Research Lab: Remote Sensing for Ecology and Ecosystem Conservation (RSEEC)

About the lab

The main focus in my lab is the development of application-oriented research on digital photogrammetry- and remote sensing-assisted analysis of ecological indicators by means of terrestrial, airborne and spaceborne data sources. Besides, I put considerable efforts on educational and research projects for efficient combination of field- and remote methods for ecosystem invetory as well as biodiversity and lanudse monitoring. My students are aimed to form the next generation of practice-oriented photogrammetry and remote sensing experts specielized for protecting the natural environment .

Featured research (35)

Regardless of the regeneration origin, forest classification by tree growth form (multi-stem vs. single-stem) is essential for forest resource assessment in arid and semi-arid ecosystems, as they relate to a wide variety of ecosystem services like aboveground biomass and applications like forest decline monitoring. However, studies on UAV photogrammetry based discrimination of tree growth forms are generally scarce, in particular across semi-arid woody vegetation. Here, we studied the potential of UAV-RGB data captured by consumer-grade UAVs for classification of multi-stem with standards vs. single-stem in semi-arid Zagros forests of Iran. We addressed 1) whether multi-stem and single-stem trees can be distinguished using cost-effective UAV-RGB data, 2) the most efficient UAV-RGB features and 3) the environmental properties that influence the classification potential of UAV-RGB features. Beside a range of spatial and textural UAV features, we particularly suggest the eccentricity as an additional indicator of multi-stem tree structures to be applied in future relevant studies. Our results show that UAV-RGB provides support information to classify multi-stem vs. single-stem for trees with dense crown over sites with gentle slopes.
1. Remote sensing (RS) and geospatial sciences already amount to a long history of fostering research in topics related to ecology. Data and methods have mainly been subject to research and experiments, but trends are now emerging that suggest the use of RS in practical applications like nationwide monitoring programs and assisting global conservation goals. However, use of active remote sensing for ecological and conservation is in its infancy, and the implications of active sensor data, including light detection and ranging and radio detection and ranging that mostly deliver three-dimensional (3D) information, are still relatively primitive and have largely been limited to indirect use of their extracted proxies for ecological modelling. 2. This cross-journal special feature between Methods in Ecology and Evolution, Journal of Animal Ecology, Journal of Applied Ecology and Journal of Ecology includes 18 papers that include full research papers, reviews and technical applications. They are mostly novel in either or both their interpretation of proxies derived from active RS data and the direct usage of 3D RS techniques (terrestrial, airborne , UAV borne and spaceborne) to address ecological topics. 3. We categorized the published contributions into the following thematic groups, with some degree of overlap: (i) ecosystem structural analysis by active data (nine studies); (ii) response of animal populations to climate dynamics as shown by active data; (iii) interactive effects of forest structure and wildlife monitoring (five studies); (iv) forest inventories assisted by active data (one study) and (v) tree type classification by active data (one study). 4. Synthesis. The studies in this Special Feature and trends shown by other recent works at the interface of ecology and active RS confirm the ongoing shift from indirect and solely proxy-based approaches to direct and more data-science driven methods in approaching ecology and conservation problems by means of active sensors. Relatively affordable and accessible drone and citizen science-based on-demand active RS data acquisition are becoming common practice, and the future of sensor development is hypothesized to go beyond the current domination of very high spatial resolution data and towards multiple spaceborne platforms. These tools and methods will support spatial upscaling, uncertainty analysis, large-scale
Tree decline is a highly complex process and is inherently a function of manifold climatic, physiologic, and anthropogenic factors. Monitoring decline processes and their underlying dynamics primarily entails identifying their location and intensity across different ecosystems, for which airborne and satellite remote sensing approaches offer cost-effective and spatially explicit alternatives to field methods. Consumer-grade unmanned aerial vehicles (UAVs) can barely be used as standalone means for large-area monitoring due to their constrains in spatial and spectral domains. However, they could effectively be integrated alongside satellite data to unlock their information for subsequent upscaling on landscape level. We designed a novel two-step workflow to describe the severity of tree decline by linking UAV-RGB information to space-borne multispec-tral and digital elevation model (DEM) data over 15 forest sites dominated by Persian oak across the latitudinal gradient of Zagros Forests in western Iran. We display how to 1) leverage UAV as reference data across multiple structurally