Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements

Responses of Land Surface Phenology to Wildfire Disturbances in the Western United States Forests

Land surface phenology (LSP) characterizes the seasonal dynamics in the vegetation communities observed for a satellite pixel and it has been widely associated with global climate change. However, LSP and its long-term trend can be influenced by land disturbance events, which could greatly interrupt the LSP responses to climate change. Wildfire is one of the main disturbance agents in the western United States (US) forests, but its impacts on LSP have not been investigated yet. To gain a comprehensive understanding of the LSP responses to wildfires in the western US forests, this dissertation focused on three research objectives: (1) to perform a case study of wildfire impacts on LSP and its trend by comparing the burned and a reference area, (2) to investigate the distribution of wildfire impacts on LSP and identify control factors by analyzing all the wildfires across the western US forests, and (3) to quantify the contributions of land cover composition and other environmental factors to the spatial and interannual variations of LSP in a recently burned landscape. The results reveal that wildfires play a significant role in influencing spatial and interannual variations in LSP across the western US forests. First, the case study showed that the Hayman Fire significantly advanced the start of growing season (SOS) and caused an advancing SOS trend comparing with a delaying trend in the reference area. Second, summarizing \u3e800 wildfires found that the shifts in LSP timing were divergent depending on individual wildfire events and burn severity. Moreover, wildfires showed a stronger impact on the end of growing season (EOS) than SOS. Last, LSP trends were interrupted by wildfires with the degree of impact largely dependent on the wildfire occurrence year. Third, LSP modeling showed that land cover composition, climate, and topography co-determine the LSP variations. Specifically, land cover composition and climate dominate the LSP spatial and interannual variations, respectively. Overall, this research improves the understanding of wildfire impacts on LSP and the underlying mechanism of various factors driving LSP. This research also provides a prototype that can be extended to investigate the impacts on LSP from other disturbances

Study of the urban heat island (UHI) using remote sensing data/techniques: a systematic review.

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus andWeb of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020. This work was funded by National Funds through the FCT-Foundation for Science and Technology and FEDER, under the projects UIDB/04683/2020 and PT2020 Program for financial support to CIMO UIDB/00690/2020.info:eu-repo/semantics/publishedVersio

Comparison between satellite-based and cosmic ray probe soil moisture estimates : a case study in the Cathedral Peak catchment.

Master of Science in Environmental Hydrology. University of KwaZulu-Natal, Pietermaritzburg 2015.Abstract available in PDF file

A Geospatial Approach for Analysis of Drought Impacts on Vegetation Cover and Land Surface Temperature in the Kurdistan Region of Iraq

Drought is a common event in Iraq’s climate, and the country has severely suffered from drought episodes in the last two decades. The Kurdistan Region of Iraq (KRI) is geographically situated in the semi-arid zone in Iraq, whose water resources have been limited in the last decades and mostly shared with other neighboring countries. To analyze drought impacts on the vegetation cover and the land surface temperature in the KRI for a span of 20 years from 1998 to 2017, remote sensing (RS) and Geographical Information Systems (GIS) have been adopted in this study. For this study, 120 Landsat satellite images were downloaded and utilized, whereas six images covering the entire study area were used for each year of the study period. The Normalized Difference Vegetation Index (NDVI) and Land Surfaces Temperature Index (LST) were applied to produce multi-temporal classified drought maps. Changes in the area and values of the classified NDVI and LST were calculated and mapped. Mann–Kendall and Sen’s Slope statistical tests were used to assess the variability of drought indices variation in 60 locations in the study area. The results revealed increases in severity and frequency of drought over the study period, particularly in the years 2000 and 2008, which were characterized by an increase in land surface temperatures, a decrease in vegetation area cover, and a lack of precipitation averages. Climate conditions affect the increase/decrease of the vegetated cover area, and geographical variability is also one factor that significantly influences the distribution of vegetation. It can be concluded that the southeast and southwestern parts of the KRI were subjected to the most severe droughts over the past 20 years

The effects of drought on predicted air quality in Texas

textDrought is a natural disaster that has profound and complex social, economic, and environmental impacts. As drought is predicted to occur more frequently within Texas with changes in future climate, it is critical to understand its impacts on regional air quality as the State endeavors to achieve and maintain attainment with National Ambient Air Quality Standards for ozone and fine particulate matter. Drought-induced changes in various natural systems, including emissions of biogenic hydrocarbons from vegetation and the physical removal of pollutants by vegetation via dry deposition, have the potential to effect air quality. This work characterizes land cover for eastern Texas climate regions during years with severe to exceptional drought conditions as well as years with average to above average precipitation patterns. Variability in meteorological conditions, biogenic emissions, and dry deposition rates is explored with widely applied global and regional models that have been configured specifically for multi-year analysis of eastern Texas conditions. The Comprehensive Air Quality Model with Extensions (CAMx), which has been used for air quality planning and management efforts in Texas, is used to quantify the relative contributions of various physical and chemical processes to ground-level ozone formation and changes in ground-level ozone concentrations during representative drought and wet periods. The analyses indicate that drought influences air quality in complex ways. This work suggests that the two largest drought driven changes to the physical and chemical processes that influence air quality are increased biogenic emissions due to elevated temperatures and decreased air pollutant removal through dry deposition due to changes in leaf-level processes. Both of these changes degrade air quality and their combined effect can be as large as an increase of approximately 5 ppb in ground level, 8-hour averaged ozone concentrations in parts of eastern Texas. The effects of soil moisture on biogenic emissions estimates can be as significant as temperature, but current land surface model configurations and the adequacy of the Model of Emissions of Gases and Aerosols Nature (MEGAN) algorithm to fully represent short and long-term responses to soil moisture remain highly uncertain. The characterization of soil moisture through ground and satellite-based measurement programs and validation of global and regional-scale land cover distributions should continue to be high priorities to support air quality planning in Texas.Chemical Engineerin

機械学習を用いた空間ダウンスケールの気温予測法及び都市ヒートアイランドへの応用に関する研究

This study introduced a temperature spatial downscaling method based on machine learning algorithm to downscale air temperature from 1 km to 250 m for high-resolution atmosphere urban heat island (UHI) analysis. The core of this downscaling method is to establish the regression model between urban structure and temperature, and then we used the unchanged characteristics of regression models at different scale to predict high-resolution temperature data with high-resolution resolution urban structure, thereby analyzed atmosphere urban heat island. Finally, we compared the similarity and differentiation between atmosphere UHI and surface UHI. The results indicated the following: (1) The machine learning method was proved to be suitable for the air temperature spatial downscaling predication; (2) The UHI characteristics of metropolitan areas in different climatic regions of Japan are different; (3) There are great differences in intensity and spatial distribution between atmosphere UHI and surface UHI is great.北九州市立大