Remote Sensing for Monitoring the Mountaintop Mining Landscape: Applications for Land Cover Mapping at the Individual Mine Complex Scale

The aim of this dissertation was to investigate the potential for mapping land cover associated with mountaintop mining in Southern West Virginia using high spatial resolution aerial- and satellite-based multispectral imagery, as well as light detection and ranging (LiDAR) elevation data and terrain derivatives. The following research themes were explored: comparing aerial- and satellite-based imagery, combining data sets of multiple dates and types, incorporating measures of texture, using nonparametric, machine learning classification algorithms, and employing a geographical object-based image analysis (GEOBIA) framework. This research is presented as four interrelated manuscripts.;In a comparison of aerial National Agriculture Imagery Program (NAIP) orthophotography and satellite-based RapidEye data, the aerial imagery was found to provide statistically less accurate classifications of land cover. These lower accuracies are most likely due to inconsistent viewing geometry and radiometric normalization associated with the aerial imagery. Nevertheless, NAIP orthophotography has many characteristics that make it useful for surface mine mapping and monitoring, including its availability for multiple years, a general lack of cloud cover, contiguous coverage of large areas, ease of availability, and low cost. The lower accuracies of the NAIP classifications were somewhat remediated by decreasing the spatial resolution and reducing the number of classes mapped.;Combining LiDAR with multispectral imagery statistically improved the classification of mining and mine reclamation land cover in comparison to only using multispectral data for both pixel-based and GEOBIA classification. This suggests that the reduced spectral resolution of high spatial resolution data can be combated by incorporating data from another sensor.;Generally, the support vector machines (SVM) algorithm provided higher classification accuracies in comparison to random forests (RF) and boosted classification and regression trees (CART) for both pixel-based and GEOBIA classification. It also outperformed k-nearest neighbor, the algorithm commonly used for GEOBIA classification. However, optimizing user-defined parameters for the SVM algorithm tends to be more complex in comparison to the other algorithms. In particular, RF has fewer parameters, and the program seems robust regarding the parameter settings. RF also offers measures to assess model performance, such as estimates of variable importance and overall accuracy.;Textural measures were found to be of marginal value for pixel-based classification. For GEOBIA, neither measures of texture nor object-specific geometry improved the classification accuracy. Notably, the incorporation of additional information from LiDAR provided a greater improvement in classification accuracy then deriving complex textural and geometric measures.;Pre- and post-mining terrain data classified using GEOBIA and machine learning algorithms resulted in significantly more accurate differentiation of mine-reclaimed and non-mining grasslands than was possible with spectral data. The combination of pre- and post-mining terrain data or just pre-mining data generally outperformed post-mining data. Elevation change data were shown to be of particular value, as were terrain shape parameters. GEOBIA was a valuable tool for combining data collected using different sensors and gridded at variable cell sizes, and machine learning algorithms were particularly useful for incorporating the ancillary data derived from the digital elevation models (DEMs), since these most likely would not have met the basic assumptions of multivariate normality required for parametric classifiers.;Collectively, this research suggests that high spatial resolution remotely sensed data are valuable for mapping and monitoring surface mining and mine reclamation, especially when elevation and spectral data are combined. Machine learning algorithms and GEOBIA are useful for integrating such diverse data

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California's almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R 2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April-June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability

GIS-based urban land use characterization and population modeling with subpixel information measured from remote sensing data

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Weng’s urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements

Ensemble Methods in Environmental Data Mining

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil

Classifying multisensor remote sensing data : Concepts, Algorithms and Applications

