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This Special Issue (Engaging Students in Sustainable Science Education) compiled effective approaches to student engagement in science-related classes. Some articles were written by researchers in science education; however, the majority were prepared by college and university instructors based on their own instructional approaches, and were designed to help other practitioners improve student engagement in scientific contexts. Both types of contributors added value to this conversation. This Special Issue serves to unite science and education, identifying approaches that create stimulating scientific learning environments.

Humanities --- Education --- progressive pedagogy --- three teaching stages --- individual learning --- cooperative learning --- mixed-method study --- engagement --- engineering students --- first day of class --- ICT --- management --- motivation --- reciprocal interview activity --- sustainable science education --- science implementation --- youth empowerment --- high schoolstudents --- STEM --- sports science --- school science climate --- disciplinary climate --- science dispositions --- epistemology --- enjoyment --- interest --- self-efficacy --- science literacy --- Lab-at-Home --- new normal experimentation --- green analytical chemistry --- hands-on remotelearning --- higher education --- sustainability development --- multinomial logistic regression --- academic performance --- econometric models

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In response to the increasing urbanization, advances in the science of urban hydrology have improved urban water system management, creating more livable cities in which public safety and health, as well as the environment, are protected. The ultimate goal of urban water management is to mimic the hydrological cycle prior to urbanization. On top of urbanization, climate change, which has been demonstrated to alter the hydrological cycle in all respects, has introduced additional challenges to managing urban water systems. To mitigate and adapt to urbanization under a changing climate, understanding key hydrologic components should expand to include complex issues brought forth by climate change. Thus, effective and efficient measures can be formulated. This Special Issue of Water presents a variety of research papers that span a range of spatial and temporal scales of relevance in different societies’ efforts in adapting to the eminent changes in climate and the continuous changes in the landscape. From mitigating water quality in permeable pavements and bioretention swales to understanding changes in groundwater recharge in large regions, this Special Issue examines the state-of-the-art in sustainable urban design for adaptation and resiliency.

permeable asphalt --- heavy metal --- leaching behavior --- MSWI-BAA --- stormwater --- low impact development --- sustainable urban drainage systems --- stormwater modelling --- urban development --- GIS --- SAW --- decision-making --- strategic planning --- spatial analysis --- stormwater quality --- fecal coliforms --- Vancouver Island --- nearshore areas --- bacteria loading --- multinomial logistic regression --- periodicity analysis --- land use impacts --- climate impacts --- green roof --- energy performance --- heat island effect --- bio-retention --- green infrastructure --- runoff control performance --- storm inlet hydraulics --- flow distribution hydraulics --- climate change --- urbanization --- urban runoff --- Toronto --- Montreal --- Vancouver --- flooding --- geospatial modeling --- groundwater level --- trends --- non-stationarity --- climate variability --- land use/land cover change --- developing cities --- n/a

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The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

Research & information: general --- Environmental economics --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression

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The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression --- n/a