A Review Of Natural Resource Information And Interpretation Of Remote Sensing Imagery As Aids In Improving A Soil Survey In Ingham County Michigan

Author : Jhony Armando Benavides Bolanos
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

DOWNLOAD NOW!

Download or read book An Exploration of Remote Sensing Techniques for Identifying Hydrologically Sensitive and Critical Source Areas in Agricultural Landscapes written by Jhony Armando Benavides Bolanos and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The identification of risks associated with nutrient transport via runoff or erosion processes in agricultural landscapes can be achieved using remotely sensed data, in combination with ground truth and secondary sources of soil data. Spatial data obtained from Earth observation platforms (EO) and small remotely piloted aircraft systems (sRPAS) can provide estimations of soil moisture content distribution at different scales. The accuracy of these estimations can be improved using ground truth data based on soil and plant tissue sampling campaigns. Furthermore, different types of classification and image processing algorithms can provide new insights about vulnerable areas. By utilizing these decision support tools (DSTs), it is possible to gather data on the distribution and variability of landscape features that affect hydrology and water quality. This enables farmers, researchers, and policymakers to make more informed decisions on how best to manage these resources. Two DSTs were analyzed in this dissertation, namely the Hydrologically Sensitive Area and the soil P Critical Source Area Indices. A hydrologically sensitive area (HSA) refers to a region within a watershed that exhibits heightened vulnerability or sensitivity to changes in hydrological processes, whereas a critical source area (CSA) is a specific land area or portion within a watershed that contributes a disproportionally high concentrations of soil nutrients to waterbodies. The two most problematic soil nutrient contaminants of water bodies are Nitrogen (N) and Phosphorous (i.e., P adsorbed to soil particles and/or legacy P). This research will focus on P CSAs because of the combination of phosphorous' retention and transport mechanisms, its direct influence on the acceleration of eutrophication processes, and its long-term accumulation and legacy effects in soils. This research is structured into four chapters. Chapter One introduces the central ideas and goals of this dissertation. It further delves into the existing void in the literature and highlights the potential advancements it can bring to the scientific fields of soil digital mapping, soil nutrient management, and remote sensing. In Chapter Two, publicly available EO platforms are evaluated to develop HSA and P CSA indices. The chapter explores the use of Google Earth Engine (GEE) data such as the US Department of Agriculture National Agriculture Imagery Program (NAIP) and Radio Detection and Ranging data from the Sentinel 1-A mission (RADAR) data to derive HSAs and P CSAs. Additionally, secondary data from the Gridded Soil Survey Geographic (gSSURGO) database are used for further calculations. The results suggest that HSA Index maps can be derived based on freely available EO elevation and soil data (gSSURGO) with a minimum overestimated area of 16.78% compared to a reference HSA Index product. The reference HSA Index is derived from airborne Light Detection and Ranging (LiDAR) elevation and gSSURGO data. However, the prediction of P CSA Index areas based solely on NAIP multispectral data via the classification and regression tree method resulted in unacceptable overestimations of P CSA Index areas (approximately five-fold), which would have substantially changed the management recommendations. It was concluded that soil sampling to derive soil total P data as an input to the CSA index is required to develop a reliable P CSA Index. In Chapter Three, the focus was on how agricultural fields with nutrient-rich sediments can lead to waterway contamination through surface runoff. The HSA and P CSA indices were developed to predict surface runoff volumes and surface P loads in areas with concentrated flow pathways (CFPs) and surface runoff. High-resolution digital elevation models (DEMs) and soil P data are required to develop these indices. However, not all agricultural fields have been surveyed using airborne LiDAR sensors, which can provide high-resolution DEMs. In this regard, the chapter proposes the use of HSA and P CSA indices developed using DEMs derived from visible spectrum images acquired through sRPAS and processed using the Structure from Motion (SfM) with multi-view stereo photogrammetry technique. The validation of these indices was done using soil total P data and LiDAR-derived DEMs. The results suggest that SfM-derived HSA and P CSA indices can be comparable to those derived from LiDAR elevation data, although training in pre- and post-processing is required. The study concluded that sRPAS paired with SfM can be a portable, flexible, and rapidly deployable device in the field, providing comparable results to LiDAR data. Chapter Four discusses the use of sRPAS technology to monitor wastewater spray-irrigated agricultural fields (Penn State's Living Filter). The aim of the study was to evaluate the monitoring capabilities of sRPAS for identifying surface soil moisture flow pathways, and to correlate these findings with ground truth data and soil chemical properties. The sRPAS technology was equipped with thermal infrared (TIR) and multispectral sensors to collect data, which was used to develop terrain analysis maps and DEMs. The results suggest that SfM-derived elevation data can delineate CFPs with accuracies comparable to LiDAR data. Elevation data was found to be a good predictor of soil volumetric water content (VWC), while Normalized Difference Vegetation Index (NDVI) data was found to be a poor predictor of soil VWC. Results of this chapter suggest that sRPAS technology can greatly accelerate data collection and help irrigation managers and farmers to delineate vulnerable areas to runoff in agricultural fields with accuracies comparable to the ones obtained using LiDAR data. An Extended Summary and Main Conclusions sections are followed. Additionally, Appendix A provides an overview of the International Agricultural and Development (INTAD) dual title component of this dissertation. This component diverges from the natural sciences and engineering focus of the preceding chapters. Instead, it delves into the social sciences. In the INTAD component, the significance of scientific-based soil science and ethnopedology is explored within the context of climate change. Ethnopedology involves examining the perceptions, practices, and locally derived knowledge held by farmers regarding soil properties, types, and descriptions. By integrating these social science perspectives, a more holistic understanding of the subject matter is achieved. While distinct from the preceding chapters in terms of approach, the INTAD component contributes valuable insights into the broader implications of soil science and its intersection with local knowledge and climate change. It describes socio-cultural aspects that influence agricultural practices. The vulnerability of small-scale farmers to climate change is discussed, along with the importance of integrating ethnopedology into soil management research. Various challenges related to agriculture and gender are also outlined, including limited access to natural resources and land ownership, the need for comprehensive soil and land management training, restricted access to information, land tenure and services, limited participation in farming decisions, and gender-based labor disparities. The INTAD component emphasizes the need to explore the similarities and differences between ethnopedology and scientific-based soil knowledge, considering the specific needs of women and men farmers, to enhance understanding of how these factors might contribute to sustainable soil and land management in the face of climate change uncertainty and heightened vulnerability.