP decision making analysis and its integration with fuzzy overlay analysis. For this purpose, 10 layers affecting flood occurrence have been used including: the Digital Elevation Model (DEM), Slope, NDVI, Flow Accumulation (Flow Ac.), HOFD, VOFD, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Curve Number (CN), Modified Fournier Index. Each layer was classified into 5 sub-classes and their preference at its layer was weighted by a group of experts using fuzzy analytical hierarchy processes (GFAHP) method. Finally, the risk map of the studied area with the weight of experts and fuzzy overlay method was product and divided into five categories.
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Miardini, Arina, and Grace Serepina Saragih. "PENENTUAN PRIORITAS PENANGANAN BANJIR GENANGAN BERDASARKAN TINGKAT KERAWANAN MENGGUNAKAN TOPOGRAPHIC WETNESS INDEX Studi Kasus di DAS Solo." Jurnal Ilmu Lingkungan 17, no. 1 (May 29, 2019): 113. http://dx.doi.org/10.14710/jil.17.1.113-119.
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The increasing frequency of flood events is an indication of the failure of watershed management. Natural resource utilization activities in the Solo watershed tend to be intensive from upstream to downstream, cause a decrease in the carrying capacity of the watershed. To restore the carrying capacity of the watershed, efforts are needed to monitor and evaluate watersheds. The initial stage that needs to be done is to ensure the accuracy of the flood-prone areas by determining priority areas. The purpose of the study is to determine the flood-prone areas in the Solo watershed based on the level of flood vulnerability. Flood vulnerability is influenced by topographic conditions. The Topographic Wetness Index (TWI) method was used to determine the flood-prone areas. The high TWI value indicates that the area has high flood vulnerability and is associated with flat topography with high flow density. This method is based on raster data was derived from DEM 30 m data which is reduced to slope through spatial analysis tools and the accumulation flow is analyzed using Watershed Delineation Tools (WDT). Based on the results of the analysis, the priority of flood handling is determined in the criterion-very vulnerable area with TWI 11.65-38.30 identified as 387098.23 ha (39.68%). Flood handling in the Solo watershed is prioritized on 1) Bojonegoro Regency covering 105215.13 ha, 2) Ngawi (56810.68 ha), 3) Madiun (44102.06 ha), 4) Tuban covering an area of 43072.06 ha, and 5) Ponorogo (35853.62 ha).
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Ågren, A. M., W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp. "Evaluating digital terrain indices for soil wetness mapping – a Swedish case study." Hydrology and Earth System Sciences Discussions 11, no. 4 (April 11, 2014): 4103–29. http://dx.doi.org/10.5194/hessd-11-4103-2014.
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Abstract. Driving with forestry machines on wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are more susceptible to rutting. It is important to model and map the extent of these areas and associated wetness variations. This can be done with adequate reliability using high resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically fairly robust. Since the DTW derivations vary by the area threshold used for setting stream flow initiation we found that the optimal threshold values varied by landform, e.g., 1–2 ha for till-derived landforms vs. 8 –16 ha for a coarse-textured alluvial floodplain.
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Kopecký, Martin, Martin Macek, and Jan Wild. "Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition." Science of The Total Environment 757 (February 2021): 143785. http://dx.doi.org/10.1016/j.scitotenv.2020.143785.
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Jancewicz, Kacper, Piotr Migoń, and Marek Kasprzak. "Connectivity patterns in contrasting types of tableland sandstone relief revealed by Topographic Wetness Index." Science of The Total Environment 656 (March 2019): 1046–62. http://dx.doi.org/10.1016/j.scitotenv.2018.11.467.
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Petroselli, Andrea, Federico Vessella, Lucia Cavagnuolo, Gianluca Piovesan, and Bartolomeo Schirone. "Ecological behavior of Quercus suber and Quercus ilex inferred by topographic wetness index (TWI)." Trees 27, no. 5 (March 14, 2013): 1201–15. http://dx.doi.org/10.1007/s00468-013-0869-x.
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Pavlin, Lovrenc, Borbála Széles, Peter Strauss, Alfred Paul Blaschke, and Günter Blöschl. "Event and seasonal hydrologic connectivity patterns in an agricultural headwater catchment." Hydrology and Earth System Sciences 25, no. 4 (April 29, 2021): 2327–52. http://dx.doi.org/10.5194/hess-25-2327-2021.
