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Wetlands are vital ecosystems providing essential services to both humans and the environment, yet they face threats from human activities leading to loss and disturbance. This study utilizes remote sensing (RS) methods, including object-based image analysis (OBIA), to map and assess wetland health in Kristianstad’s Vattenrike in the southernmost part of Sweden between 2015 and 2023. Objectives include exploring RS capabilities in detecting wetlands and changes, deriving wetland health indicators, and assessing classification accuracy. The study uses Sentinel-2 imagery, elevation data, and high-resolution aerial images to focus on wetlands along the river Helge å. Detection and classifications were based on Sentinel-2 imagery and elevation data, and the eCognition software was employed. The health assessment was based on the spectral indices Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (mNDWI). Validation was conducted through aerial photo interpretation. The derived classifications demonstrate acceptable accuracy levels and the analysis reveals relatively stable wetland conditions, with an increase in wetland area attributed to the construction of new wetlands. Changes in wetland composition, such as an increase in open meadows and swamp forests, were observed. However, an overall decline in NDVI values across the study area indicates potential degradation, attributed to factors like bare soil exposure and water presence. These findings provide insights into the local changes in wetland extent, composition, and health between the study years.

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First, the field survey campaigns have been carried out from March 22, 2007 to December 27, 2007 around 5-15 days in the interval in the study area of Nansha Island. The field work includes the survey of spatial distribution of various land use and crop types and the ground measurements of the crop biophysical parameters (such as the plant height, leave area index, fresh biomass, and plant water content) and the soil parameters (such as the soil water content and surface roughness parameters) of rice field and sugarcane field. And at the same time, the ENVISAT ASAR data were acquired from March 22, 2007 to December 27, 2007 in the interval of 35 days. During the acquisition dates of the ENVISAT ASAR data, the field surveys were also conducted. / Fourth, the sufficient ground measurements and simultaneous C-band HH- and VV-polarized SAR data of sugarcane crop have enriched the knowledge of understanding the temporal radar scatter mechanisms in sugarcane canopies. The C-band VV-polarized radar backscatters are larger than those of HH-polarization during the sugarcane growth cycle, and the difference is around 0.5 dB to 2 dB. The theoretical model MIMICS was adapted in modeling the scattering terms in sugarcane fields to interpret the temporal behavior of radar backscatters. For more robotic operation, the empirical regression models were used in estimation of the sugarcane LAI and fresh biomass, and mapping the sugarcane growth situation. The accuracies of the sugarcane LAI map and Biomass map are 0.74 and 0.70, respectively. / In conclusion, the C-band ENVISAT ASAR data can be efficiently used in the Pearl River Delta to monitor the crop growth, including the crop spatial distribution, crop acreages, and crop growth situation evaluation. The efficient crop growth monitoring program can not only help instruct the flexible farming actions, but also estimate the crop yield production for the decision-making government. (Abstract shortened by UMI.) / Second, field surveys were combined with the ENVISAT ASAR data to map the agricultural area. The analysis of the temporal radar backscatter characteristics of various land cover categories demonstrated that the time series of C-band SAR data is efficient in separating the eight land cover categories (rice paddy, sugarcane, banana, lotus ponds, mangrove wetlands, fish ponds, seawater, and buildings) in the PRD. The decision tree classifier is also approved to work efficiently on satellite SAR images with an overall accuracy of 77% and the Kappa coefficient of 0.74. The acreages of the land cover categories were also derived from the classification result with accuracies from 70% to 90%. / The Pearl River Delta is a typical developing region. It lies in the cloud-prone and rainy area of south China with multi-species of crops cultured in the agriculture areas. With a goal of developing an efficient, timely and accurate crop growth monitoring program in this area, field measurement, satellite SAR remote sensing technique, quantitative analysis of the crop biophysical parameters, and radar backscatter modeling methods have been integrated to study the multi-temporal and multi-polarized SAR data in estimating plant parameters (LAI, fresh biomass) of rice and sugarcane crop, and mapping the agricultural land cover categories of the study area in the PRD. / Third, in the study of rice growth monitoring, the trends of the relationships between C-band radar backscattering coefficients and rice parameters (plant height, LAI, fresh biomass, et al.) are proved to be constant with the reports in previous literatures. It was demonstrated that the differences between HH- and VV-polarized backscatter are not so evident (around 0.5 dB) in rice paddy canopies during the crop growth cycle. Moreover, by inducting a semi-empirical soil surface scattering component, a modified Water Cloud Model was developed to simulate the radar backscatter in rice crop canopies in different ground background situations (water surface, and soil surface) and to estimate the rice LAI and above ground fresh Biomass with reasonable accuracy. The rice growth conditions were displayed by LAI map and Biomass map generated from the model estimation, and the accuracies of the LAI and Biomass level classification are 0.77 and 0.71. / Wang, Dan. / Advisers: Hui Lin; Jin-Song Chen. / Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 132-138). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Landscape heterogeneity is thought to differ among farm management types (i.e. organic and conventional), and this difference is hypothesized to result in variations in pest control by natural enemies. However, it is unclear if these variations in pest control are driven by landscape structure or by farm management practices themselves. Remotely sensed datasets were used to describe the landscape structure surrounding a group of organic and conventional caneberry farms in Oregon and Washington that have different leafroller parasitism rates attributed to farm management type. A finer scale survey was done at one of the farms using the remotely sensed data as well as field surveys. Landscape metrics of diversity, richness and percent non-crop were used to describe the landscapes surrounding the farm fields at scales ranging from 0.05 km to 5.00 km for the large scale study, and 0.05 km to 0.20 km for the fine scale study. In the fine scale study, data on parasitoid species assemblages, diversity, and parasitism rate were collected and analyzed against the calculated landscape metrics spatially and seasonally. The purpose of this study was to quantify effects of farm management type on habitat structure, effect of habitat structure on leafroller parasitism rate, and to access correlations between landscape metrics calculated at the landscape and field scale. Overall, the farms were embedded in a landscape that was broadly similar, with very few differences in landscape structure occurring between organic and conventional farms. Organic farms had higher vegetation height class diversity at the largest scale compared to conventional farms, while conventional farms had significantly higher percent non-crop area compared to organic farms. There was no significant effect of any of the calculated landscape metrics on parasitism rates. In the field scale study, no correlations were found between habitat metrics and parasitism rates, or between field based metrics and those calculated at the landscape scale. The results of this study suggest that conventional and organic caneberry farms in the Willamette Valley are broadly similar in the habitat conditions they provide parasitoids. This suggests that management changes to pesticide use alone could increase levels of leafroller biological control on conventional farms to levels that are comparable to those seen on organic farms. Our comparisons of the landscape scale and field scale landscape metrics showed no connection, this suggests that direct comparisons cannot be made with these particular metrics at these very different scales. Rather than comparing these types of data, it may be more useful to combine them in order to increase the resolution and predictive power of remotely sensed data for describing landscapes at broad scales. / Graduation date: 2013

