About

Kamel Didan is a professor in the Department of Biosystems Engineering at the University of Arizona. His office is located in Shantz 501A, and he can be reached by phone at 520-621-8514 or via email at didan@arizona.edu. His research and teaching focus on biosystems engineering, with particular interests in precision agriculture, bioenergy, and bioproducts. He is associated with the Biosystems Engineering department and is involved in various academic and research activities related to sustainable agricultural systems and innovative biotechnologies.

Research topics

• Ecology
• Geography
• Environmental science
• Geology
• Remote sensing
• Computer Science
• Physical geography
• Meteorology
• Physics
• Climatology
• Telecommunications
• Biology

Selected publications

• Restoration Efforts in Riparian Ecosystems in the Colorado River Delta as Measured by Greenness Indices and Evapotranspiration (ET) and using Hydrology, Avian Studies and ET Change Maps 

  2025-03-14

  preprintOpen accessSenior author

  Colorado River water has been allocated through recent Minutes (319 from 2014-2017; 323 from 2018-2026) to the 1944 Water Treaty between the United States and Mexico to support efforts to restore native riparian forests, which provide essential habitat for migratory birds, in the Colorado River delta. Our study was largely conducted in the context of assessing the effects of restoration efforts on riparian corridor health. We processed and analyzed remotely sensed data from 2000 to 2023 to assess large-scale dynamics of vegetation health by measuring satellite vegetation index (VI, a proxy for canopy greenness) and plant water use (actual evapotranspiration, ETa) in the riparian corridor.Under Minute 323, water deliveries are used primarily to irrigate managed restoration areas. Our study reports the outcomes of restoration actions on variables such as vegetation extent and density through two-band Enhanced Vegetation Index (EVI2) measurements and hydrological processes including ETa. We integrated EVI2 with potential ET from two sources, the Yuma Valley Arizona Meteorological Station “AZMET” ground station and gridded Daymet, to calculate ETa. We quantify ETa in restoration sites compared to the unrestored reaches from 2000-2023. Our findings showed an average increase of 42% in EVI2, an indication of land cover greenness, within the restoration sites in the decade since 2014, when efforts by many non-government organizations collaborated to improve the riparian corridors, with one large effort in Reach 2 and a dozen smaller sites in Reach 4. Conversely, greenness in adjacent, unrestored areas in these reaches declined by 27%. The study also indicates a 22% increase in ETa in the restored areas, compared to a 31% reduction in the unrestored regions. Restored sites in Reach 4, which contains a dozen restoration areas, experienced ETa increases ranging from 9-12%, whereas their unrestored counterparts show a decline of 21%. Restoration efforts focusing on small plots have successfully revitalized habitat, the motivation for this research.Measurements of VIs and ETa several years after the Minute 323 federal flows were delivered in 2020 and 2021 to the riparian corridor, including to restoration sites in Reaches 2 and 4, do not show any boost to the greenness and ETa in the unrestored riparian reaches in the delta after these federal flows were delivered. However, further downstream, in Reaches 5 and 7, the non-native shrub saltcedar (Tamarisk spp.) has been repeatedly defoliated by saltcedar beetles (Diorhabda spp.). Select regions of these defoliated shrubs in Reaches 5 and 7 were measured using Landsat time series data from 2000-2023 using peak growing season dates of May 1 through October 30. The measured change between the ETa in the first five years (2000-2004), with a mean of 737 mm/year, and latter five years (2019-2023), with a mean of 599 mm/year, showed a decrease of 138 mm/year in ETa, which is a decrease in ETa of 18.7%. Despite the challenges posed by small water deliveries and beetle defoliation for non-native saltcedar shrubs, restoration efforts focusing on small plots have successfully revitalized habitat, the motivation for this research.