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Abstract. Connectivity of the hillslope and the stream is a non-stationary and non-linear phenomenon dependent on many controls. The objective of this study is to identify these controls by examining the spatial and temporal patterns of the similarity between shallow groundwater and soil moisture dynamics and streamflow dynamics in the Hydrological Open Air Laboratory (HOAL), a small (66 ha) agricultural headwater catchment in Lower Austria. We investigate the responses to 53 precipitation events and the seasonal dynamics of streamflow, groundwater and soil moisture over 2 years. The similarity, in terms of Spearman correlation coefficient, hysteresis index and peak-to-peak time, of groundwater to streamflow shows a clear spatial organization, which is best correlated with topographic position index, topographic wetness index and depth to the groundwater table. The similarity is greatest in the riparian zone and diminishes further away from the stream where the groundwater table is deeper. Soil moisture dynamics show high similarity to streamflow but no clear spatial pattern. This is reflected in a low correlation of the similarity with site characteristics. However, the similarity increases with increasing catchment wetness and rainfall duration. Groundwater connectivity to the stream on the seasonal scale is higher than that on the event scale, indicating that groundwater contributes more to the baseflow than to event runoff.
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Laamrani, Ahmed, and Osvaldo Valeria. "Ranking Importance of Topographical Surface and Subsurface Parameters on Paludification in Northern Boreal Forests Using Very High Resolution Remotely Sensed Datasets." Sustainability 12, no. 2 (January 13, 2020): 577. http://dx.doi.org/10.3390/su12020577.
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The accumulation of organic material on top of the mineral soil over time (a process called paludification) is common in Northern Boreal coniferous forests. This natural process leads to a marked decrease in forest productivity overtime. Topography both at the surface of the forest floor (i.e., above ground) and the subsurface (i.e., top of mineral soil which is underground) is known to play a critical role in the paludification process. Until recently, the availability of more accurate topographic information regarding the surface and subsurface was a limiting factor for land management and modeling of spatial organic layer thickness (OLT) variability, a proxy for paludification. However so far, no research has assessed which of these two topographic variables has the greatest influence on paludification. This study aims to assess which topographic variable (surface or subsurface) better explains paludification, using high-resolution remote sensing technology (i.e., Light Detection and Ranging: LiDAR and Ground Penetrating Radar: GPR). To this end, field soil measurements were made in over 1614 sites distributed throughout the reference Valrennes Experimental site in Canadian northern coniferous forests. Then, a machine learning model (i.e., Random Forest, RF) was implemented to rank a set of selected predictor topographic variables (i.e., slope, aspect, mean curvature, plan curvature, profile curvature, and topographic wetness index) using the Mean Decrease Gini (MDG) index as an indicator of importance. Results showed that overall 83% of the overall variance was explained by the RF selected model, while the derived subsurface topography predictors had the lowest MDGs for predicting paludification. On the other hand, the surface slope predictor had the highest MDGs and better explained paludification. This finding would be particularly useful for implanting sustainable management strategies based on the surface variables of paludified northern boreal forests. This study has also highlighted the potential of LiDAR data to provide surface topographic spatial detail information for planning and optimizing forest management activities in paludified boreal forests. This is even of great importance when we know that LiDAR variables are easier to obtain compared to GPR derived variables (subsurface topographic variables).
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Eyre, Riley, John Lindsay, Ahmed Laamrani, and Aaron Berg. "Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models." Remote Sensing 13, no. 20 (October 16, 2021): 4152. http://dx.doi.org/10.3390/rs13204152.
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Accurate yield estimation and optimized agricultural management is a key goal in precision agriculture, while depending on many different production attributes, such as soil properties, fertilizer and irrigation management, the weather, and topography.The need for timely and accurate sensing of these inputs at the within field-scale has led to increased adoption of very high-resolution remote and proximal sensing technologies. With regard to topography attributes, greater attention is currently being devoted to LiDAR datasets (Light Detection and Ranging), mainly because numerous topographic variables can be derived at very high spatial resolution from these datasets. The current study uses LiDAR elevation data from agricultural land in southern Ontario, Canada to derive several topographic attributes such as slope, and topographic wetness index, which were then correlated to seven years of crop yield data. The effectiveness of each topographic derivative was independently tested using a moving-window correlation technique. Finally, the correlated derivatives were selected as explanatory variables for geographically weighted regression (GWR) models. The global coefficient of determination values (determined from an average of all the local relationships) were found to be R2 = 0.80 for corn, R2 = 0.73 for wheat, R2 = 0.71 for soybeans and R2 = 0.75 for the average of all crops. These results indicate that GWR models using topographic variables derived from LiDAR can effectively explain yield variation of several crop types on an entire-field scale.
