Bharat Bhushan, Surender Singh, Somnath Mandal, Vipasha Sharma, Pradeep Kumar, Attila Fülöp, Sara Elhdad
Rohtak, which is a major city in the National Capital Region (NCR) of India, has experienced a fast-tracked transformation over the last 40 years as shown by the increased population growth through migration, urban development, and spatial restructuring. The research applies the landscape ecological metrics and the multivariate trajectory analysis to examine the distance and direction specific nature of urban development during 2000–2020, through the Mann-Kendall and Principal Component Analysis (PCA). The findings indicate that there is a high level of anisotropy in the growth patterns with the northern, western and north-western regions recording the highest growth due to the existence of educational hubs, industrial estates and improved transport connectivity leaving the eastern corridor relatively underdeveloped. A distance-gradient analysis suggests that infill saturation occurs between 2 and 4 km of the core, whereas leapfrog and edge-expansion processes occur between 6 and 10km, especially around the larger institutions and the Industrial Model Township. It is possible to identify three phases of urban development: the first fragmented phase (2000–2010), the transitional phase of urban development (2010–2015), and the coalescence phase (2015–2020), which can be characterized by the transition between dispersed urban forms and compact urban forms. Directional heterogeneity is also supported by trajectory metrics, whereby long, linear growth paths are oriented towards the north, southeast and south, and little expansion towards the west. On the whole, Rohtak is a typical ripple-effect urbanisation model, with a shift in the monocentric core to the polycentric, corridor-based growth at the edges. The results provide a critical role of the institutional hubs, industrial estates, and transport infrastructure in determining urban trajectories, hence providing important insights into sustainable urban planning in the fast-growing secondary cities in the NCR.
R. Wayne Litaker, Michelle C. Tomlinson, William C. Holland, Kimberly S. Reece, Todd A. Egerton, Travis O. Briggs, Gail P. Scott, Hamish J. Small, Sylvain Gaillard, Savannah A. Mapes
Harmful algal blooms of Alexandrium monilatum and Margalefidinium polykrikoides are a common occurrence in the tributaries of lower Chesapeake Bay, United States of America. This study evaluated whether daily processed imagery from the Ocean and Land Colour Instrument (OLCI) can accurately measure chlorophyll a (chl a) concentrations in these turbid, bloom-prone tributaries. Since HAB cell concentrations provide a more direct measure of risk than chl a, a second objective was to quantify the accuracy of bloom cell concentrations derived from satellite chl a. Field collected chl a concentrations and processed OLCI data were used to evaluate the accuracy of satellite-derived chl a concentrations in the York and James Rivers. Field, laboratory, and literature data were used to quantify the precision of satellite-based cell estimates. Satellite-derived chl a estimates correlated well with laboratory extracted chl a from the field and were stable over time for the entire OLCI satellite image dataset. The results of laboratory experiments and field studies showed that variations in the pg chl a cell -1 used to convert chl a to cell estimates, often varied five- to hundred-fold, depending on prevailing environmental growth conditions and the chl a contributed by co-occurring phytoplankton. The satellite-derived chl a concentrations proved accurate over a wide range of chl a concentrations, confirming their usefulness in tracking high biomass blooms and guiding sampling efforts required to evaluate potential adverse impacts, and as a basis for potentially estimating relative HAB cell concentrations. However, field, literature, and laboratory data showed that chl a cell -1 estimates at a given chl a concentration can vary by up to 100-fold. Consequently, satellite-based cell concentrations will be highly inaccurate. More accurate HAB cell estimates might be possible if chl a cell -1 is measured periodically during a bloom and used to estimate cell concentration from satellite chl a in real-time.
Sheila Baber, Sergii Skakun, Yuval Sadeh, Leonid Shumilo, Oleksandra Oliinyk, Mehdi Hosseini, Josef Wagner, Abdul Qadir Khan, Shabarinath Nair, Abhishek Kotcharlakota, Inbal Becker-Reshef
The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.
Eva Scrivner, Dexter Hamilton, Daniel Sousa, Margot Mattson, Nicholas E. Williams, Seeta Sistla
Global application of agricultural plastic mulch is increasing. While these films offer benefits for crop production, they also are a large source of plastic waste and pollution in both terrestrial and aquatic systems. The current scale of global mulch application and its deleterious impacts are unclear. Current estimates of agricultural plastic use remain limited by field-specific growing patterns and data limitations. Satellite spectroscopy may offer a means to reduce this uncertainty and model agricultural plastic use on large spatial and temporal scales, though temporally variable crop canopy growth and spectrally diverse plastic targets complicate this approach. To build toward improved plasticulture estimates, this study presents an interpretable, spectral-based classification of bulk plastic use in a California agricultural site using Sentinel-2 imagery. In situ reflectance measurements of soil and white, green, and black plastic mulch endmembers were collected using a Spectra Vista Corporation HR-1024i Spectroradiometer from 350–2500 nm. A combination of field observations and high spatial resolution NearMap imagery (n = 416 spectra) were compiled and divided into 60% training and 40% testing data using a stratified sampling approach. Informed by field reflectance data, mixture analyses, and Sentinel-2 reflectance imagery, an empirical decision tree classification was parameterized to classify dark and bright plastic debris from soil and crop (accuracy = 0.982, kappa coefficient = 0.973, macro-averaged F1-score = 0.967). Modeled agricultural plastic distributions were then retrieved through a time series of Sentinel-2 imagery over Santa Maria, CA, from April 2023 to January 2025, elucidating patterns in plastic application and crop canopy growth without the need for prior knowledge of field growing and management patterns. These results are encouraging for the utility of optical remote sensing in modeling plastic use patterns and identifying contamination hotspots throughout California and, potentially, similarly mulched agroecological systems.
