About

Curtis E. Woodcock is a Professor in the Department of Earth & Environment at Boston University. He holds a Ph.D. from the University of California, Santa Barbara, obtained in 1986. His research focuses on monitoring forest change and its implications for terrestrial carbon budgets, as well as urbanization as a component of global change. He investigates the influence of forest canopy structure on canopy gap structure and radiation transmission, and works on the validation of terrestrial remote sensing products. Professor Woodcock teaches courses including EE 444/644 Digital Image Processing-Remote Sensing and EE 501 Advanced Topics in Remote Sensing. His professional contact information includes his office at CAS 436A, email at curtis@bu.edu, and phone number 617-353-5746.

Research topics

• Computer Science
• Geography
• Environmental science
• Remote sensing
• Ecology
• Agroforestry
• Geology
• Forestry
• Mathematics
• Statistics
• Environmental resource management

Selected publications

• Global Land cover Mapping and Estimation (GLanCE): a record of land cover and land cover change at Landsat scale across decades

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Detection of new construction activities with Sentinel-1 and Landsat time series

  Remote Sensing Applications Society and Environment · 2026-01-01

  articleSenior author
  PublisherDOI

• Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina

  UNC Libraries · 2026-02-17

  articleOpen access

  Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R2 and RMSE values across models: KNN (R2 of 0.496, RMSE of 5.13 m), RF (R2 of 0.510, RMSE of 5.06 m), SVM (R2 of 0.544, RMSE of 4.88 m), and XGB (R2 of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies.

  PublisherDOI

• Drivers of forest disturbance in Southeast Asia

  International Journal of Applied Earth Observation and Geoinformation · 2026-03-07

  articleOpen access

  • We mapped and analyzed forest disturbance drivers in the entire Southeast Asia. • We applied a Landsat time series method, CCDC-SMA, combined with auxiliary data. • We estimated the area of different drivers over time. • The margins of error of our area estimates for all classes are all less than 26%. • New plantation and shifting cultivation have the largest areas among the drivers. Although Southeast Asia is a hotspot of forest disturbance, there is a lack of fine-resolution and long-term analysis of drivers of forest disturbance in Southeast Asia. Here, we present a study of mapping and analyzing drivers of forest disturbance in Southeast Asia at 30-meter resolution from 2000 to 2021. We used a combination of Continuous Change Detection and Classification − Spectral Mixture Analysis (CCDC-SMA), object-based image analysis, and land cover classification to attribute the drivers of forest disturbances. The overall accuracy of the map of drivers of forest disturbance reached 76.3% (77% before area-adjusted estimates). The margins of error of our area estimates for all classes are all less than 26%. We found that shifting cultivation affected 231,927 ± 45,009 km 2 , which accounted for 4.7% ± 0.9% of Southeast Asia. The area of the new plantation is 186,457 ± 40,487 km 2 (3.8% ± 0.8% of Southeast Asia). Deforestation accounted for 3.0% ± 0.8% of Southeast Asia, equivalent to 148,511 ± 37,547 km 2 . Shifting cultivation has the lowest uncertainty among the change classes. Shifting cultivation mainly occurs in Myanmar, Laos, and Indonesia. New plantations mainly occur in Indonesia, Malaysia, and Myanmar. Deforestation mainly occurs in Indonesia, Cambodia, and Laos. New plantations increased significantly in 2006–2010 compared to 2001–2005, and then decreased after 2010. The area of deforestation decreased slightly from 2001 to 2010 and increased slightly from 2010 to 2020. Shifting cultivation has a moderately increasing trend over time. Our study provides important information on drivers of forest disturbance in Southeast Asia, which helps to improve decision-making for forest conservation and land management.

  PublisherDOI

• The next Landsat: Mission turning point?