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Newman, Evan A., R. Edward DeWalt, and Scott A. Grubbs. "Plecoptera (Insecta) Diversity in Indiana: A Watershed-Based Analysis." Diversity 13, no. 12 (December 15, 2021): 672. http://dx.doi.org/10.3390/d13120672.
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Plecoptera, an environmentally sensitive order of aquatic insects commonly used in water quality monitoring is experiencing decline across the globe. This study addresses the landscape factors that impact the species richness of stoneflies using the US Geological Survey Hierarchical Unit Code 8 drainage scale (HUC8) in the state of Indiana. Over 6300 specimen records from regional museums, literature, and recent efforts were assigned to HUC8 drainages. A total of 93 species were recorded from the state. The three richest of 38 HUC8s were the Lower East Fork White (66 species), the Blue-Sinking (58), and the Lower White (51) drainages, all concentrated in the southern unglaciated part of the state. Richness was predicted using nine variables, reduced from 116 and subjected to AICc importance and hierarchical partitioning. AICc importance revealed four variables associated with Plecoptera species richness, topographic wetness index, HUC8 area, % soil hydrolgroup C/D, and % historic wetland ecosystem. Hierarchical partitioning indicated topographic wetness index, HUC8 area, and % cherty red clay surface geology as significantly important to predicting species richness. This analysis highlights the importance of hydrology and glacial history in species richness of Plecoptera. The accumulated data are primed to be used for monograph production, niche modeling, and conservation status assessment for an entire assemblage in a large geographic area.
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Kumhálová, J., F. Zemek, P. Novák, O. Brovkina, and M. Mayerová. "Use of Landsat images for yield evaluation within a small plot." Plant, Soil and Environment 60, No. 11 (November 4, 2014): 501–6. http://dx.doi.org/10.17221/515/2014-pse.
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Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was –0.816 in the image acquisition date of 4. 6. 2011.
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Ågren, A. M., W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp. "Evaluating digital terrain indices for soil wetness mapping – a Swedish case study." Hydrology and Earth System Sciences 18, no. 9 (September 12, 2014): 3623–34. http://dx.doi.org/10.5194/hess-18-3623-2014.
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Abstract. Trafficking wet soils within and near stream and lake buffers can cause soil disturbances, i.e. rutting and compaction. This – in turn – can lead to increased surface flow, thereby facilitating the leaking of unwanted substances into downstream environments. Wet soils in mires, near streams and lakes have particularly low bearing capacity and are therefore more susceptible to rutting. It is therefore important to model and map the extent of these areas and associated wetness variations. This can now be done with adequate reliability using a high-resolution digital elevation model (DEM). In this article, we report on several digital terrain indices to predict soil wetness by wet-area locations. We varied the resolution of these indices to test what scale produces the best possible wet-areas mapping conformance. We found that topographic wetness index (TWI) and the newly developed cartographic depth-to-water index (DTW) were the best soil wetness predictors. While the TWI derivations were sensitive to scale, the DTW derivations were not and were therefore numerically robust. Since the DTW derivations vary by the area threshold for setting stream flow initiation, we found that the optimal threshold values for permanently wet areas varied by landform within the Krycklan watershed, e.g. 1–2 ha for till-derived landforms versus 8–16 ha for a coarse-textured alluvial floodplain.
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Luca, Carol, Bing C. Si, and Richard E. Farrell. "Upslope length improves spatial estimation of soil organic carbon content." Canadian Journal of Soil Science 87, no. 3 (May 1, 2007): 291–300. http://dx.doi.org/10.4141/cjss06012.
