Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Afforestation/reforestation (or forestation) has been implemented worldwide as an effective measure towards sustainable ecosystem services and addresses global environmental problems such as climate change. The conversion of grasslands, croplands, shrublands, or bare lands to forests can dramatically alter forest water, energy, and carbon cycles and, thus, ecosystem services (e.g., carbon sequestration, soil erosion control, and water quality improvement). Large-scale afforestation/reforestation is typically driven by policies and, in turn, can also have substantial socioeconomic impacts. To enable success, forestation endeavors require novel approaches that involve a series of complex processes and interdisciplinary sciences. For example, exotic or fast-growing tree species are often used to improve soil conditions of degraded lands or maximize productivity, and it often takes a long time to understand and quantify the consequences of such practices at watershed or regional scales. Maintaining the sustainability of man-made forests is becoming increasingly challenging under a changing environment and disturbance regime changes such as wildland fires, urbanization, drought, air pollution, climate change, and socioeconomic change. Therefore, this Special Issue focuses on case studies of the drivers, dynamics, and impacts of afforestation/reforestation at regional, national, or global scales. These new studies provide an update on the scientific advances related to forestation. This information is urgently needed by land managers and policy makers to better manage forest resources in today’s rapidly changing environments.

simulation modeling --- shear strength --- stand structure --- vegetation restoration --- surface runoff --- soil and water conservation function --- soil enzymes --- riverbank --- evapotranspiration --- human activity --- afforestation --- Artemisia ordosica --- forest cover --- precipitation variation --- soil bioengineering --- base flow --- Poyang Lake Basin --- in situ calibration --- quantification --- chlorophyll fluorescence --- photoprotection --- remote sensing --- root distribution --- ecosystem model --- CASA --- afforestation ecosystem --- phenophase --- vegetation cover change --- soil characteristics --- Robinia pseudoacacia L. and Pinus tabulaeformis Carr. mixed plantations --- composted pine bark --- water-energy balance --- sediment load --- soil respiration --- energy partitioning --- soil microbial biomass --- transpiration --- net primary productivity --- spatio-temporal scales --- seedling quality --- peat moss --- fresh pine sawdust --- understory plants --- ring-porous trees --- different climatic conditions --- dye tests --- structural equation model --- Loess Plateau --- evapotranspiration (ET) --- Pinus engelmannii Carr. --- empirical statistics --- heat dissipation probes --- MODIS --- slope change ratio of cumulative quantities (SCRCQ) --- soil water balance --- LAI --- climate fluctuation --- BTOPMC model --- living brush mattress --- vegetation greening --- streamflow

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book provides a practical introduction to remote sensing applications for detecting changes in the terrestrial water cycle and understanding the causes and consequences of these changes. Covering a wide range of innovative remote sensing approaches for hydrological study, this book contributes significantly to the knowledge base of hydrology in the Anthropocene, i.e., global change hydrology. It is an excellent reference for students and professionals in the fields of hydrology, climate change, and geography.

Research & information: general --- Geography --- hydrological cycle --- Three-North region --- climate change --- land cover change --- Variable Infiltration Capacity (VIC) model --- evapotranspiration --- runoff --- soil moisture --- three-temperature model --- infrared remote sensing --- urban hedges --- cooling effects --- irrigation mapping --- remote sensing --- random forest --- subhumid region --- dry-wet regime --- vegetation dynamics --- GLDAS --- GIMMS --- Yarlung Zangbo River --- Microwave emissivity difference vegetation index (EDVI) --- evapotranspiration (ET) --- satellite remote sensing --- cloudy sky --- clouds and earth’s radiation energy system (CERES) --- ChinaFLUX --- precipitation classification --- K-nearest neighbor --- Doppler radar --- Tropical Precipitation Measurement Mission (TRMM) --- irrigation signal --- SMAP --- irrigation intensity --- winter wheat --- precipitation --- evaluation --- error analysis --- Fengyun --- quantitative precipitation estimates --- GPM --- IMERG --- deep learning --- Daihai Lake --- Huangqihai Lake --- lake degradation --- weather radar quantitative precipitation estimation --- rain gauge --- radar-rain gauge merging --- leave-one-out cross validation --- verification --- China --- exorheic catchments --- water balance --- GRACE --- terrestrial water storage changes --- reservoir storage --- MODIS --- SRTM --- n/a --- clouds and earth's radiation energy system (CERES)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