  PublisherDOI

• Deep Learning-Driven Weed Detection in Lettuce Farms: Box Annotation and Post-Segmentation

  Preprints.org · 2025-06-09

  preprintOpen access

  Weed infestations cause billions of dollars in annual loss and devastate natural habitats. Current weed recognition methods remain vulnerable to seasonal and environmental variations, and their performance relies on tedious manual curation. To address these limitations, we proposed a straightforward framework that combined pre-trained deep learning models (including transformers) with simple box annotations and Segment Anything Model (SAM) for precise postprocessing boundary delineation. We evaluated this approach by comparing the state-of-the-art Faster R-CNN (Region-based Convolutional Neural Network) against the pioneering transformer-based DETR on lettuce-farm imagery. Of 939 annotated images, 760 (≈81%) were used for training, 92 (≈10%) for validation, and the remaining 87 (≈9%) reserved for independent testing. Faster R-CNN achieved an overall F1 score of 95.0%—97.5% for lettuce and 92.5% for weeds—while DETR achieved 87.1% overall, with 88.1% for lettuce and 86.1% for weeds. In both models, SAM achieved near-perfect segmentation—even for overlapping or closely spaced objects—by focusing on a single object per bounding box. This research not only automates weed detection to boost lettuce yield, but also enables targeted weeding application, reducing the treatment cost and environmental impact.

  PublisherOA PDFDOI

• A hydrologic engineering analysis of a failed rangeland water control structure on the Buenos Aires national wildlife refuge

  Brazilian Journal of Animal and Environmental Research · 2025-02-18

  articleOpen accessSenior author

  This report summarizes a hydrologic engineering analysis of a failed concrete drop spillway (broad-crested weir) water control structure at the outlet or “pour point” on the Buenos Aires National Wildlife Refuge (BANWR) watershed southwest of Tucson, AZ. The main objective was to determine the maximum flow the structure could pass without failure (discharge capacity) as well as the rainfall recurrence interval and duration which would result in a flood of a magnitude which would exceed the capacity of the spillway and thus likely lead to failure of the structure. The layout, function, and characterization of the watershed was established using modern software programs and available imagery. The discharge capacity of the concrete drop spillway was found to be 21.1 [m3s-1]. After evaluating results obtained from the Rational Method and the Curve Number (CN) Method (with assumptions of closed upper-watershed gates and stock pond retention having no effect), the spillway capacity was adequate to withhold runoff volumes generated from 10-yr to 25-yr recurrence interval rainfalls of variable durations and intensities provided the spatial extent of rainfall was limited to one of the two small sub-watersheds (Sub-watersheds A and B). However, if rainfall occurred over the entire watershed or Sub-watershed C then the spillway capacity was exceeded by runoff volumes generated for 10-yr and 25-yr recurrence interval rainfalls of all durations as well as all generated design storms of greater magnitude and intensity.

  PublisherOA PDFDOI

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

  Restoration Ecology · 2024-07-04 · 2 citations

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• 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

  Remote Sensing · 2024-05-18 · 1 citations

  articleOpen accessSenior author

  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).

  PublisherOA PDFDOI

• QUANTITATIVE TRENDS OF ACTUAL EVAPOTRANSPIRATION IN THE UPPER SAN PEDRO RIVER RIPARIAN CORRIDOR

  Abstracts with programs - Geological Society of America · 2024-01-01

  articleSenior author
  PublisherDOI

• Saguaro Recognition from Drone Imagery Using Mask R-CNN in Detectron2

  Preprints.org · 2024-10-03

  preprintOpen access

  The saguaro cactus (Carnegiea gigantea) plays a pivotal role in desert ecosystems, making its population monitoring essential. Traditional census methods used by the United States Forestry Service are resource-intensive, prompting a need for more cost-effective alternatives. Automated detection methods using advanced object detection models applied to drone imagery present a promising solution. In a proof-of-concept study, 244 drone images of saguaros were captured from a top-down perspective over an undeveloped hill adjacent to Sun Ray Park, Phoenix, Arizona (33.3188° N, 111.9980° W), from altitudes of 486, 507, and 519 meters above mean sea level (AMSL). We employed the Mask R-CNN model from the Detectron2 framework for model training. The images were divided into training, validation, and test sets in an approximate 8:1:1 ratio, with the sets separated by their physical locations within the park: training data was centralized, validation data was positioned to the east, and test data was located in the west. The Mask R-CNN achieved an average precision of 89.8% and an average F1 score of 90.3% in identifying saguaros across 27 test images from 486/507 m AMSL, demonstrating the model's effectiveness in accurately identifying saguaro cacti. Despite the limited sample size, the model's adaptability to diverse scenarios underscores its potential for practical applications in ecological conservation. This research contributes to the field of automated monitoring by offering a viable alternative to labor-intensive methods, thus supporting the sustainability of saguaro in their native habitat.