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Quantifying soil organic carbon (SOC) is important to aide in assessing carbon (C) sequestration potential, and as an indicator of soil quality. However, intensive s ampling of SOC for quantification can be expensive and time consuming. The objectives of this study were to identify which topographic index correlated best with SOC and determine if incorporating the index improved interpolation of limited SOC data. A transect with 93 sample points spaced 6 m apart was set up, and four topographical indices (curvature, wetness index, upslope length, and elevation) were evaluated for their potential as secondary variables. Three Kriging-based interpolation methods, ordinary kriging, cokriging, and simple kriging with varying local means were compared to determine if incorporating topographical indices improved interpolation of SOC. The upslope length, which takes into consideration the quantity of water that will be redistributed to a point, was found to have the strongest relationship with SOC (R2 = 0.48, P < 0.01) and was used as a secondary variable for kriging. Thirty points from the SOC data were randomly selected and used in the kriging algorithms to estimate the remain ing 63 points. The sum of squared differences (SSD) showed a significant reduction (from 1677 to 1455 for SKlm and from 1677 to 1464 for cokriging) in estimates when upslope length was used as a secondary variable. These results indicate that fewer samples may be taken to estimate SOC accurately and precisely if upslope length is incorporated. On a landscape scale this could facilitate quantification of carbon credits and management decisions in precision farming systems. Key words: Geostatics, kriging, cokriging, organic carbon, landscape processes, wetness index
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Bou Kheir, R., P. K. Bøcher, M. B. Greve, and M. H. Greve. "The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data." Hydrology and Earth System Sciences Discussions 7, no. 1 (January 18, 2010): 389–416. http://dx.doi.org/10.5194/hessd-7-389-2010.
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Abstract. Accurate information about soil organic carbon (SOC), presented in a spatially form, is prerequisite for many land resources management applications (including climate change mitigation). This paper aims to investigate the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes at unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to statistically explain SOC field measurements in hydromorphic landscapes of the chosen Danish area. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morphological/hydrological) parameters only, (3) selected pairs of parameters and (4) excluding each parameter one at a time from the potential pool of predictor parameters. The best classification tree model (with the lowest misclassification error and the smallest number of terminal nodes and predictor parameters) combined the steady-state topographic wetness index and soil type, and explained 68% of the variability in field SOC measurements. The overall accuracy of the produced predictive SOC map (at 1:50 000 cartographic scale) using the best tree was estimated to be ca. 75%. The proposed classification-tree model is relatively simple, quick, realistic and practical, and it can be applied to other areas, thereby providing a tool to help with the implementation of pedological/hydrological plans for conservation and sustainable management. It is particularly useful when information about soil properties from conventional field surveys is limited.
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Bou Kheir, R., P. K. Bøcher, M. B. Greve, and M. H. Greve. "The application of GIS based decision-tree models for generating the spatial distribution of hydromorphic organic landscapes in relation to digital terrain data." Hydrology and Earth System Sciences 14, no. 6 (June 1, 2010): 847–57. http://dx.doi.org/10.5194/hess-14-847-2010.
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Abstract. Accurate information about organic/mineral soil occurrence is a prerequisite for many land resources management applications (including climate change mitigation). This paper aims at investigating the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes in unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to explain organic/mineral field measurements in hydromorphic landscapes of the Danish area chosen. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morphological/hydrological) parameters only, (3) selected pairs of parameters and (4) excluding each parameter one at a time from the potential pool of predictor parameters. The best classification tree model (with the lowest misclassification error and the smallest number of terminal nodes and predictor parameters) combined the steady-state topographic wetness index and soil type, and explained 68% of the variability in organic/mineral field measurements. The overall accuracy of the predictive organic/inorganic landscapes' map produced (at 1:50 000 cartographic scale) using the best tree was estimated to be ca. 75%. The proposed classification-tree model is relatively simple, quick, realistic and practical, and it can be applied to other areas, thereby providing a tool to facilitate the implementation of pedological/hydrological plans for conservation and sustainable management. It is particularly useful when information about soil properties from conventional field surveys is limited.
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Devadoss, Jashvina, Nicola Falco, Baptiste Dafflon, Yuxin Wu, Maya Franklin, Anna Hermes, Eve-Lyn S. Hinckley, and Haruko Wainwright. "Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem." Remote Sensing 12, no. 17 (August 24, 2020): 2733. http://dx.doi.org/10.3390/rs12172733.
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In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.
