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The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing––reaching an accuracy above to 90%––in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection.

Unmanned aerial systems (UAS) carrying commercially sold multispectral sensors equipped with a sunshine sensor, such as Parrot Sequoia, enable mapping of vegetation at high spatial resolution with a large degree of flexibility in planning data collection. It is, however, a challenge to perform radiometric correction of the images to create reflectance maps (orthomosaics with surface reflectance) and to compute vegetation indices with sufficient accuracy to enable comparisons between data collected at different times and locations. Studies have compared different radiometric correction methods applied to the Sequoia camera, but there is no consensus about a standard method that provides consistent results for all spectral bands and for different flight conditions. In this study, we perform experiments to assess the accuracy of the Parrot Sequoia camera and sunshine sensor to get an indication if the quality of the data collected is sufficient to create accurate reflectance maps. In addition, we study if there is an influence of the atmosphere on the images and suggest a workflow to collect and process images to create a reflectance map. The main findings are that the sensitivity of the camera is influenced by camera temperature and that the atmosphere influences the images. Hence, we suggest letting the camera warm up before image collection and capturing images of reflectance calibration panels at an elevation close to the maximum flying height to compensate for influence from the atmosphere. The results also show that there is a strong influence of the orientation of the sunshine sensor. This introduces noise and limits the use of the raw sunshine sensor data to compensate for differences in light conditions. To handle this noise, we fit smoothing functions to the sunshine sensor data before we perform irradiance normalization of the images. The developed workflow is evaluated against data from a handheld spectroradiometer, giving the highest correlation (R2 = 0.99) for the normalized difference vegetation index (NDVI). For the individual wavelength bands, R2 was 0.80–0.97 for the red-edge, near-infrared, and red bands.

The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future.

The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels.

Forests have extraordinary importance for the conservation of endemic species in Madagascar. However, they are disappearing fast due to a number of pressures, notably unsustainable agricultural practices leading to aggravated status of biodiversity. Here, we used a number of ecological and spatial criteria to identify and prioritise unprotected forest areas for the conservation of the eight critically endangered species of lemur belonging to the Lemuridae family in Madagascar. By combining spatial information layers on the distribution areas of the studied lemurs, forest extension and conservation status, and potential human impacts (such as roads, human settlements and agriculture lands), it was possible to identify the most appropriate sites for the expansion of the conservation areas of critically endangered lemur species. Seven new sites, totalling over 33,000 ha, were identified as priority sites for the protection of those species. All of them were adjacent to or inside (just one site) existing protected areas (PAs), which likely makes their protection both feasible and socioeconomically efficient by enlarging those PAs. Legally protecting these sites would not only take Madagascar one little step ahead for meeting oncoming global biodiversity targets for 2030 but could also make a substantial contribution to the mid-term survival of the studied lemur species.

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

This paper examines various geometric shapes to determine the most suitable one for calculating tree crown area in field measurements. The study was conducted in an Eldarica pine plantation forest, which was digitally mapped using RGB images captured by a Phantom 4 Pro drone. Tree crowns were manually digitized (MD) from these images to serve as reference data. Field measurements, including the large and small crown diameters, were collected to evaluate crown areas derived from different geometric shapes. The geometric shapes considered were: Oval with Both Diameters (OBD), Circle with Small Diameter (CSD), Circle with Large Diameter (CLD), and Circle with Mean Diameters (CMD). Three analyses were performed to assess the results: correlation analysis (R2), relative root mean square error (RRMSE), and shape analysis, which included overestimation (OverID) and underestimation (UnderID) indices. The results revealed that the choice of geometric shape significantly impacts the accuracy of crown area calculations. The OBD model based on the outer boundary diameter yielded the best results with RRMSE = 0.29, R2 = 0.84, OverID = 0.18, and UnderID = 0.23, followed closely by the CMD method. In contrast, the CSD and CLD models performed less effectively, with RRMSE = 0.52, R2 = 0.42, OverID = 0.11, UnderID = 0.35 (CSD), and RRMSE = 0.59, R2 = 0.37, OverID = 0.46, UnderID = 0.22 (CLD). These differences in performance are likely due to the inclusion of empty spaces within the crown area in some models. However, the findings of this study are not universally applicable to all tree crown area calculations. The geometric shape used for crown area estimation must align with the structural characteristics of the forest and the specific geometry of the tree species under consideration.

The tree top point position is important for calculating many parameters and supporting various geometry and analyses. This study compares four methods, i.e., Local Maxima (LM), Template Matching (TM), Top Point Without Slope (TPWS) correction, and Top Point with Slope (TPS) correction, to estimate the tree top point position of Pinus eldarica using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm3). The LM, and the TM methods are applied for estimating tree top point positions. The TPWS method uses the tree's shadow on terrain without slope correction, and finally, the fourth method uses the tree's shadow on the terrain with slope correction. Results were compared against Field Tree Top (FTT) point measurements. Findings reveal that LM and TPS were the most effective. LM provided the most accurate results overall, with a relative root-mean-square error (RRMSE) of 1.08, a mean error (ME) of 0.97, and a bias score (BS) of 0.23. Estimating the tree top point position with LM showed strong correlations (R2 = 0.94) with FTT position. This study underscores the value of LM and TPS methods for precise tree top point position estimation, highlighting the need for future research into the estimation of tree top point position methods.

The fields of tropical biology and conservation face significant transformations due to rapid technological developments in remote sensing. Other fields (e.g. Archeology) are experiencing this momentous change even more rapidly. In this article, we review some of the challenges that the fields of tropical biology and conservation face during the first quarter of the twenty-first century from the perspective of various remote sensing technologies, and discuss the transformations that they may bring to these disciplines. In addition, we review two emerging technologies driving paradigm changes in the nexus of ecology, remote sensing, and analytics: near-surface remote sensing and Wireless Sensor Networks. These two technologies, arising from the eScience paradigm, offer unique opportunities to integrate field observations at hyper-temporal and spatial resolutions that were not possible as recently as 5 years ago.