Indiana University-Purdue University Indianapolis (IUPUI) / The primary objective of this research involves mapping out and validating the existence of sub surface drainage tiles in a given cropland using Remote Sensing and GIS methodologies. The process is dependent on soil edge differentiation found in lighter versus darker IR reflectance values from tiled vs. untiled soils patches. Data is collected from various sources and a primary classifier is created using secondary field variables such as soil type, topography and land Use and land cover (LULC). The classifier mask reduces computational time and allows application of various filtering algorithms for detection of edges. The filtered image allows an efficient feature recognition platform allowing the tile drains to be better identified. User defined methods and natural vision based methodologies are also developed or adopted as novel techniques for edge detection. The generated results are validated with field data sets which were established using Ground Penetration Radar (GPR) studies. Overlay efficiency is calculated for each methodology along with omission and commission errors. This comparison yields adaptable and efficient edge detection techniques which can be used for similar areas allowing further development of the tile detection process.

Vegetation status is a key indicator of the ecosystem condition in a particular area. The study objective was about the estimation of leaf nitrogen (N) as an indicator of vegetation water stress using vegetation indices especially the red edge based ones, and how leaf N concentration is influenced by various environmental factors. Leaf nitrogen was estimated using univariate and multivariate regression techniques of stepwise multiple linear regression (SMLR) and random forest. The effects of environmental parameters on leaf nitrogen distribution were tested through univariate regression and analysis of variance (ANOVA). Vegetation indices were evaluated derived from the analytical spectral device (ASD) data, resampled to RapidEye. The multivariate models were also developed to predict leaf N. The best model was chosen based on the lowest root mean square error (RMSE) and higher coefficient of determination (R2) values. Univariate results showed that red edge based vegetation index called MERRIS Terrestrial Chlorophyll Index (MTCI) yielded higher leaf N estimation accuracy as compared to other vegetation indices. Simple ratio (SR) based on the bands red and near-infrared was found to be the best vegetation index for leaf N estimation with exclusion of red edge band for stepwise multiple linear regression (SMLR) method. Simple ratio (SR3) was the best vegetation index when red edge was included for stepwise linear regression (SMLR) method. Random forest prediction model achieved the highest leaf N estimation accuracy, the best vegetation index was Red Green Index (RGI1) based on all bands with red green index when including the red edge band. When red edge band was excluded the best vegetation index for random forest was Difference Vegetation Index (DVI1). The results for univariate and multivariate results indicated that the inclusion of the red edge band provides opportunity to accurately estimate leaf N. Analysis of variance results showed that vegetation and soil types have a significant effect on leaf N distribution with p-values<0.05. Red edge based indices provides opportunity to assess vegetation health using remote sensing techniques. / Environmental Sciences / M. Sc. (Environmental Management)

Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R²) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications. / Graduation date: 2013