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Li, Yingkui, Xiaoyu Lu, Robert A. Washington-Allen, and Yanan Li. "Microtopographic Controls on Erosion and Deposition of a Rilled Hillslope in Eastern Tennessee, USA." Remote Sensing 14, no. 6 (March 9, 2022): 1315. http://dx.doi.org/10.3390/rs14061315.
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Topography plays an important role in shaping the patterns of sediment erosion and deposition of different landscapes. Studies have investigated the role of topography at basin scales, whereas little work has been conducted on hillslopes, partially due to the lack of high-resolution topographic data. We monitored detailed topographic changes of a rilled hillslope in the southeastern United States using terrestrial laser scanning and investigated the influences of various microtopographic factors on erosion and deposition. The results suggest that the contributing area is the most important factor for both rill erosion and deposition. Rills with large contributing areas tend to have high erosion and deposition. Slope is positively related to erosion but negatively related to deposition. Roughness, on the other hand, is positively related to deposition but negatively related to erosion. Higher erosion and lower deposition likely occur on north-facing aspects, possibly because of higher soil moisture resulting from less received solar insolation. Similarly, soil moisture is likely higher in areas with higher terrain wetness index values, leading to higher erosion. This work provides important insight into the sediment dynamic and its microtopographic controls on hillslopes.
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Andersson, J. O., and L. Nyberg. "Relations between topography, wetlands, vegetation cover and stream water chemistry in boreal headwater catchments in Sweden." Hydrology and Earth System Sciences Discussions 5, no. 3 (May 29, 2008): 1191–226. http://dx.doi.org/10.5194/hessd-5-1191-2008.
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Abstract. A large part of the spatial variation of stream water chemistry is found in headwater streams and small catchments. To understand the dominant processes, taking place in small and heterogeneous catchments, spatial and temporal data with high resolution is needed. In most cases available map data has too low quality and resolution to successfully be used in environmental assessments and modelling. In this study 18 forested catchments (1–4 km2) were selected within a 120×50 km area in the county of Värmland in western Sweden. The aim was to test if topographic and vegetation variables derived from official datasets were correlated to stream water chemistry, represented by DOC, Al, Fe and Si content. A GIS was used to analyse the elevation characteristics, generate topographic indices and calculate the percentage of wetlands and a number of vegetation classes. The results clearly show that the topography has a major influence on the occurrence of wetlands, which has a major influence on stream water chemistry. There were very strong correlations between mean slope and percentage wetland, percentage wetland and DOC, mean slope and DOC and mean topographic wetness index and DOC. The conclusion was that official topographic data, despite uncertain or low quality and resolution, could be useful in the prediction of headwater chemistry in boreal forested catchments.
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Andersson, J. O., and L. Nyberg. "Using official map data on topography, wetlands and vegetation cover for prediction of stream water chemistry in boreal headwater catchments." Hydrology and Earth System Sciences 13, no. 4 (April 27, 2009): 537–49. http://dx.doi.org/10.5194/hess-13-537-2009.
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Abstract. A large part of the spatial variation of stream water chemistry can be related to inputs from headwater streams. In order to understand and analyse the dominant processes taking place in small and heterogeneous catchments, accurate data with high spatial and temporal resolution is necessary. In most cases, the quality and resolution of available map data are considered too poor to be used in environmental assessments and modelling of headwater stream chemistry. In this study 18 forested catchments (1–4 km2) were selected within a 120×50 km region in the county of Värmland in western Sweden. The aim was to test if topographic and vegetation variables derived from official datasets were correlated to stream water chemistry, primarily the concentration of dissolved organic carbon (DOC), but also Al, Fe and Si content. GIS was used to analyse the elevation characteristics, generate topographic indices, and calculate the percentage of wetlands and a number of vegetation classes. The results clearly show that topography has a major influence on stream water chemistry. There were strong correlations between mean slope and percentage wetland, percentage wetland and DOC, mean slope and DOC, and a very strong correlation between mean topographic wetness index (TWI) and DOC. The conclusion was that official topographic data, despite uncertain or of low quality and resolution, could be useful in the prediction of headwater DOC-concentration in boreal forested catchments.
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Meena, Sansar Raj, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani. "Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)." Natural Hazards and Earth System Sciences 22, no. 4 (April 21, 2022): 1395–417. http://dx.doi.org/10.5194/nhess-22-1395-2022.
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Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.
