Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects.

Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.

Sea level rise can severely reduce biodiversity and community net production on rocky shores.

Sea level rise (SLR), driven by anthropogenic climate change, can be a major threat to coastal ecosystems. Among the most biologically diverse but SLR-threatened coastal ecosystems are rocky shores, especially in regions with a small tidal range. Nonetheless, the impacts of SLR on rocky shore biodiversity, community structure and ecosystem functions have rarely been studied. Here, we use the biogenic intertidal ecosystem, Mediterranean vermetid reefs on the Israeli coast, as case study for testing the potential impact of SLR on reef communities, with surveys, 3D topographic mapping plus SLR simulations, and a manipulative community translocation experiment. We show that: (1) biodiversity is much lower on very shallow, permanently submerged, horizontal rocky surfaces compared to that on intertidal reef platforms, (2) the extensive intertidal platforms will permanently drown under even modest SLR scenarios, (3) the rich intertidal community will transform, when permanently submerged, either to a very different but still rich community when protected from grazing by highly abundant invasive fish (rabbitfish), or to a much poorer turf community when exposed to such fish grazing, and (4) the reef community net production will drastically drop under permanent submersion. Because the main ecosystem engineer of the vermetid reefs, Dendropoma anguliferum (Monterosato, 1878), is nearly extinct in the southeast Levant, it is unlikely that new reefs will be formed higher on the shore in the future, presumably resulting in extensive coastal ecological shifts. Considerable coastal community shifts are forecasted for many regions globally due to SLR, as many shorelines are predicted to suffer from "coastal squeeze". Hence, similar manipulative experiments are encouraged in other regions to test for generality vs. context dependency in SLR ecological impacts. We suggest that in cases where essential/unique intertidal habitats like vermetid reefs are expected to vanish by SLR, constructing carefully-planned, ecologically friendly, artificial alternatives should be considered.

3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism.

Effective control of the parasitic weed sunflower broomrape (Orobanche cumana Wallr.) can be achieved by herbicides application in early parasitism stages. However, the growing environmental concerns associated with herbicide treatments have motivated the adoption of precise chemical control approaches that detect and treat infested areas exclusively. The main challenge in developing such control practices for O. cumana lies in the fact that most of its life-cycle occurs in the soil sub-surface and by the time shoots emerge and become observable, the damage to the crop is irreversible. This paper approaches early O. cumana detection by hypothesizing that its parasitism already impacts the host plant morphology at the sub-soil surface developmental stage. To validate this hypothesis, O. cumana- infested sunflower and non-infested control plants were grown in pots and imaged weekly over 45-day period. Three-dimensional plant models were reconstructed using image-based multi-view stereo followed by derivation of their morphological parameters, down to the organ-level. Among the parameters estimated, height and first internode length were the earliest definitive indicators of infection. Furthermore, the detection timing of both parameters was early enough for herbicide post-emergence application. Considering the fact that 3-D morphological modeling is nondestructive, is based on commercially available RGB sensors and can be used under natural illumination; this approach holds potential contribution for site specific pre-emergence managements of parasitic weeds and as a phenotyping tool in O. cumana resistant sunflower breeding projects.

Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification.

BACKGROUND: Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. RESULTS: We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. CONCLUSION: The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models.