different Persian oak-dominated sites in semi-arid Zagros mountains of Iran; 2) link UAV, Copernicus DEM, and Sentinel-2 data to retrieve decline information within a model-driven context; and 3) analyze the sensitivity of models by means of a global variance-based sensitivity analysis. Results suggested a high association between UAV and field data on the intensity of decline, which enabled using sampled UAV data as reference to estimate the decline severity using space-borne data by means of semi-parametric generalized additive model (GAM) and non-parametric random forest (RF) approaches. Conclusively, this study provided a baseline for multi-scale analysis of tree decline using budget and partially free data sources, which can be of high scientific and practical assets for monitoring in remote, sparse, mountainous, and continuously degrading forest areas.
The Hyrcanian Forests comprise a continuous 800-km belt of mostly deciduous broadleaf forests and are considered as Iran’s most important vegetation region in terms of density, canopy cover and species diversity. One of the few evergreen species of the Hyrcanian Forests is the box tree (Buxus), which is seriously threatened by box blight disease and box tree moth outbreaks. Therefore, information on the spatial distribution of intact and infested box trees is essential for recovery monitoring, control treatment and management. To address this critical knowledge gap, we integrated a genetic algorithm (GA) with a support vector machine (SVM) ensemble classification based on the combination of leaf-off optical Sentinel-2 and radar Sentinel-1 data to map the spatial distribution of box tree mortality. We additionally considered the overstorey species composition to account for a potential impact of overstory stand composition on the spectral signature of understorey defoliation. We consequently defined target classes based on the combination of dominant overstorey trees (using two measures including the relative frequency and the diameter at breast height) and two defoliation levels of box trees (including dead and healthy box trees). Our classification workflow applied a GA to simultaneously derive optimal vegetation indices (VIs) and tuning parameters of the SVM. Then the distribution of box tree defoliation was mapped by an SVM ensemble with bagging using GA-optimized VIs and radar data. The GA results revealed that normalized difference vegetation index, red edge normalized difference vegetation index and green normalized difference vegetation index were appropriate for box tree defoliation mapping. An additional comparison of GA-SVM (using GA-optimized VIs and tuning parameters) with a simple SVM (using all VIs and user-based tuning parameters) showed that our suggested workflow performs notably better than the simple SVM (overall accuracy of 0.79 vs 0.74). Incorporating Sentinel-1 data to GA-SVM, marginally improved the performance of the model (overall accuracy: 0.80). The SVM ensemble model using Sentinel-2 and -1 data yielded high accuracies and low uncertainties in mapping of box tree defoliation. The results showed that infested box trees were mostly located at low elevations, low slope and facing north. We conclude that mortality of evergreen understorey tree species can be mapped with good accuracies using freely available satellite data if a suitable work-flow is applied.
Forest measurement in semi-arid ecosystems and protective stands like Zagros forests is essential for collecting primary information as well as those related to biometrical, ecological and conservational aspects. While the access, time, logistics, financial and lack of trained staff hamper the timely and frequent implementation of forest inventory projects in Zagros, using data and methods based on consumer-grade unmanned aerial vehicles will be discussed here as an alternative or supplement to conventional forest inventories. The challenges and opportunities related to such UAV data in its basic configuration (nadir flight, RGB image composites images and fixed gimbal) will be briefly reviewed and discussed in connection with the basic primary and secondary variables of forest inventory in Zagros. The findings and suggestions here are aimed to facilitate the decision-making of practical sector managers and researchers within the Zagros region.

Lab head

Hooman Latifi
  • Faculty of Geodesy and Geomatics Engineering
About Hooman Latifi
  • I am an Associate Professor of Ecological Remote Sensing with the primary research interest in spatiotemporal analysis of forest structure, biodiversity and health by means of spaceborne and airborne data sources. Besides my teaching and research activities, I am an Associate Editor for Methods in Ecology and Evolution (Wiley) and Forestry (Oxford University Press).

Members (5)

Seyed Arvin Fakhri
  • Khaje Nasir Toosi University of Technology
Marziye Ghasemi Mobaraki
  • Khaje Nasir Toosi University of Technology
Dan Kanmegne
  • University of Wuerzburg
Faez Hussein
  • Universiti Putra Malaysia - K. N. Toosi University of Technology.
Mohammadreza Fathi
  • Khaje Nasir Toosi University of Technology
Elham rezagholi
Elham rezagholi
  • Not confirmed yet

Alumni (13)

Siddhartha Khare
  • Indian Institute of Technology Roorkee
Raja Ram Aryal
  • The University of Queensland
Omid Karami
  • Sari Agricultural Sciences and Natural Resources University
Steven Hill
  • University of Wuerzburg