Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

Digital terrain analysis (DTA) is one of the most important contents in the research of geographical information science (GIS). However, on the basis of the digital elevation model (DEM), many problems exist in the current research of DTA in geomorphological studies. For instance, the current DTA research appears to be focused more on morphology, phenomenon, and modern surface rather than mechanism, process, and underlying terrain. The current DTA research needs to be urgently transformed from the study of landform morphology to one focusing on landform process and mechanism. On this basis, this study summarizes the current research status of geomorphology-oriented DTA and systematically reviews and analyzes the research about the knowledge of geomorphological ontology, terrain modeling, terrain derivative calculation, and terrain analytical methods. With the help of DEM data, DTA research has the advantage of carrying out geomorphological studies from the perspective of surface morphology. However, the study of DTA has inherent defects in terms of data expression and analytic patterns. Thus, breakthroughs in basic theories and key technologies are necessary. Moreover, scholars need to realize that DTA research must be transformed from phenomenon to mechanism, from morphology to process, and from terrain to landform. At present, the research development of earth science has reached the critical stage in which the DTA research should focus more on geomorphological ontology. Consequently, this study proposes several prospects of geomorphology-oriented DTA from the aspects of value-added DEM data model, terrain derivatives and their spatial relations, and macro-terrain analysis. The study of DTA based on DEM is at a critical period along with the issue on whether the current GIS technology can truly support the development of geography. The research idea of geomorphology-oriented DTA is expected to be an important exploration and practice in the field of GIS.

Geomorphology-oriented digital terrain analysis: Progress and perspectives

Soil organic matter (SOM) is an important soil property that affects physical, chemical, and biological properties of soil. Accurate estimation of SOM variability could provide critical information for understanding nutrients cycling and sediment. In the current study, artificial neural networks (ANNs) were developed to predict SOM variability based on topographic variables (topographic wetness index, relative position index, slope length and elevation) in hilly areas. A total of 265 soil samples collected from a depth of 0–20 cm were used to calibrate and validate the models. The best performed ANN model was compared with multiple linear regression (MLR) equation. The performance accuracy was evaluated by Pearson’s correlation coefficient (r), mean error (ME), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2). In terms of MSE and r, the ANN model with topographic wetness index, relative position index, and slope length outperformed other ANNs. The best performed ANN model was also superior to the MLR equation. Values of ME, RMSE, and R2 were −0.0337 g/kg, 1.0919 g/kg, and 0.8714 for ANN model, and were 0.1574 g/kg, 1.3296 g/kg, and 0.8172 for MLR equation, respectively. The results of ANN and MLR suggested that topographic wetness index was the most important topographic indicator affecting SOM variability in the current study area.

Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas

Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30–40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30–40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.

Mapping soil salinity using a similarity-based prediction approach: A case study in Huanghe River Delta, China

Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions: Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yanjun Lu

Author Tools

Chat about Author

Papers

Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions: Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions