Integrating Different Types of Knowledge for Digital Soil Mapping

2009 ◽  
Vol 73 (5) ◽  
pp. 1682-1692 ◽  
Author(s):  
X. Shi ◽  
R. Long ◽  
R. Dekett ◽  
J. Philippe
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Types Of Knowledge ◽  
Different Types

Application of legacy soil data in digital soil mapping for the elaboration of novel, countrywide maps of soil conditions

2014 ◽  
Vol 63 (1) ◽  
pp. 79-88 ◽  
Author(s):  
László Pásztor ◽  
E. Dobos ◽  
G. Szatmári ◽  
A. Laborczi ◽  
K. Takács ◽  
...  
Keyword(s):  
Object Oriented ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Conditions ◽  
Related Information ◽  
Soil Information

The main objective of the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project is to significantly extend the potential, how demands on spatial soil related information could be satisfied in Hungary. Although a great amount of soil information is available due to former mappings and surveys, there are more and more frequently emerging discrepancies between the available and the expected data. The gaps are planned to be filled with optimized digital soil mapping (DSM) products heavily based on legacy soil data, which still represent a valuable treasure of soil information at the present time. The paper presents three approaches for the application of Hungarian legacy soil data in object oriented digital soil mapping.

scholarly journals Knowledge Mobilization in the Beaver Hills Biosphere, Alberta, Canada

Land ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 424
Author(s):  
Clara-Jane Blye ◽  
Elizabeth A. Halpenny ◽  
Glen T. Hvenegaard ◽  
Dee Patriquin
Keyword(s):  
Biosphere Reserve ◽  
Working Relationships ◽  
Knowledge Mobilization ◽  
Focus Group Interviews ◽  
Types Of Knowledge ◽  
Loss Of Autonomy ◽  
Different Types ◽  
Time Periods ◽  
Group Interviews ◽  
Forms Of Knowledge

This study explores how knowledge was and is mobilized to advance the objectives of the Beaver Hills Biosphere Reserve, located in Alberta, Canada. Established in 2016, a 12-year collaborative effort worked to establish the biosphere reserve and achieve formal UNESCO designation. Subsequent efforts to grow the newly established biosphere reserve have accelerated in recent years. Our study documented how different types of knowledge were accessed, created, curated, and shared between partners during these two time periods. Focus group interviews were conducted with 14 participants, who are affiliated with Beaver Hills Biosphere Reserve partner organizations, and revealed the following findings: (1) not all knowledge is equally valued or understood; (2) partnerships are highly valued, and were essential to successful knowledge mobilization, but were stronger among individuals rather than organizations; (3) fear of the loss of autonomy and potential complications due to the establishment of a biosphere reserve slowed the exchange of information and engagement by some regional actors; and (4) knowledge mobilization is and was impeded by staff and agency capacity, finances, and time scarcity. This was further complicated by entrenched norms of practice, existing successful working relationships impeding the development of new partnerships, and embracing alternative forms of knowledge.

scholarly journals Modeling intentional agency: a neo-Gricean framework

Synthese ◽  
2021 ◽  
Author(s):  
Matti Sarkia
Keyword(s):  
Philosophy Of Mind ◽  
General Framework ◽  
Building Blocks ◽  
Theoretical Modeling ◽  
Model Construction ◽  
Coarse Grained ◽  
Scientific Model ◽  
Types Of Knowledge ◽  
Different Types ◽  
Intentional Agency

AbstractThis paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in terms of some domain-general framework of lawlike regularities, which involves no detailed reference to particular building blocks or exemplars of intentional agency (although it may involve coarse-grained or heuristic reference to some of them). Given the contrasting procedural approaches that they employ and the different types of knowledge that they embody, the three strategies are argued to provide mutually complementary perspectives on intentional agency.

scholarly journals Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

2019 ◽  
Vol 11 (14) ◽  
pp. 1683 ◽  
Author(s):  
Yangchengsi Zhang ◽  
Long Guo ◽  
Yiyun Chen ◽  
Tiezhu Shi ◽  
Mei Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Time Series Data ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Series Data ◽  
Landsat 8 ◽  
Auxiliary Variables ◽  
Jianghan Plain ◽  
Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

scholarly journals Application of Biomimetics to Architectural and Urban Design: A Review across Scales

2020 ◽  
Vol 12 (23) ◽  
pp. 9813 ◽  
Author(s):  
Yuta Uchiyama ◽  
Eduardo Blanco ◽  
Ryo Kohsaka
Keyword(s):  
Urban Design ◽  
Architectural Design ◽  
Evolutionary Process ◽  
Future Research ◽  
Conceptual Level ◽  
Sustainable Society ◽  
Types Of Knowledge ◽  
The Social ◽  
Different Types ◽  
Shed Light

Application of biomimetics has expanded progressively to other fields in recent years, including urban and architectural design, scaling up from materials to a larger scale. Besides its contribution to design and functionality through a long evolutionary process, the philosophy of biomimetics contributes to a sustainable society at the conceptual level. The aim of this review is to shed light on trends in the application of biomimetics to architectural and urban design, in order to identify potential issues and successes resulting from implementation. In the application of biomimetics to architectural design, parts of individual “organisms”, including their form and surface structure, are frequently mimicked, whereas in urban design, on a larger scale, biomimetics is applied to mimic whole ecosystems. The overall trends of the reviewed research indicate future research necessity in the field of on biomimetic application in architectural and urban design, including Biophilia and Material. As for the scale of the applications, the urban-scale research is limited and it is a promising research which can facilitate the social implementation of biomimetics. As for facilitating methods of applications, it is instrumental to utilize different types of knowledge, such as traditional knowledge, and providing scientific clarification of functions and systems based on reviews. Thus, interdisciplinary research is required additionally to reach such goals.

Digital soil mapping of compositional particle-size fractions using proximal and remotely sensed ancillary data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB201-WB211 ◽  
Author(s):  
S. Buchanan ◽  
J. Triantafilis ◽  
I. O. A. Odeh ◽  
R. Subansinghe
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Soil Mapping ◽  
Additive Models ◽  
Digital Soil Mapping ◽  
Unbiased Estimation ◽  
Ancillary Data ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Log Ratio

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

Harmonizing legacy soil data for digital soil mapping in Indonesia

Geoderma ◽  
2013 ◽  
Vol 192 ◽  
pp. 77-85 ◽  
Author(s):  
Yiyi Sulaeman ◽  
Budiman Minasny ◽  
Alex B. McBratney ◽  
Muhrizal Sarwani ◽  
Atang Sutandi
Keyword(s):  
Soil Mapping ◽  
Digital Soil Mapping
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