Today, a large quantity of the Earth’s land surface has been affected by human induced land cover changes. Detailed knowledge of the land cover is elementary for several decision support and monitoring systems. Earth-observation (EO) systems have the potential to frequently provide information on land cover. Thus many land cover classifications are performed based on remotely sensed EO data. In this context, it has been shown that the performance of remote sensing applications is further improved by multisensor data sets, such as combinations of synthetic aperture radar (SAR) and multispectral imagery. The two systems operate in different wavelength domains and therefore provide different yet complementary information on land cover. Considering the increase in revisit times and better spatial resolutions of recent and upcoming systems like TerraSAR-X (11 days; up to1 m), Radarsat-2 (24 days; up to 3 m), or RapidEye constellation (up to 1 day; 5 m), multisensor approaches become even more promising. However, these data sets with high spatial and temporal resolution might become very large and complex. Commonly used statistical pattern recognition methods are usually not appropriate for the classification of multisensor data sets. Hence, one of the greatest challenges in remote sensing might be the development of adequate concepts for classifying multisensor imagery. The presented study aims at an adequate classification of multisensor data sets, including SAR data and multispectral images. Different conventional classifiers and recent developments are used, such as support vector machines (SVM) and random forests (RF), which are well known in the field of machine learning and pattern recognition. Furthermore, the impact of image segmentation on the classification accuracy is investigated and the value of a multilevel concept is discussed. To increase the performance of the algorithms in terms of classification accuracy, the concept of SVM is modified and combined with RF for optimized decision making. The results clearly demonstrate that the use of multisensor imagery is worthwhile. Irrespective of the classification method used, classification accuracies increase by combining SAR and multispectral imagery. Nevertheless, SVM and RF are more adequate for classifying multisensor data sets and significantly outperform conventional classifier algorithms in terms of accuracy. The finally introduced multisensor-multilevel classification strategy, which is based on the sequential use of SVM and RF, outperforms all other approaches. The proposed concept achieves an accuracy of 84.9%. This is significantly higher than all single-source results and also better than those achieved on any other combination of data. Both aspects, i.e. the fusion of SAR and multispectral data as well as the integration of multiple segmentation scales, improve the results. Contrary to the high accuracy value by the proposed concept, the pixel-based classification on single-source data sets achieves a maximal accuracy of 65% (SAR) and 69.8% (multispectral) respectively. The findings and good performance of the presented strategy are underlined by the successful application of the approach to data sets from a second year. Based on the results from this work it can be concluded that the suggested strategy is particularly interesting with regard to recent and future satellite missions

Optical remote sensing of aboveground forest biomass and carbon stocks in resource-constrained African environments.

Ph. D. University of KwaZulu-Natal, Pietermaritzburg 2015.No abstract available

Remote sensing of forest health : the detection and mapping of Pinus patula trees infested by Sirex noctilio.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2008.Sirex noctilio is causing considerable mortality in commercial pine forests in KwaZulu- Natal, South Africa. The ability to remotely detect S. noctilio infestations remains crucial for monitoring the spread of the wasp and for the effective deployment of suppression activities. This thesis advocates the development of techniques based on remote sensing technology to accurately detect and map S. noctilio infestations. To date, no research has examined the potential of remote sensing technologies for the detection and mapping of Pinus patula trees infested by S. noctilio. In the first part of this thesis, the focus was on whether high spatial resolution imagery could characterize S. noctilio induced stress in P. patula forests. Results showed that, the normalized difference vegetation index derived from high spatial resolution imagery has the potential to accurately detect and map the later stages of S. noctilio infestations. Additionally, operational guidelines for the optimal spatial resolutions that are suitable for detecting and mapping varying levels of sustained S. noctilio mortality were defined. Results showed that a pixel size of 2.3 m is recommended to detect high (11-15%) infestation levels, and a pixel size of 1.75 m is recommended for detecting low to medium infestation levels (1-10%). In the second part of this thesis, the focus was on the ability of high spectral resolution (hyperspectral) data to discriminate between healthy trees and the early stages of S. noctilio infestation. Results showed that specific wavelengths located in the visible and near infrared region have the greatest potential for discriminating between healthy trees and the early stages of S. noctilio infestation. The researcher also evaluated the robustness and accuracy of various machine learning algorithms in identifying spectral parameters that allowed for the successful detection of S. noctilio infestations. Results showed that the random forest algorithm simplified the process by identifying the minimum number of spectral parameters that provided the best overall accuracies. In the final part of this thesis spatial modelling techniques were used to proactively identify pine forests that are highly susceptible to S. noctilio infestations. For the first time the random forest algorithm was used in conjunction with geographic information systems for mapping pine forests that are susceptible to S. noctilio infestations. Overall, there is a high probability of S. noctilio infestation for the majority (63%) of pine forest plantations located in Mpumalanga, South Africa. Compared to previous studies, the random forest model identified highly susceptible pine forests at a more regional scale and provided an understanding of localized variations of environmental conditions in relation to the distribution of the wasps

Characterizing Spatiotemporal Patterns of White Mold in Soybean across South Dakota Using Remote Sensing

Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to previous reports, environmental conditions corresponding to high yield potential are most conducive for white mold development. These conditions include cool temperature (12-24 °C), continued wet and moist conditions (70-120 h) generally resulting from rain, but the disease development requires the presence of a susceptible soybean variety. To better understand white mold development in the field, there is a need to investigate its spatiotemoral characteristics and provide accurate estimates of the damages that white mold may cause. Current and accurate data about white mold are scarce, especially at county or larger scale. Studies that explored the characteristics of white mold were generally field oriented and local in scale, and when the spectral characteristics were investigated, the authors used spectroradiometers that are not accessible to farmers and to the general public and are mostly used for experimental modeling. This study employed free remote sensing Landsat 8 images to quantify white mold in South Dakota. Images acquired in May and July were used to map the land cover and extract the soybean mask, while an image acquired in August was used to map and quantify white mold using the random forest algorithm. The land cover map was produced with an overall accuracy of 95% while white mold was mapped with an overall accuracy of 99%. White mold area estimates were respectively 132 km2, 88 km2, and 190 km2, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties. This study also explored the spatial characteristics of white mold in soybean fields and its impact on yield. The yield distribution exhibited a significant positive spatial autocorrelation (Moran’s I = 0.38, p-value \u3c 0.001 for Moody field, Moran’s I = 0.45, p-value \u3c 0.001, for Marshall field) as an evidence of clustering. Significant clusters could be observed in white mold areas (low-low clusters) or in healthy soybeans (high-high clusters). The yield loss caused by the worst white mold was estimated at 36% and 56% respectively for the Moody and the Marshall fields, with the most accurate loss estimation occurring between late August and early September. Finally, this study modeled the temporal evolution of white mold using a logistic regression analysis in which the white mold was modeled as a function of the NDVI. The model was successful, but further improved by the inclusion of the Day of the Year (DOY). The respective areas under the curves (AUC) were 0.95 for NDVI and 0.99 for NDVI+DOY models. A comparison of the NDVI temporal change between different sites showed that white mold temporal development was affected by the site location, which could be influenced by many local parameters such as the soil properties, the local elevation, management practices, or weather parameters. This study showed the importance of freely available remotely sensed satellite images in the estimation of crop disease areas and in the characterization of the spatial and temporal patterns of crop disease; this could help in timely disease damage assessment

Forest Disturbance Detection and Aboveground Biomass Modeling Using Moderate-Resolution, Time-Series Satellite Imagery

Human-induced and natural disturbances are an important feature of forest ecosystems. Disturbances influence forest structure and composition and can impact crucial ecosystem services. However, deriving spatially explicit estimates of past forest disturbance across a large region can prove challenging. Researchers have recognized that remote sensing is an important tool for monitoring forest ecosystems and mapping land use and land cover change. One of the most important sources of remotely sensed imagery is the United States Geologic Survey’s Landsat program which has continuously acquired earth observations since 1972. This repository of imagery has the spatial, spectral, and temporal resolution necessary to produce maps of disturbance which are meaningful for the analysis of forested ecosystems. In this analysis, we utilize the imagery from the Landsat archive to produce maps of forest disturbance from 1985 to 2017 for the New England states and the Canadian Maritime provinces. The change detection maps were developed using stacked generalization, a modeling technique that fuses the outputs of an ensemble of individual change-detection algorithms through the use of a secondary classifier. To better understand the error associated with these classifications, we quantified the spectral characteristics associated with different harvesting practices. Using two case studies, the 1998 ice storm and the 2016 gypsy moth outbreak in southern New England, we performed experiments to examine how the stacked generalization framework can be utilized to increase the accuracy of disturbance maps following large-scale natural disturbances. The change detection maps developed in this analysis possessed a 98.7% overall accuracy and a 27.5% balance of the errors of omission and commission. Our results indicated that adjusting the probability threshold associated with the secondary classifier in the stacked generalization framework increase the spatial coherence of disturbance patches and better capture the low- to moderate-severity disturbances. Using the maps of disturbance for the New England states and Maritime Provinces, we derived metrics describing the spectral change magnitude, timing, and percent spectral recovery across the study region. Recent research has found that including metrics of disturbance and recovery processes, derived from the analysis of time-series satellite imagery, can improve the accuracy of AGB models. However, these studies have largely been conducted in regions with relatively homogenous forest composition and structure and disturbance regimes dominated by stand-replacing disturbances. This analysis expands upon the existing literature by exploring how disturbance and recovery metrics can improve the predictions of AGB models in a heterogeneous landscape with a complex land-use history. Gradient boosting models, a sophisticated machine learning technique, were used to produce regional AGB models using spectral, disturbance, and environmental (e.g., topographic, climatological, etc.) metrics. Additionally, we explore how adjusting the rate of mapped disturbance through modifications to the class-inclusion rate associated with the secondary classifier can impact estimates of AGB. We conclude that landscape heterogeneity, as well as the general lack of stand-replacing disturbances, negatively impacts the predictive utility of disturbance and recovery metrics for modeling AGB