Application of the topographic position index to heterogeneous landscapes

Geomorphology ◽  
2013 ◽  
Vol 186 ◽  
pp. 39-49 ◽  
Author(s):  
Jeroen De Reu ◽  
Jean Bourgeois ◽  
Machteld Bats ◽  
Ann Zwertvaegher ◽  
Vanessa Gelorini ◽  
...  
Keyword(s):  
Topographic Position ◽  
Topographic Position Index ◽  
Heterogeneous Landscapes ◽  
Position Index

scholarly journals A Parsimonious Approach to Estimate Soil Organic Carbon Applying Unmanned Aerial System (UAS) Multispectral Imagery and the Topographic Position Index in a Heterogeneous Soil Landscape

2021 ◽  
Vol 13 (18) ◽  
pp. 3557
Author(s):  
Marc Wehrhan ◽  
Michael Sommer
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Errors ◽  
Field Scale ◽  
Local Approach ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Soil Landscape ◽  
Position Index

Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils’ SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R²) = 0.91; root mean square error (RMSE) = 0.11% and R² = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R² = 0.88, RMSE = 0.07%; R² = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.

scholarly journals Evolution of Habitat Quality and Its Topographic Gradient Effect in Northwest Hubei Province from 2000 to 2020 Based on the InVEST Model

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 857
Author(s):  
Mengyao Li ◽  
Yong Zhou ◽  
Pengnan Xiao ◽  
Yang Tian ◽  
He Huang ◽  
...  
Keyword(s):  
Land Use ◽  
Habitat Quality ◽  
Cultivated Land ◽  
Topographic Position ◽  
Invest Model ◽  
Topographic Position Index ◽  
Gradient Effect ◽  
Topographic Gradient ◽  
Position Index

Regional land use change and ecological security are important fields and have been popular issues in global change research in recent years. Regional habitat quality is also an important embodiment of the service function and health of ecosystems. Taking Shiyan City of Hubei Province as an example, the spatiotemporal differences in habitat quality in Shiyan City were evaluated using the habitat quality module of the InVEST model and GIS spatial analysis method based on DEM and land use data from 2000, 2005, 2010, 2015, and 2020. According to the habitat quality index values, the habitats were divided into four levels indicating habitat quality: I (very bad), II (bad), III (good), and IV (excellent), and the topographic gradient effect of habitat quality was studied using the topographic position index. The results show the following. (1) The habitat quality of Shiyan City showed relatively high and obvious spatial heterogeneity overall and, more specifically, was high in the northwest and southwest, moderate in the center, and low in the northeast. The higher quality habitats (levels III, IV) were mainly distributed in mountain and hill areas and water areas, while those with lower quality habitats (levels I, II) were mainly distributed in agricultural urban areas. (2) From 2000 to 2020, the overall average habitat quality of Shiyan City first increased, then decreased, and then increased again. Additionally, the habitat area increased with an improvement in the level. There was a trend in habitat transformation moving from low to high quality level, showing a spatial pattern of “rising in the southwest and falling in the northeast”. (3) The habitat quality in the water area and woodland area was the highest, followed by grassland, and that of cultivated land was the lowest. From 2000 to 2020, the habitat quality of cultivated land, woodland, and grassland decreased slightly, while the habitat quality of water increased significantly. (4) The higher the level of the topographic position index, the smaller the change range of land use types with time. The terrain gradient effect of habitat quality was significant. With the increase in terrain level, the average habitat quality correspondingly improved, but the increasing range became smaller and smaller. These results are helpful in revealing the spatiotemporal evolution of habitat quality caused by land use changes in Shiyan City and can provide a scientific basis for the optimization of regional ecosystem patterns and land use planning and management, and they are of great significance for planning the rational and sustainable use of land resources and the construction of an ecological civilization.

Identifying Small Depressional Wetlands and Using a Topographic Position Index to Infer Hydroperiod Regimes for Pond-Breeding Amphibians

Wetlands ◽  
2017 ◽  
Vol 37 (2) ◽  
pp. 325-338 ◽  
Author(s):  
Jeffrey W. Riley ◽  
Daniel L. Calhoun ◽  
William J. Barichivich ◽  
Susan C. Walls
Keyword(s):  
Depressional Wetlands ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Position Index

Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece)

2017 ◽  
Vol 17 (1) ◽  
pp. 90-97 ◽  
Author(s):  
Athanasios Skentos ◽  
Anagnostopoulou Ourania
Keyword(s):  
Morphometric Analysis ◽  
Aegean Sea ◽  
Gis Application ◽  
Topographic Position ◽  
Spatially Correlated ◽  
Topographic Position Index ◽  
Geological Setting ◽  
Terrain Attributes ◽  
Position Index ◽  
Landform Units

Abstract The main objective of this study is to classify the landforms of Ikaria Island by conducting morphometric analysis. The whole classification process is based on the calculation of the Topographic Position Index (TPI). The delivered TPI landform classes are spatially correlated with the geology, slope, valley depth and the topographic ruggedness of the island. The results of this study indicate the presence of two distinctive landform units, affected mainly by the local geological setting.

scholarly journals Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

2014 ◽  
Vol 8 (5) ◽  
pp. 1989-2006 ◽  
Author(s):  
J. Revuelto ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
S. M. Vicente-Serrano
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Climatic Conditions ◽  
Small Catchment ◽  
Topographic Position ◽  
Topographic Position Index ◽  
Spanish Pyrenees ◽  
Position Index ◽  
Snow Distribution ◽  
Variance Explained

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions (MLRs) and binary regression trees (BRTs) were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that best explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and interannual timescales. The topographic position index (index that compares the relative elevation of each cell in a digital elevation model to the mean elevation of a specified neighborhood around that cell with a specific shape and searching distance), which has rarely been used in these types of studies, most accurately explained the distribution of snow. The good capability of the topographic position index (TPI) to predict snow distribution has been observed in both, MLRs and BRTs for all analyzed days. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation and northing. The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snowpack was thinner and more patchily. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, similarities in snow distributions patterns were observed which are directly related to terrain topographic characteristics.

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