Geology topics

Kamel Didan

Publications and source records attributed to Kamel Didan.

18 recordsLinked to original sources

USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗

USGS2024

Effect of water delivery and irrigation for riparian restoration in the Colorado River Delta, Mexico

Along Mexico's arid Colorado River Delta, the riparian corridor lacks water due to a reduction in frequent flows, climate change, human infrastructure, and altered riparian landcover from disturbances to invasive species, fire, and high soil and water salinities, which have led to declines in riparian plant health in recent decades. Restoration efforts focusing on small plots have successfully revitalized habitat, which is the motivation for this research. Accurate estimations of water use by riparian vegetation are crucial in arid environments, where measuring actual evapotranspiration (ETa) poses a significant challenge in these narrow corridors. This study utilizes field-validated remote sensing techniques to quantify ETa at restoration sites. Our methods are twofold; we use the Landsat-8 two-band Enhanced Vegetation Index (EVI2) to monitor changes in vegetation greenness—a proxy of plant health—and we integrate EVI2 with potential evapotranspiration (ET) to calculate ETa. Our findings reveal a notable increase in vegetation greenness within the restoration sites over 9 years, with an average increase of 41.3%. Conversely, greenness in adjacent, unrestored control areas declined by 27.3%. The study also indicates a 22.1% increase in ETa in the restored areas, compared to a 30.8% reduction in the unrestored regions. Restored sites in reach 4 experienced ETa increases ranging from 9.2 to 12.2%, whereas their unrestored counterparts show a decline of 21.4%. Valuable estimates are provided of riparian greenness and water use that may assist natural resource managers who are tasked with allocating water and managing habitats within similar riparian corridors.

Colorado River Delta↗

USGS2024

Greenness and actual evapotranspiration in the unrestored riparian corridor of the Colorado River Delta in response to in-channel water deliveries in 2021 and 2022

Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2).

Colorado River and Delta↗

USGS2023

Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin

Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.

Lower Colorado River basin↗

USGS2023

Random forest classification of multitemporal Landsat 8 spectral data and phenology metrics for land cover mapping in the Sonoran and Mojave Deserts

Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation Region (BCR 33) due to the area’s complex vegetation communities and the discontinuity in data availability across the United States (US) and Mexico (MX) border. This research aimed to produce the first 30 m continuous land cover map of BCR 33 by prototyping new methods for desert vegetation classification using the Random Forest (RF) machine learning (ML) method. The developed RF classification model utilized multitemporal Landsat 8 Operational Land Imager spectral and vegetation index data from the period of 2013–2020, and phenology metrics tailored to capture the unique growing seasons of desert vegetation. Our RF model achieved an overall classification F-score of 0.80 and an overall accuracy of 91.68%. Our results portrayed the vegetation cover at a much finer resolution than existing land cover maps from the US and MX portions of the study area, allowing for the separation and identification of smaller habitat pockets, including riparian communities, which are critically important for desert wildlife and are often misclassified or nonexistent in current maps. This early prototyping effort serves as a proof of concept for the ML and data fusion methods that will be used to generate the final high-resolution land cover map of the entire BCR 33 region.

Remote Sensing↗

USGS2023

Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI* scaled , and ET-NDVI Kc ) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI* scaled estimations were close. ET-NDVI Kc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVI Kc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI* scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons.

Remote Sensing↗

USGS2022

Riparian plant evapotranspiration and consumptive use for selected areas of the Little Colorado River watershed on the Navajo Nation

Estimates of riparian vegetation water use are important for hydromorphological assessment, partitioning within human and natural environments, and informing environmental policy decisions. The objectives of this study were to calculate the actual evapotranspiration (ETa) (mm/day and mm/year) and derive riparian vegetation annual consumptive use (CU) in acre-feet (AF) for select riparian areas of the Little Colorado River watershed within the Navajo Nation, in northeastern Arizona, USA. This was accomplished by first estimating the riparian land cover area for trees and shrubs using a 2019 summer scene from National Agricultural Imagery Program (NAIP) (1 m resolution), and then fusing the riparian delineation with Landsat-8 OLI (30-m) to estimate ETa for 2014–2020. We used indirect remote sensing methods based on gridded weather data, Daymet (1 km) and PRISM (4 km), and Landsat measurements of vegetation activity using the two-band Enhanced Vegetation Index (EVI2). Estimates of potential ET were calculated using Blaney-Criddle. Riparian ETa was quantified using the Nagler ET(EVI2) approach. Using both vector and raster estimates of tree, shrub, and total riparian area, we produced the first CU measurements for this region. Our best estimate of annual CU is 36,983 AF with a range between 31,648–41,585 AF and refines earlier projections of 25,387–46,397 AF.