  PublisherOA PDFDOI

• Riparian Corridors of the Sonoran Desert: New Estimates of Riparian Evapotranspiration Change Using Daymet and Landsat Vegetation Index Methods

  2024-03-08

  preprintOpen accessSenior author

  Accurate estimates of riparian vegetation water use are import-ant to quantify, particularly in arid environments. In these narrow riparian corridors, we quantify loss of water from leaves and soil as one variable, actual evapotranspiration (ETa). ETa is one of the most difficult components of the water cycle to measure, but our remote sensing estimates of ETa have been validated for dryland riparian corridor species using ground-based sensors (e.g., sap flow, tower). Increases in ETa are indicative of increasing vegetation cover and therefore increasing ‘losses’ of water through ETa represent positive trends in riparian ecosystem health; decreasing ETa may indicate dwindling riparian cover due to less available water for canopy growth due to drought, groundwater flux, beetle defoliation, fire, increasing salinity.The objective of this study was to calculate actual annual ETa (mmyr-1) for selected riparian areas in the Sonoran Desert in the southwestern U.S. Riparian reaches for a dozen rivers in the Lower Colorado River Basin, mostly in Arizona, were delineated and monitored using the two-band Enhanced Vegetation Index (EVI2). We acquired 30-m resolution Landsat scenes, processed and performed a pixel-wise quality assessment to remove pixels with high aerosols and clouds, and computed EVI2 every 16-days over 20 years. We then computed daily potential ET using the Blaney-Criddle formula with input temperature data from gridded weather data using Daymet (1 km). Riparian ETa was quantified using the Nagler ET(EVI2) model to produce time-series data for the period 2000-2021.From 2000 to 2021, various rivers were studied to determine the average annual ET(EVI2) (mmyr-1) for riparian corridors, unrestored areas, and restored areas. The findings indicate that the Salt River experienced a 13.7% increase from 800 mmyr-1 to 910 mmyr-1, whereas the Gila River only saw a 2.7% increase from 725 mmyr-1 to 745 mmyr-1 during the same period, with occasional periods of decreases (e.g., 2002, 2013) followed by increases. The San Pedro increased 7.4%. The Santa Cruz River showed the most significant increase in average annual ET(EVI2) with a 24.0% increase from 770 mmyr-1 to 955 mmyr-1 (2000-2021). The increasing trends on these rivers could be due to riparian species composition altered by the tamarisk beetle followed by secondary or replacement species which established green canopies, restoration efforts or other changes in water or land management. This study provides valuable estimates of riparian water use that may assist with decision-making by natural resource managers tasked with allocating water and managing habitat along these riparian corridors. Our findings have continued to be used to assist managers with decision-making for ecological restoration success. These data, tools, methods, and results can be utilized by decision makers in their quest to mitigate and understand how declines of riparian ecosystems can be slowed or possibly reversed.

  PublisherDOI

• Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

  Remote Sensing · 2023 · 26 citations

  • Environmental science
  • Remote sensing
  • Geography

  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-NDVIKc) 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-NDVIKc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVIKc 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.

  PublisherOA PDFDOI

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

  European Journal of Remote Sensing · 2023-09-25 · 12 citations

  articleOpen access

  ABSTRACTNumerous 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.

  PublisherDOI

Frequent coauthors

• Pamela L. Nagler

  Southwest Biological Science Center

  61 shared

• Armando Barreto‐Muñoz

  51 shared

• Sattar Chavoshi Borujeni

  31 shared

• Hamideh Nouri

  Australian Government

  19 shared

• Tomoaki Miura

  University of Hawaiʻi at Mānoa

  19 shared

• Alfredo Huete

  University of Technology Sydney

  18 shared

• Christopher J. Jarchow

  University of Arizona

  18 shared

• Willem van Leeuwen

  University of Arizona

  12 shared

Labs

• Terrestrial Biophysics and Remote Sensing LabPI