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Carreno-Luengo, Hugo, Guido Luzi, and Michele Crosetto. "First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model." Remote Sensing 11, no. 21 (October 31, 2019): 2556. http://dx.doi.org/10.3390/rs11212556.
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Understanding the effects of Earth’s surface topography on Global Navigation Satellite Systems Reflectometry (GNSS-R) space-borne data is important to calibrate experimental measurements, so as to provide accurate soil moisture content (SMC) retrievals. In this study, several scientific observables obtained from delay-Doppler maps (DDMs) ⟨ | Y r , t o p o ( τ , f ) | 2 ⟩ generated on board the Cyclone Global Navigation Satellite System (CyGNSS) mission were evaluated as a function of several topographic parameters derived from a digital elevation model (DEM). This assessment was performed as a function of Soil Moisture Active Passive (SMAP)-derived SMC at grazing angles θ e ~ [20,30] ° and in a nadir-looking configuration θ e ~ [80,90] °. Global scale results showed that the width of the trailing edge (TE) was small T E ~ [100, 250] m and the reflectivity was high Γ ~ [–10, –3] dB over flat areas with low topographic heterogeneity, because of an increasing coherence of Earth-reflected Global Positioning System (GPS) signals. However, the strong impact of several topographic features over areas with rough topography provided motivation to perform a parametric analysis. A specific target area with little vegetation, low small-scale surface roughness, and a wide variety of terrains in South Asia was selected. A significant influence of several topographic parameters i.e., surface slopes and curvatures was observed. This triggered our study of the sensitivity of T E and Γ to SMC and topographic wetness index ( T W I ). Regional scale results showed that T E and Γ are strongly correlated with the T W I , while the sensitivity to SMC was almost negligible. The Pearson correlation coefficients of T E and Γ with T W I are r Γ ~ 0.59 and r T E ~−0.63 at θ e ~ [20, 30] ° and r Γ ~ 0.48 and r T E ~ −0.50 at θ e ~ [80, 90] °, respectively.
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Ayele, Gebiaw T., Solomon S. Demissie, Mengistu A. Jemberrie, Jaehak Jeong, and David P. Hamilton. "Terrain Effects on the Spatial Variability of Soil Physical and Chemical Properties." Soil Systems 4, no. 1 (December 20, 2019): 1. http://dx.doi.org/10.3390/soilsystems4010001.
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Understanding topography effects on soil properties is vital to modelling landscape hydrology and establishing sustainable on-field management practices. This research focuses on an arable area (117 km2) in Southwestern Ethiopia where agricultural fields and bush cover are the dominant land uses. We postulate that adapting either of the soil data resources, coarse resolution FAO-UNESCO (Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization) soil data or pedo-transfer functions (PTFs) is not reliable to indicate future watershed management directions. The FAO-UNESCO data does not account for scale issues and assigns the same soil property at different landscape gradients. The PTFs, on the other hand, do not account for environmental effects and fail to provide all the required data. In this regard, mapping soil property spatial dynamics can help understand landscape physicochemical processes and corresponding land use changes. For this purpose, soil samples were collected across the watershed following a gridded sampling scheme. In areas with heterogeneous topography, soil is spatially variable as influenced by land use and slope. To understand the spatial variation, this research develops indicators, such as topographic index, soil topographic wetness index, elevation, aspect, and slope. Pearson correlation (r), among others, was used to investigate terrain effects on selected soil properties: organic matter (OM), available water content (AWC), sand content (%), clay content (%), silt content (%), electrical conductivity (EC), moist bulk density (MBD), and saturated hydraulic conductivity (Ksat). The results show that there were statistically significant correlations between elevation-based variables and soil physical properties. Among the variables considered, the ‘r’ value between topographic index and soil attributes (i.e., OM, EC, AWC, sand, clay, silt, and Ksat) were 0.66, 0.5, 0.7, 0.55, 0.62, 0.4, and 0.66, respectively. In conclusion, while understanding topography effects on soil properties is vital, implementing either FAO-UNESCO or PTFs soil data do not provide appropriate information pertaining to scale issues.
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Umbara, Raditya Panji, and Deliyanti Ganesha. "PEMETAAN BAHAYA BANJIR DI KABUPATEN BANGGAI KEPULAUAN." Jurnal Sains dan Teknologi Mitigasi Bencana 12, no. 1 (August 6, 2019): 10–20. http://dx.doi.org/10.29122/jstmb.v12i1.3696.