  Remote Sensing of Environment · 2025-10-16 · 3 citations

  articleOpen access

  For over fifty years, the Landsat satellite series has provided continuous and comprehensive data for monitoring changes on the Earth's terrestrial surface. Eight successive missions, carrying progressively more sophisticated sensors, with improved radiometric, geometric, and spatial characteristics, have provided an unbroken series of optical and thermal imagery, unparalleled globally. With limited lifetimes for each Landsat satellite, planning of each mission typically overlaps to ensure continuity. Commencing in 2021, planning of a Landsat-9 successor gathered user needs from across the Earth Observation (EO) community, resulting in the Landsat Next (LNext) mission design of three sun-synchronous satellites to acquire reflective and thermal wavelength observations with two to three times the temporal, spatial, and spectral resolution of previous missions. Proposed 2026 U.S. budgets have significantly reduced NASA Earth Science funding. Alternate architectures are now being investigated for Landsat Next that would only meet Landsat-9 design requirements. While this would provide observation continuity, this implies a revised Landsat Next program launched in the early 2030s with nearly 30 year old capabilities, that may acquire data with lower radiometric quality than the current on-orbit Landsat-8 and 9 missions, and that will not support the new capabilities advocated for by the EO user community. This correspondence serves to raise community awareness that the decision is pending, and outlines the observation requirements originally envisioned for LNext and how they were derived to provide context for evaluating the restructured and descoped capability now being considered. • Landsat Next design: twenty-one 10/20-m VSWIR and five 60-m TIR bands. • Three-satellite constellation, six-day global land surface revisit. • Derivation of Landsat Next observation requirements for science and applications. • Descoped mission will not meet future user needs. • Descoped mission risks less, and lower quality, data than current Landsats.

  PublisherDOI

• Time Series Analysis of Landsat and Sar Data Reveals Loss of Native Forest in Ghana is High, But Lower than Prior Indicators

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Detecting utility-scale solar installations and associated land cover changes using spatiotemporal segmentation of Landsat imagery

  Science of Remote Sensing · 2025-11-20 · 2 citations

  articleOpen accessSenior author

  As utility-scale solar development expands across temperate forest regions, concerns have emerged over its unintended environmental impacts, particularly land-use and land-cover change (LULCC) and associated impacts on carbon dynamics. This study develops and tests an object-based change detection framework that integrates Continuous Change Detection and Classification (CCDC) with Simple Non-Iterative Clustering (SNIC) to map utility-scale solar development and related forest disturbances in Massachusetts from 2005 to 2024. By combining temporal and spatial segmentation, this method maps the spatiotemporal footprint of utility-scale solar installations with associated deforestation and post-clearing dynamics. Our results show approximately 51% of utility-scale solar installations occurred on previously forested land, with an additional 64% of surrounding forest cleared relative to the directly deforested area. On average, each hectare of new solar development on forest land was associated with 1.66 hectares of deforestation between 2005 and 2024. Temporal trends from CCDC models quantify post-clearing vegetation dynamics and microclimatic effects. Exploration of the microclimatic effects of solar installations using time series method indicates Land Surface Temperature (LST) increases exceeding +7.2 °C in solar developments with deforestation and Albedo reductions after solar panel installations, suggesting substantial shifts in site-level biophysical conditions. Normalized Difference Vegetation Index (NDVI) time series showed a positive slope following solar installation, indicating gradual vegetation regrowth. The results indicate that the newly developed CCDC-SNIC was successfully applied to Landsat time series for detecting utility-scale solar installations and their associated LULCC. This method can provide a foundation for integrating LULCC data with carbon modeling to quantify the net climate impact of utility-scale solar. • A spatiotemporal segmentation of Landsat time series for utility-scale solar • 51% of solar developments in Massachusetts were built on forested land • An additional 64% of surrounding forest was cleared for solar developments • Solar projects with deforestation increased local land surface temperature • NDVI trends revealed gradual vegetation regrowth after solar installation

  PublisherDOI

• Detection of New Construction Activities with Sentinel-1 and Landsat Time Series

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The next Landsat: Mission turning point?

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina

  Remote Sensing · 2025-04-25 · 4 citations

  articleOpen access

  Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R2 and RMSE values across models: KNN (R2 of 0.496, RMSE of 5.13 m), RF (R2 of 0.510, RMSE of 5.06 m), SVM (R2 of 0.544, RMSE of 4.88 m), and XGB (R2 of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies.

  PublisherOA PDFDOI

Frequent coauthors

• Alan H. Strahler

  Boston University

  120 shared

• Pontus Olofsson

  Marshall Space Flight Center

  71 shared

• Zhe Zhu

  Shandong Academy of Sciences

  62 shared

• Crystal Schaaf

  51 shared

• Sucharita Gopal

  Boston University

  44 shared

• Wenge Ni‐Meister

  City University of New York

  37 shared

• David L.B. Jupp

  Commonwealth Scientific and Industrial Research Organisation

  37 shared

• M. A. Friedl

  Boston University

  35 shared

Education

• Ph.D.

  University of California, Santa Barbara

  1986