Arizona, Colorado, New Mexico, Utah↗

USGS2022

Effect of restoration on plant greenness and water use in relation to drought in the riparian corridor of the Colorado River delta

Revitalization of the Colorado River delta riparian corridor and increasing riparian plant greenness and water use may be accomplished by added water and restoration efforts to offset declines measured since 2000 by Landsat. We use the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness) and evapotranspiration (ET, mm/day) using EVI2 and potential ET(ETo) from Yuma Valley. We assess if restoration with only 7.5% landcover had an impact on the unrestored reach-level landcover by measuring these two metrics, EVI2 and ET(EVI2) by comparing restored and unrestored areas. A key finding is that over 21-years EVI2 in the unrestored corridor decreased by 23.6% and ET(EVI2) decreased by 32% (0.87 mm/day). Since 2011, the unrestored reaches lost EVI2 (11%) and −0.73 mm/day ET(EVI2) (28%), but restored sites increased in EVI2 (36%) and 0.58 mm/day ET(EVI2) (20%). Water delivered to restored sites increased EVI2 by 33.6% and 1.29 mm/day ET(EVI2) (58%). EVI2 and ET(EVI2) as a function of the Standardized Precipitation Evapotranspiration Index for drought periods highlight that restored and unrestored sites respond differently. Unrestored reaches are in decline; restored sites show increases in EVI2 and ET(EVI2). Restored sites do not have a significant impact on unrestored adjacent area, but smaller surface flows, a greater reliance on directed agricultural return flows, and deliveries of water to active restoration sites have revitalized habitat and increased ecosystem services in the delta.

Colorado River delta↗

USGS2022

Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018

This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date. This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively). This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.

Colorado River Delta↗

USGS2021

Estimating actual evapotranspiration over croplands using vegetation index methods and dynamic harvested area

Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVI MccL and EVI2 MccL ) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

Remote Sensing↗

USGS2021

Riparian area changes in greenness and water use on the Lower Colorado River in the USA from 2000-2020

Declines in riparian ecosystem greenness and water use have been observed in the delta of the Lower Colorado River (LCR) since 2000. The purpose of our case study was to measure these metrics on the U.S. side of the border between Hoover and Morelos Dams to see if declining greenness was unique to the portion of the river in Mexico. In this case study, five riparian reaches of the LCR from Hoover to Morelos Dam since 2000 were studied to evaluate trends in riparian ecosystem health. We measure these riparian woodlands using remotely sensed measurements of the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness); daily evapotranspiration (ET; mmd −1 ) using EVI2 (ET(EVI2)); and an annualized ET based on EVI2, the Phenology Assessment Metric (PAM ET), an annualized ET using Landsat time-series. A key finding is that riparian health and its water use has been in decline since 2000 on the U.S. portion of the LCR, depicting a loss of green vegetation over the last two decades. EVI2 results show a decline of −13.83%, while average daily ET(EVI2) between the first and last decade had a decrease of over 1 mmd −1 (−27.30%) and the respective average PAM ET losses were 170.91 mmyr −1 (−17.95%). The difference between the first and last five-year periods, 2000–2005 and 2016–2020, showed the largest decrease in daily ET(EVI) of 1.24 mmd −1 (−32.61%). These declines come from a loss in healthy, green, riparian plant-cover, not a change in plant water use efficiency nor efficient use of managed water resources. Our results suggest further deterioration of biodiversity, wildlife habitat and other key ecosystem services on the U.S. portion of the LCR.

Arizona, California↗

USGS2020

Ecohydrological responses to surface flow across borders: Two decades of changes in vegetation greenness and water use in the riparian corridor of the Colorado River Delta

Hydrological and bioclimatic processes that lead to drought may stress plants and wildlife, restructure plant community type and architecture, increase monotypic stands and bare soils, facilitate the invasion of non‐native plant species and accelerate soil erosion. Our study focuses on the impact of a paucity of Colorado River surface flows from the United States (U.S.) to Mexico. We measured change in riparian plant greenness and water use over the past two decades using remotely sensed measurements of vegetation index (VI), evapotranspiration (ET), and a new annualized Phenology Assessment Metric (PAM) for ET. We measure these long‐term (2000‐2019) metrics and their short‐term (2014‐2019) response to an environmental, pulse flow in 2014, as prescribed under Minute 319 of the 1944 Water Treaty between the two nations. In subsequent years, small directed flows were provided to restoration areas under Minute 323. We use 250 m MODIS and 30 m Landsat imagery to evaluate three vegetation indices (NDVI, EVI, EVI2). We select EVI2 to parameterize an optical‐based ET algorithm and test the relationship between ET from Landsat and MODIS by regression approaches. Our analyses show significant decreases in VIs and ET for both the 20‐year and post‐pulse 5‐year periods. Over the last 20 years, EVI Landsat declined 34% (30% by EVI MODIS ) and ET Landsat‐EVI declined 38% (27% by ET MODIS‐EVI ), overall ca. 1.61 mmd ‐1 or 476 mmyr ‐1 drop in ET. Over the 5 years since the 2014 pulse flow, EVI Landsat declined 20% (13% by EVI MODIS ) and ET Landsat‐EVI declined 23% (4% by ET MODIS‐EVI ) with a 0.77 mmd ‐1 or a 209 mmyr ‐1 5‐year drop in ET. Data and change maps show the pulse flow contributed enough water to slow the rate of loss, but only for the very short‐term (1‐2 years). These findings are critically important as they suggest further deterioration of biodiversity, wildlife habitat and key ecosystem services due to anthropogenic diversions of water in the U.S. and Mexico and from land clearing, fires, and plant‐related drought which affect hydrological processes.