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Pemetaan Bahaya Banjir di Kabupaten Banggai Kepulauan telah dilakukan dengan menggunakan metode TWI (Topographic Wetness Index). Metode tersebut menggunakan variabel topografi secara dominan dengan tetap mempertimbangkan variabel curah hujan, penggunaan lahan, geologi dan data historis banjir. Hasil perhitungan dan analisis data diperkuat dengan survei lapangan. Peta Bahaya Banjir di Kabupaten Banggai Kepulauan terbagi menjadi 3 klasifikasi, yaitu tinggi, sedang dan rendah. Wilayah dengan bahaya banjir tinggi dan daerah langganan banjir berdasarkan BPBD Kabupaten Banggai Kepulauan berada di Desa Ponding Ponding, Tatakalai, Luk Sago Kecamatan Tinangkung Utara serta di Desa Lopito Kecamatan Totikum.
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Azedou, Ali, Said Lahssini, Abdellatif Khattabi, Modeste Meliho, and Nabil Rifai. "A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)." Sustainability 13, no. 2 (January 12, 2021): 682. http://dx.doi.org/10.3390/su13020682.
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Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods).
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Bałazy, Radomir, Agnieszka Kamińska, Mariusz Ciesielski, Jarosław Socha, and Marcin Pierzchalski. "Modeling the Effect of Environmental and Topographic Variables Affecting the Height Increment of Norway Spruce Stands in Mountainous Conditions with the Use of LiDAR Data." Remote Sensing 11, no. 20 (October 17, 2019): 2407. http://dx.doi.org/10.3390/rs11202407.
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Differing levels of humidity, sunlight exposure or temperature in different areas of mountain ranges are fundamental to the existence of particular vegetation types. A better understanding of even local variability of trees may bring significant benefits, not only economic, but most of all, nature-related. The main focus of this study was the analysis of relationships between increment in stand height, age and the natural topography in the examined area. Among others, the following were examined with regard to their influence on the growing process: age, altitude above sea level (m a.s.l.), aspect and slope, topographic wetness index (TWI), and topographic position index (TPI) generated from an airborne laser scanning (ALS)-derived elevation model. To precisely calculate forest growth dynamics in mountain conditions for different spruce stands, repeated airborne lidar measurements from 2007 and 2012 were used (with resolution respectively 4 and 6 pts./m2). Detailed information on every stand including species composition, share of individual species, as well as their age, were acquired from the State Forests IT System (SILP). It was proven in this study, that environmental and topographic variables may have an impact on forest growth dynamics on even closely located areas. Apart from the age, the greatest influence on tree growth has an altitude above sea level, aspect and slope. The highest height increment of spruce was observed in the stands of up to 30 years old, those that had grown at an altitude under 850 m a.s.l., on the slopes up to 15 degrees or on those which were on the northeastern exposure. The results obtained show that the physiology of species, even those that are well known, largely depends on local topographic conditions. The proven impact of different topography factors on the growth of spruce may be used while planning economic activities in precision forestry. Additional research with using multiple laser scanning in the context of other regions or other species may bring us better recognition of local growth conditions and in consequence, significantly better planning and higher revenues obtained from the sale of trees.
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Khan, M. Nazish, M. Kashif, and A. Shah. "Off-Road Trafficability for Military Operations Using Multi-Criteria Decision Analysis." International Journal of Advanced Remote Sensing and GIS 10, no. 1 (March 16, 2021): 3425–37. http://dx.doi.org/10.23953/cloud.ijarsg.489.
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This study has been carried out in the Pathankot region, having strategic importance in terms of military operations. It explores the ability of remote sensing and GIS in assessing off-road trafficability which is integral part of terrain intelligence. Number of thematic layers has been prepared using Sentinal -2 satellite images and PALSAR Digital Elevation Model (DEM) viz. LULC, Slope, Topographic Wetness Index (TWI), Terrain Roughness Index (TRI) and ground conditions to assess the potential of off-road trafficability in the study area for military operations. Results clearly depict that most of the region is suitable for off-road movement. However, north western part is showing less suitability. Keywords PALSAR; Multi-criteria Decision Analysis; AHP; Trafficability