Geology topics

Darrell Napton

Publications and source records attributed to Darrell Napton.

4 recordsLinked to original sources

USGS2016

An assessment of the cultivated cropland class of NLCD 2006 using a multi-source and multi-criteria approach

We developed a method that analyzes the quality of the cultivated cropland class mapped in the USA National Land Cover Database (NLCD) 2006. The method integrates multiple geospatial datasets and a Multi Index Integrated Change Analysis (MIICA) change detection method that captures spectral changes to identify the spatial distribution and magnitude of potential commission and omission errors for the cultivated cropland class in NLCD 2006. The majority of the commission and omission errors in NLCD 2006 are in areas where cultivated cropland is not the most dominant land cover type. The errors are primarily attributed to the less accurate training dataset derived from the National Agricultural Statistics Service Cropland Data Layer dataset. In contrast, error rates are low in areas where cultivated cropland is the dominant land cover. Agreement between model-identified commission errors and independently interpreted reference data was high (79%). Agreement was low (40%) for omission error comparison. The majority of the commission errors in the NLCD 2006 cultivated crops were confused with low-intensity developed classes, while the majority of omission errors were from herbaceous and shrub classes. Some errors were caused by inaccurate land cover change from misclassification in NLCD 2001 and the subsequent land cover post-classification process.

Remote Sensing↗

USGS2014

Evaluation of a model framework to estimate soil and soil organic carbon redistribution by water and tillage using 137 Cs in two U.S. Midwest agricultural fields

Cultivated lands in the U.S. Midwest have been affected by soil erosion, causing soil organic carbon (SOC) redistribution in the landscape and other environmental and agricultural problems. The importance of SOC redistribution on soil productivity and crop yield, however, is still uncertain. In this study, we used a model framework, which includes the Unit Stream Power-based Erosion Deposition (USPED) and the Tillage Erosion Prediction (TEP) models, to understand the soil and SOC redistribution caused by water and tillage erosion in two agricultural fields in the U.S. Midwest. This model framework was evaluated for different digital elevation model (DEM) spatial resolutions (10-m, 24-m, 30-m, and 56-m) and topographic exponents ( m = 1.0–1.6 and n = 1.0–1.3) using soil redistribution rates from 137 Cs measurements. The results showed that the aggregated 24-m DEM, m = 1.4 and n = 1.0 for rill erosion, and m = 1.0 and n = 1.0 for sheet erosion, provided the best fit with the observation data at both sites. Moreover, estimated average SOC redistributions were 1.3 ± 9.8 g C m − 2 yr − 1 in field site 1 and 3.6 ± 14.3 g C m − 2 yr − 1 in field site 2. Spatial distribution patterns showed SOC loss (negative values) in the eroded areas and SOC gain (positive value) in the deposition areas. This study demonstrated the importance of the spatial resolution and the topographic exponents to estimate and map soil redistribution and the SOC dynamics throughout the landscape, helping to identify places where erosion and deposition from water and tillage are occurring at high rates. Additional research is needed to improve the application of the model framework for use in local and regional studies where rainfall erosivity and cover management factors vary. Therefore, using this model framework can help to improve the information about the spatial distribution of soil erosion across agricultural landscapes and to gain a better understanding of SOC dynamics within eroding and previously eroded fields.

Iowa↗

USGS2014

A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010

Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area estimates revealed that total mangrove area decreased by approximately half (51.8%) from 1918 to 2010. This study provides the most current and reliable data regarding the Philippines mangrove area and spatial distribution and delineates where and when mangrove change has occurred in recent decades. The results from this study are useful for developing conservation strategies, biodiversity loss mitigation efforts, and future monitoring and analysis.

Journal of Coastal Research↗

USGS2009

Exploring subtle land use and land cover changes: A framework for future landscape studies

Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.

Landscape Ecology↗