Research & information: general --- UAS --- UAV --- vegetation cover --- multispectral --- land cover --- forest --- Acacia --- Indonesia --- tropics --- vegetation ground cover --- vegetation indices --- agro-environmental measures --- olive groves --- southern Spain --- sUAS --- water stress --- ornamental --- container-grown --- artificial intelligence --- machine learning --- deep learning --- neural network --- visual recognition --- precision agriculture --- canopy cover --- image analysis --- crop mapping --- evapotranspiration (ET) --- GRAPEX --- remote sensing --- Two Source Energy Balance model (TSEB) --- contextual spatial domain/resolution --- data aggregation --- eddy covariance (EC) --- Fusarium wilt --- crop disease --- banana --- multispectral remote sensing --- purple rapeseed leaves --- unmanned aerial vehicle --- U-Net --- plant segmentation --- nitrogen stress --- Glycine max --- RGB --- canopy height --- close remote sensing --- growth model --- curve fitting --- NDVI --- solar zenith angle --- flight altitude --- time of day --- operating parameters --- CNN --- Faster RCNN --- SSD --- Inception v2 --- patch-based CNN --- MobileNet v2 --- detection performance --- inference time --- disease detection --- cotton root rot --- plant-level --- single-plant --- plant-by-plant --- classification --- UAV remote sensing --- crop monitoring --- RGB imagery --- multispectral imagery --- century-old biochar --- semantic segmentation --- random forest --- crop canopy --- multispectral image --- chlorophyll content --- remote sensing technique --- individual plant segmentation --- plant detection --- transfer learning --- maize tassel --- tassel branch number --- convolution neural network --- VGG16 --- plant nitrogen estimation --- vegetation index --- image segmentation --- transpiration --- method comparison --- oil palm --- multiple linear regression --- support vector machine --- artificial neural network --- UAV hyperspectral --- wheat yellow rust --- disease monitoring --- texture --- spatial resolution --- RGB camera --- thermal camera --- drought tolerance --- forage grass --- HSV --- CIELab --- broad-sense heritability --- phenotyping gap --- high throughput field phenotyping --- UAV digital images --- winter wheat biomass --- multiscale textures --- red-edge spectra --- least squares support vector machine --- variable importance --- drone --- hyperspectral --- thermal --- nutrient deficiency --- weed detection --- disease diagnosis --- plant trails

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book compiles a set of original and innovative papers included in the Special Issue on UAVs for vegetation monitoring, which proves the wide scope of UAVs in very diverse vegetation applications, both in agricultural and forestry scenarios, ranging from the characterization of relevant vegetation features to the detection of plant or crop stressors. New methods and techniques are developed and applied to diverse vegetation scenarios to meet the main challenge of sustainability.

UAS --- UAV --- vegetation cover --- multispectral --- land cover --- forest --- Acacia --- Indonesia --- tropics --- vegetation ground cover --- vegetation indices --- agro-environmental measures --- olive groves --- southern Spain --- sUAS --- water stress --- ornamental --- container-grown --- artificial intelligence --- machine learning --- deep learning --- neural network --- visual recognition --- precision agriculture --- canopy cover --- image analysis --- crop mapping --- evapotranspiration (ET) --- GRAPEX --- remote sensing --- Two Source Energy Balance model (TSEB) --- contextual spatial domain/resolution --- data aggregation --- eddy covariance (EC) --- Fusarium wilt --- crop disease --- banana --- multispectral remote sensing --- purple rapeseed leaves --- unmanned aerial vehicle --- U-Net --- plant segmentation --- nitrogen stress --- Glycine max --- RGB --- canopy height --- close remote sensing --- growth model --- curve fitting --- NDVI --- solar zenith angle --- flight altitude --- time of day --- operating parameters --- CNN --- Faster RCNN --- SSD --- Inception v2 --- patch-based CNN --- MobileNet v2 --- detection performance --- inference time --- disease detection --- cotton root rot --- plant-level --- single-plant --- plant-by-plant --- classification --- UAV remote sensing --- crop monitoring --- RGB imagery --- multispectral imagery --- century-old biochar --- semantic segmentation --- random forest --- crop canopy --- multispectral image --- chlorophyll content --- remote sensing technique --- individual plant segmentation --- plant detection --- transfer learning --- maize tassel --- tassel branch number --- convolution neural network --- VGG16 --- plant nitrogen estimation --- vegetation index --- image segmentation --- transpiration --- method comparison --- oil palm --- multiple linear regression --- support vector machine --- artificial neural network --- UAV hyperspectral --- wheat yellow rust --- disease monitoring --- texture --- spatial resolution --- RGB camera --- thermal camera --- drought tolerance --- forage grass --- HSV --- CIELab --- broad-sense heritability --- phenotyping gap --- high throughput field phenotyping --- UAV digital images --- winter wheat biomass --- multiscale textures --- red-edge spectra --- least squares support vector machine --- variable importance --- drone --- hyperspectral --- thermal --- nutrient deficiency --- weed detection --- disease diagnosis --- plant trails