Temporal Feature Selection with Symbolic Regression

Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

Improving land cover classification using genetic programming for feature construction

Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.publishersversionpublishe

La evaluación de los servicios de los ecosistemas como herramienta para planificar la restauración ecológica de cuencas hidrográficas

La provisión de servicios por los ecosistemas podría empeorar considerablemente y rápidamente durante la primera mitad del presente siglo si no se restauran eficientemente ecosistemas degradados. Frente a la aproximación clásica de la restauración basada en sistemas de referencia a imitar, existe el reto de obtener metodologías para territorio amplio y complejo y no solo para un sitio con un tipo de ecosistema. Existen muchas opciones para conservar o fortalecer servicios específicos de los ecosistemas de forma que se reduzcan las elecciones negativas que nos veamos obligados a hacer o que se creen sinergias positivas con otros servicios de los ecosistemas. En esta tesis se ha desarrollado una metodología basada en la evaluación de servicios de los ecosistemas, como variables de estado, y del riesgo de erosión, como factor de disturbio, para establecer una jerarquización espacial de actuaciones de restauración a escala de cuenca hidrográfica. Para ello se ha realizado la evaluación de servicios de los ecosistemas, modelización de la erosión y se han utilizado sistemas de información geográfica (SIG) para la elaboración de cartografía jerárquica y análisis espacial. El área de estudio utilizada es la cuenca del Río Martín (Teruel, NE España, 1938 km2) como unidad funcional que, por su susceptibilidad natural a la erosión y con su elevada heterogeneidad paisajística y diferentes usos del suelo (agrícola, minería, ganadera) se presta como un valioso territorio donde aplicar y testar la metodología propuesta. La cartografía elaborada para la estimación de las tasas de erosión ha sido extrapolada con el modelo RUSLE (Ecuación de pérdida de suelo revisada) utilizando un innovador índice de vegetación (GPVI). Este índice fue elaborado mediante una técnica de inteligencia artificial llamada programación genética, la cual fue calibrada con los datos de campo del factor C de RUSLE (muestreo de suelos, transectos de vegetación) del presente estudio. Los datos de campo utilizados para crear el mapa de erosión han sido complementados con imágenes satelitales Landsat 5-TM y mapas disponibles de las características del territorio (litología, uso del suelo, ortofotos aéreas). Las tasas de erosión observadas en la cuenca del Martín tienen una media de 13.8 t ha-1 año-1 siendo notablemente mayores en la parte sur (20 t ha-1 año-1) debido a su irregular orografía que en las zonas de llanura del norte (10 t ha-1 año-1). Los servicios de los ecosistemas se evaluaron mediante indicadores obtenidos a partir de bases de datos nacionales y regionales complementados con datos de campo. Los datos son expresados para cada servicio en las unidades de medida correspondientes y se basan en el análisis de los mapas de diferentes datos físico-químicos y biológicos. Los datos de los servicios relacionados con el agua han sido proporcionados para la Confederación Hidrográfica del Ebro (CHE), los datos de acumulación de carbono en pies mayores han sido proporcionados por el Departamento de Recursos forestales del Centro de Investigación de tecnología y investigación agraria de Aragón (CITA). Los datos de acumulación de carbono en el suelo son disponibles en el Portal de Suelos Europeo (European Soil Portal). Las rutas de eco-turismo han sido descargadas de la pagina de rutas wiki-loc y la pagina de senderos de Aragón. La retención de suelo fue modelizada combinando datos del factor C para estimar el porcentual de cobertura vegetal y las tasas de erosión del modelo RUSLE-SIG. Los servicios de los ecosistemas variaron también entre amplios y diferentes rangos. La acumulación de carbono varía entre 0 y 4648 t CO2 eq en zonas menos densas de vegetación y 40442 y 118073 t CO2 eq en las zonas forestales densas; la provisión de agua superficial en el norte varía entre 0 y 13 mm y 100 y 210 en el sur de la cuenca, principalmente en fondos de valles; el control de la escorrentía (recarga acuíferos) es más alto en zonas montañosas del sur de la cuenca con valores entre 8 y 81 mm año-1 con valores mínimos entre 8 y 34 mm año-1 en el norte y máximos de 81 mm año-1 en el sur; la retención del suelo se ha expresado en valores relativos que varían de 1 a 5 dependiendo de la relación entre porcentaje de cobertura vegetal y perdida del suelo (estimada por la RUSLE-SIG en 5 clases de muy baja a muy alta), con valor máximo de retención de suelo a coberturas mayores de 70% y erosión menor de 12 t ha-1 año-1, y mínimo a zonas de cobertura inferior a 30% y erosión mayor de 17 t ha-1 año-1. El servicio de eco-turismo se ha evaluado como presencia-ausencia, asignando valor 1 a las áreas de la cuenca que se observan desde los senderos usando la herramienta de visualización de cuenca en SIG (viewshed) y 0 en el resto de la cuenca que no se observa desde los senderos según el modelo digital del terreno utilizado. Tratándose de datos con unidades diferentes, entre ellos se utilizó una agrupación en el rango relativo de 1 a 5 de cada servicio por cortes naturales (Natural Breaks) en SIG, que genera clases cuyos límites se ubican donde hay diferencias relativamente grandes en los valores de los datos por cada servicio. Ecoturismo tenía un valor 0 o 1 según la ausencia o posibilidad de visualización del paisaje en el recorrer los caminos. El valor más elevado de un determinado servicio se considera un área de elevado valor definido como hotspot, que es un área de una importancia máxima para ese servicio. Análisis de solapamiento han sido realizados para entender las relaciones entre servicios. Finalmente a través de la creación de mapas jerárquicos los datos de erosión y servicios ecosistémicos han sido relacionados analizando la congruencia espacial y los patrones espaciales a diferentes escalas anidadas entre ellas, dándonos la posibilidad de analizar el comportamiento de los dos factores, y contrastar el factor de disturbio y las variables de estado a diferentes escalas espaciales. Se ha identificado la zona sur de la cuenca del área de estudio, como el área donde se presentan más servicios y se observan las tasas de erosión más altas debido a factores topográficos, entre otros. En ésta zona, y particularmente en las subcuencas con zonas mineras no restauradas (donde la erosión muestra tasas máximas y los servicios son muchas veces nulos y en subcuencas con altas tasas de erosión y alto número de servicios las acciones de restauración han de ser prioritarias si no se quieren perder servicios que benefician aguas abajo en la cuenca. Claramente según los objetivos del gestor las prioridades pueden modificarse y nuestra metodología fácilmente adaptarse. En la zona norte, llana y mayoritariamente usada para agricultura de cereal de secano, la erosión es relativamente baja y la provisión de servicios de regulación también. Es la zona de menor interés para realizar acciones de restauración dado que la mejora de los servicios no está asegurada y se podría entrar en conflicto con intereses de usos (trade off) de otros servicios (por ej., producción de alimentos) incluidos sociales. También se ha demostrado la utilidad de realizar evaluaciones a diferentes resoluciones espaciales para la mejor identificación de las zonas óptimas de restauración. Se propone un modelo conceptual general de toma de decisiones de restauración a escala de cuenca en función de la provisión de servicios de los ecosistemas y de los factores de alteración ecológica. Finalmente la metodología aquí propuesta, desarrollada con SIG con la creación de mapas jerárquicos, ha resultado fácilmente adaptable a la escala de paisaje. Esto hace que nuestro modelo dependiendo de la disponibilidad de datos, sea una herramienta útil y fácilmente aplicable para la restauración a escala de cuenca hidrográfica o de paisaje, donde los servicios ecosistémicos estén alterados por diferentes factores de disturbio

Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications

This 2005 version has been superseded by the 2017 edition, available in full here: http://hdl.handle.net/1813/48159Throughout history much of the world has witnessed ever-greater demands for reliable, high-quality and inexpensive water supplies for domestic consumption, agriculture and industry. In recent decades there have also been increasing demands for hydrological regimes that support healthy and diverse ecosystems, provide for water-based recreational activities, reduce if not prevent floods and droughts, and in some cases, provide for the production of hydropower and ensure water levels adequate for ship navigation. Water managers are challenged to meet these multiple and often conflicting demands. At the same time, public stakeholder interest groups have shown an increasing desire to take part in the water resources development and management decision making process. Added to all these management challenges are the uncertainties of natural water supplies and demands due to changes in our climate, changes in people's standards of living, changes in watershed land uses and changes in technology. How can managers develop, or redevelop and restore, and then manage water resources systems - systems ranging from small watersheds to those encompassing large river basins and coastal zones - in a way that meets society's changing objectives and goals? In other words, how can water resources systems become more integrated and sustainable

Desertification

IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL) Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem

Advances in Sustainable River Management

The main objective of this Special Issue is to contribute in understanding and provide science-based knowledge, new ideas/approaches and solutions in sustainable river management, to improve water management policies and practices following different environmental requirements aspects

Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management