Lab
Remote Sensing for Ecology and Ecosystem Conservation (RSEEC)
Institution: Khaje Nasir Toosi University of Technology
Department: Faculty of Geodesy and Geomatics Engineering
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 (6)
Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.
The ongoing impacts of climatic changes, coupled with intensified human activities, are leading to a significant loss of plant diversity, prompting urgent calls for comprehensive monitoring of forest ecosystems. This is particularly concerning in regions with the encroachment of human activities into forest zones, and inadequate regulations that primarily view forests as a source of timber without fully accounting for their critical roles in biodiversity. These issues highlight the need for effective geospatial approaches to support conservation strategies and sustained monitoring of plant diversity. The majority of remote sensing studies of plant diversity have focused on alpha-diversity, while beta-diversity plays an important role in providing a comprehensive picture of biodiversity patterns across scales. Among various remote sensing proxies, Rao's Q index stands out as a reliable metric to quantify beta-diversity using multispectral remote sensing indices. However, a holistic approach that can incorporate multiple remote sensing indices and enable comparative analysis using graphical means with computational efficiency is lacking. In response, we developed PaRaVis, an open-source Python-based graphical package for deriving spectral diversity from multispectral remote sensing datasets as a proxy for functional diversity using Rao's Q index. This tool encompasses all the necessary steps for parallelized computation, visualization, and analysis of Rao's index from either single or multiple multispectral indices. It is capable of calculating and visualizing 75 vegetation indices (VIs), from a raster scene, followed by calculating Rao's Q both unidimensionally and multidimensionally in parallel. PaRaVis also offers features for visualizing, analyzing, and comparing Rao's Q outputs using statistical performance diagnostics. It provides a unique means to infer diversity patterns in space and time and is therefore invaluable for researchers or organizations involved with plant diversity monitoring, especially those with limited data analysis or computer programming experience. To demonstrate the tool's effectiveness, we analyzed plant diversity patterns using satellite-derived spectral heterogeneity measures in forest sites within the Hyrcanian Forests of Iran and sites located in Germany. The first case study focused on UNESCO Natural Heritage sites. Our analysis revealed that employing EVI, SR3, and TCI indices as inputs for the multidimensional Rao's Q yielded higher performance in monitoring more heterogeneous forests compared to the unidimensional mode. In the second case study, we utilized field Species Richness and Shannon-Wiener diversity indices to evaluate PaRaVis and our method. We found that using a multi-temporal, multi-index approach enhances the results compared to multi-seasonal and classical Rao based on single time.
Climate change is one of the main factors that caused scarcity of fresh water phenomenon all over the world. The lack of water in major parts of Iraq affected all sectors that use water and cause obvious damages to ecosystems. Karbala province suffers from frequent water scarcity due to water scarcity and abnormally high temperatures. In the present study, remote sensing and GIS were applied to quantify water scarcity and evaluate its effects on vegetation in this fragile semiarid ecosystem. Analysis of hydrological data of the study area was carried out during 2013 to 2022 to compute water availability and shortage based on the criteria and requirements of water sector and environmental management in Iraq. Remotely sensed Landsat 8 images data were applied to measure changes on vegetation and the effects of water scarcity. Soil Adjusted Vegetation Index (SAVI) was employed to identify vegetation and detect its change. Results showed that the area witnessed decreasing in water availability compared to the reference year. Maximum available water reached 1977.535 million
in 2013, while the minimum of 859.227 million was observed in 2022. The maximum and minimum vegetation area reached 535.610
and 430.605
in 2013 and 2022, respectively. Results indicated that all the years posterior to the reference year experienced water scarcity and vegetation damage, where the maximum and minimum water scarcity rates were 56% and 8% in 2022 and 2016, respectively. The maximum impact of water scarcity rate on vegetation was ca. 20% in both years 2015 and 2022. Water scarcity is constantly increasing over time, thus evaluating its impacts and forecasting its future specification will support decision-makers to take the necessary measures to mitigate its effects.
This study aims to assess the spatio-temporal defoliation dynamics of box tree, one of the few evergreen species of the Hyrcanian Forests. For this, we integrated multi-temporal leaf-off optical Sentinel-2 and radar Sentinel-1 data from 2017 to 2021 with elevation data. A state-of-the-art sample migration approach was used to generate annual reference samples of two categories (defoliated and healthy box tree) for a set of target years 2017-2020. This approach is based on field samples of the reference year 2021 and two similarity measures, the Euclidean distance and the spectral angle distance. The analysis of spectral and radar profiles showed that the migrated samples were well representative of both defoliated and healthy box trees categories. The migrated samples were then used for spatially mapping the two classes using support vector machine classification. The results of support vector machine classification indicated a large extent of box tree mortality. The most significant changes from healthy box trees to defoliated ones, or vice versa, occurred during the years 2017 and 2018. In the consecutive years of 2019, 2020, and 2021, no significant changes in the distribution of healthy or defoliated box trees were observed. The statistical assessment also revealed that mortality of evergreen understory tree species can be mapped with practically sufficient overall accuracies reaching from 84% (in 2017) to 91%-92% (in 2020 and 2021) using spaceborne remote sensing data. This information using freely accessible satellite data can benefit forest managers responsible for monitoring landscapes affected by the box moth and facilitates the identification of optimal control programs.
Remote sensing-assisted monitoring of forest health entails methods that can provide up-to-date and accurate information on decline and mortality of individual trees, while maintaining time and cost efficiency. However, the trade-off of applying consumer-grade UAV-RGB data as the most affordable and accessible data source at the catchment level is constrained by its poor spectral information content. We developed a method based on the fusion of UAV-RGB data with space-borne Sentinel-2 Multispectral Instrument (MSI) at the level of tree crowns, with the specific target of supporting studies on semi-arid tree decline. We applied linear spectral unmixing (Spectral Unmixing-Based data Fusion method, LSUBF) by considering a limited number of endmem-bers and calculating the abundances (fractional covers) from the UAV data, and evaluated the results by high-resolution MSI space-borne data including SPOT-6 (1.5 m spatial resolution) and PlanetScope (3 m spatial resolution). This method suggested an increase in the coefficient of determination of the applied generalized additive model for decline severity estimation at tree crown level from 0.61 to 0.69, while it was improved from 0.70 to 0.91 when fitting a non-parametric random forest model. The results of sensitivity analysis demonstrated that the additional spectral information obtained from the proposed method results in higher accuracy in estimating decline severity. We suggest this method as a cost-effective alternative to monitor periodical tree decline, in particular across semi-arid ecosystems.
Lab head
Department
- 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).