Colorado River delta↗

USGS2020

Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data

Because groundwater recharge in dry regions is generally low, arid and semiarid environments have been considered well‐suited for long‐term isolation of hazardous materials (e.g., radioactive waste). In these dry regions, water lost (transpired) by plants and evaporated from the soil surface, collectively termed evapotranspiration (ET), is usually the primary discharge component in the water balance. Therefore, vegetation can potentially affect groundwater flow and contaminant transport at waste disposal sites. We studied vegetation health and ET dynamics at a Uranium Mill Tailings Radiation Control Act (UMTRCA) disposal site in Shiprock, New Mexico, where a floodplain alluvial aquifer was contaminated by mill effluent. Vegetation on the floodplain was predominantly deep‐rooted, non‐native tamarisk shrubs ( Tamarix sp.). After the introduction of the tamarisk beetle ( Diorhabda sp.) as a biocontrol agent, the health of the invasive tamarisk on the Shiprock floodplain declined. We used Landsat normalized difference vegetation index (NDVI) data to measure greenness and a remote sensing algorithm to estimate landscape‐scale ET along the floodplain of the UMTRCA site in Shiprock prior to (2000–2009) and after (2010–2018) beetle establishment. Using groundwater level data collected from 2011 to 2014, we also assessed the role of ET in explaining seasonal variations in depth to water of the floodplain. Growing season scaled NDVI decreased 30% ( p < .001), while ET decreased 26% from the pre‐ to post‐beetle period and seasonal ET estimates were significantly correlated with groundwater levels from 2011 to 2014 ( r 2 = .71; p = .009). Tamarisk greenness (a proxy for health) was significantly affected by Diorhabda but has partially recovered since 2012. Despite this, increased ET demand in the summer/fall period might reduce contaminant transport to the San Juan River during this period.

New Mexico↗

USGS2020

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high‐, medium‐ and coarse‐spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI‐based ET, over a 780‐ha urban park in Adelaide, Australia. We validated ET with the ground‐based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long‐term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET‐MODIS and ET‐Landsat (R2 > 0.99 for all VIs). Landsat‐VIs captured the seasonal changes of greenness better than MODIS‐VIs. We used artificial neural network (ANN) to relate the RS‐ET and ground data, and ET‐MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS‐ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water‐limited cities.

Adelaide↗

USGS2018

Application and comparison of the MODIS-Derived Enhanced Vegetation Index (EVI) to VIIRS, Landsat 5 TM, and Landsat 8 OLI platforms: A case study in the arid Colorado River Delta, Mexico

The Enhanced Vegetation Index (EVI) is a key Earth science parameter used to assess vegetation, originally developed and calibrated for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. With the impending decommissioning of the MODIS sensors by the year 2020/2022, alternative platforms will need to be used to estimate EVI. We compared Landsat 5 (2000–2011), 8 (2013–2016) and the Visible Infrared Imaging Radiometer Suite (VIIRS; 2013–2016) to MODIS EVI (2000–2016) over a 420,083-ha area of the arid lower Colorado River Delta in Mexico. Over large areas with mixed land cover or agricultural fields, we found high correspondence between Landsat and MODIS EVI (R 2 = 0.93 for the entire area studied and 0.97 for agricultural fields), but the relationship was weak over bare soil (R 2 = 0.27) and riparian vegetation (R 2 = 0.48). The correlation between MODIS and Landsat EVI was higher over large, homogeneous areas and was generally lower in narrow riparian areas. VIIRS and MODIS EVI were highly similar (R 2 = 0.99 for the entire area studied) and did not show the same decrease in performance in smaller, narrower regions as Landsat. Landsat and VIIRS provide EVI estimates of similar quality and characteristics to MODIS, but scale, seasonality and land cover type(s) should be considered before implementing Landsat EVI in a particular area.

