Landsat data will be limited during the winter months due to cloud cover, and for years prior to the Landsat 7 launch (1999) when only one satellite was operational at any given time. The Landsat image time-series was useful for identifying idle, single-, and multi-cropped fields. We found the combination of Landsat 5 and 7 image data would clearly benefit pesticide exposure assessment in this region by 1) providing information on crop field conditions at or near the time when pesticides are applied, and 2) providing information for validating the CDWR map. Many samples designated as single-cropped in the CDWR map had phenological patterns that represented multi-cropped or non-cropped fields, indicating they may have been misclassified. However, images were limited during the winter months due to cloud cover. We found the frequent overpass of Landsat enabled detection of crop field conditions (e.g., bare soil, vegetated) over most of the year. We intersected the Landsat time series with the California Department of Water Resources (CDWR) land use map and selected field samples to define the phenological characteristics of 17 major crop types or crop groups. We collected a dense time series of 24 Landsat 5 and 7 images spanning the year 2000 for an agricultural region in Fresno County. The purpose of this study was to examine the potential for using Landsat satellite data to support pesticide exposure assessment in California. Geological Survey policy offering Landsat satellite data at no cost provides researchers new opportunities to explore relationships between environment and health. Overall, our approach highlights the potentiality of combining multi-modal imageries and multiple classifiers for mapping a heterogenous environment such as the Sahel with high spatial resolution.The recent U.S. For example, we mapped rice and bare rocks that have important regional implication, which are not included in the existing products. Our classification scheme also better characterized the regional environment in the Sahel. The ensemble map had an overall accuracy of 72 ± 3.9% and it was found superior in terms of accuracy particularly with respect to built-up areas compared to the existing global land cover products in the study area. ![]() ![]() The results of this study showed that the performance of individual classifiers depends on feature selection method and accuracies can be improved by combining different classifiers. To leverage the strength of each classifier, we developed a classifier ensemble (CE) map based on the mapping accuracy of each land use class and each classifier. To understand the utility of different features from Sentinel-1 and Sentinel-2 imagery for classification, we performed feature selection and compared mapping accuracies with and without feature selection. Maximum Likelihood (ML), Support Vector Machine (SVM) and Random Forest (RF)) for land use classification. We assessed the performance of three commonly used classifiers (i.e. Our overall goal is to create an accurate, high resolution land use map covering Niamey, the capital of Niger and its surroundings which represents the unique landscape features in the Sahel using Sentinel-1 and Sentinel-2 archives. However, spatially and temporally heterogeneous landscapes in Sahel make classification of landscape features difficult. Due to its uniform quality across large areas in regular time steps, remote sensing imageries are essential input for producing land use maps. In the Sahel area, such information is valuable for risk management and mitigation in challenging sectors like food security, flood control, and urban planning. Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements, serving as an important planning tool for decision makers.
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