Colorado River Delta↗

USGS2015

Wide-area estimates of evapotranspiration by red gum ( Eucalyptus camaldulensis ) and associated vegetation in the Murray-Darling River Basin, Australia

Floodplain red gum forests ( Eucalyptus camaldulensis plus associated grasses, reeds and sedges) are sites of high biodiversity in otherwise arid regions of southeastern Australia. They depend on periodic floods from rivers, but dams and diversions have reduced flood frequencies and volumes, leading to deterioration of trees and associated biota. There is a need to determine their water requirements so environmental flows can be administered to maintain or restore the forests. Their water requirements include the frequency and extent of overbank flooding, which recharges the floodplain soils with water, as well as the actual amount of water consumed in evapotranspiration (ET). We estimated the flooding requirements and ET for a 38 134 ha area of red gum forest fed by the Murrumbidgee River in Yanga National Park, New South Wales. ET was estimated by three methods: sap flux sensors placed in individual trees; a remote sensing method based on the Enhanced Vegetation Index from MODIS satellite imagery and a water balance method based on differences between river flows into and out of the forest. The methods gave comparable estimates yet covered different spatial and temporal scales. We estimated flood frequency and volume requirements by comparing Normalized Difference Vegetation Index values from Landsat images with flood history from 1995 to 2014, which included both wet periods and dry periods. ET during wet years is about 50% of potential ET but is much less in dry years because of the trees' ability to control stomatal conductance. Based on our analyses plus other studies, red gum trees at this location require environmental flows of 2000 GL yr −1 every other year, with peak flows of 20 000 ML d −1 , to produce flooding sufficient to keep them in good condition. However, only about 120–200 GL yr −1 of river water is consumed in ET, with the remainder flowing out of the forest where it enters the Murray River system.

Murray-Darling River Basin↗

USGS2008

Wide-area estimates of saltcedar (Tamarix spp.) evapotranspiration on the lower Colorado River measured by heat balance and remote sensing methods

In many places along the lower Colorado River, saltcedar ( Tamarix spp) has replaced the native shrubs and trees, including arrowweed, mesquite, cottonwood and willows. Some have advocated that by removing saltcedar, we could save water and create environments more favourable to these native species. To test these assumptions we compared sap flux measurements of water used by native species in contrast to saltcedar, and compared soil salinity, ground water depth and soil moisture across a gradient of 200–1500 m from the river's edge on a floodplain terrace at Cibola National Wildlife Refuge (CNWR). We found that the fraction of land covered ( f c ) with vegetation in 2005–2007 was similar to that occupied by native vegetation in 1938 using satellite-derived estimates and reprocessed aerial photographs scaled to comparable spatial resolutions (3–4 m). We converted f c to estimates of leaf area index (LAI) through point sampling and destructive analyses ( r 2 = 0·82). Saltcedar LAI averaged 2·54 with an f c of 0·80, and reached a maximum of 3·7 with an f c of 0·95. The ranges in f c and LAI are similar to those reported for native vegetation elsewhere and from the 1938 photographs over the study site. On-site measurements of water use and soil and aquifer properties confirmed that although saltcedar grows in areas where salinity has increased much better than native shrubs and trees, rates of transpiration are similar. Annual water use over CNWR was about 1·15 m year −1 .

Arizona, California↗

USGS2008

Scaling sap flux measurements of grazed and ungrazed shrub communities with fine and coarse-resolution remote sensing

We measured transpiration by black greasewood ( Sarcobatus vermiculatus ) (SAVE) and fourwing saltbush ( Atriplex canescens ) (ATCA) over a nitrate-contaminated aquifer in Monument Valley, Arizona, on the Colorado Plateau. Heat balance sap flow sensors were used to measure transpiration by shrubs in 2006 and 2007 and results were scaled to larger landscape units and longer time scales using leaf area index (LAI), fractional vegetation cover, meteorological data, and the enhanced vegetation index (EVI) from MODIS sensors on the Terra satellite. Transpiration was high depending on leaf area (2·95–6·72 kg m −2 d −1 ) and was controlled by vapour pressure deficit ( D ) in the atmosphere. SAVE tended to have higher transpiration rates than ATCA and had a steeper response to D , but both exhibited midday depression of leaf conductance. Over most of the site, fractional vegetation cover ( f c ) and area-wide LAI were low (0·10 and 0·37, respectively) due to heavy grazing by cattle and sheep. However, a portion of the plume that had been protected from grazing for 10 years had f c = 0·75, LAI = 2·88. Transpiration rates on a ground-area basis varied with LAI, with midsummer daily values ranging from 1·44 mm d −1 (LAI = 0·36) to 13·1 mm d −1 (LAI = 2·88 mm) over the site, corresponding to projected annual values of 159–1447 mm year −1 . Controlling grazing could, theoretically, slow or halt the movement of the contamination plume by allowing the shrub community to extract more water than is recharged in the aquifer.

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