Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data.

High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red–Green–Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R² = 0.53), with R² > 0.7 and RRMSE < 15%.

Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R² = 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R² > 0.9 for May, June, July, and August, and R² = 0.5 for October) and hyperspectral imagery (0.6 < R² < 0.9) led to satisfactory high and consistent accuracies for all months.

Red palm weevil (RPW) is widely considered a key pest of palms, creating extensive damages to the date palm trunk that inevitably leads to palm death if no pest eradication is done. This study evaluates the potential of a remote sensing approach for the timely and reliable detection of RPW infestation on the palm canopy. For two consecutive years, an experimental field with infested and control palms was regularly monitored by an Unmanned Aerial Vehicle (UAV) carrying RGB, multispectral, and thermal sensors. Simultaneously, detailed visual observations of the RPW effects on the palms were made to assess the evolution of infestation from the initial stage until palm death. A UAV-based image processing chain for nondestructive RPW detection was built based on segmentation and vegetation index analysis techniques. These algorithms reveal the potential of thermal data to detect RPW infestation. Maximum temperature values and standard deviations within the palm crown revealed a significant (α = 0.05) difference between infested and non-infested palms at a severe infestation stage but before any visual canopy symptoms were noticed. Furthermore, this proof-of-concept study showed that the temporal monitoring of spectral vegetation index values could contribute to the detection of infested palms before canopy symptoms are visible. The seasonal significant (α = 0.05) increase of greenness index values, as observed in non-infested trees, could not be observed in infested palms. These findings are of added value for steering management practices and future related studies, but further validation of the results is needed. The workflow and resulting maps are accessible through the Mapeo^® visualization platform.

Understanding of people’s landscape preferences is important for decision-making about land planning, particularly in the disturbance patterns that usually occur in rural-urban gradients. However, the use of different types of images concerning the same landscape may influence social preferences and thus perceptions of landscape management and planning decisions. We evaluated landscape preferences and perceptions in four landscapes of southern Chile. We specifically: (1) compared people’s perceptions related to living in, visiting, the scenic beauty, well-being, risks, and level of landscape disturbance; and (2) evaluated the influence of the type of scene (i.e., eye-level or aerial images) in these preferences and perceptions. Preferences and perceptions resulted to be better when using eye-level (4.0 ± 1.1) than aerial (3.7 ± 0.6) images. In general, we observed a negative association between preferences and perceptions and the landscape disturbance; however, it was consistent when using aerial images but was masked when valuing landscape through eye-level images. Implications of these results are relevant because by far, most landscape preference studies use traditional eye-level images. Different types of scenes should be considered in order to embrace the landscape preferences and perceptions of all those involved and help decision-making in landscape planning.

Monitoring irrigated areas has received considerable attention given the importance for e.g. food security, and for the management of water resources. However, the current extent of irrigated areas at continental to global scale is still uncertain. Existing maps, especially covering large areas, are mainly derived from country-level statistics. Remote sensing has proven to be a valuable tool to map agricultural production areas, yet mapping irrigated areas has proven to be challenging. On the one hand, agricultural areas are difficult to map in general, while on the other hand the step from land cover (cropland) to land use (irrigation practice) requires additional data sources other than the optical satellite data. Recently, a generic irrigation mapping method was developed in the framework of the FAO WaPOR data portal (https://wapor.apps.fao.org), to provide irrigation maps at a continental scale for Africa and the Near- East on a seasonal and annual basis. The method combines information on (i) the land cover, (ii) the phenology as obtained from time series analysis, (iii) actual Evapotranspiration (ETa), and (iv) precipitation. In short, the Water Deficit Index (WDI) is computed as the ratio of the seasonal precipitation over the seasonal ETa, with values below one when the water consumption is larger than the water availability on a seasonal basis. Although very good results were obtained with a WDI threshold of 0.9 on a continental scale, some issues remain, especially in areas where the water availability and the consumption are very similar, or where only supplementary irrigation is applied. In addition to this physically-based approach, supervised classification methods have proven to be a suitable irrigation mapping method as well, yet suffer from the drawback that they require a large amount of reference information. To evaluate which method is best suited to distinguish between irrigated and rainfed agriculture, a comparison between both is made for three different irrigation schemes: (i) the Nile delta in Egypt, which receives full irrigation, (ii) the Bekaa valley in Lebanon where both irrigated and rainfed croplands are present, and (iii), the Koga region in Ethiopia, which is rainfed during the rainy season, but irrigated in the dry season. From the analysis, it is clear that for regions with very little precipitation (Nile delta), the physical-based method is very well suited to map the irrigated areas, without the need for ground reference information. However, in more complex systems, such as the Bekaa valley, the confusion between irrigated and rainfed areas is quite substantial for the physical-based method, and the supervised classification method obtained more promising results. This may suggest that for continental or global irrigation mapping, a combination of both methods is desirable. Irrigated areas in regions with low precipitation could best be mapped based on the WDI approach, while in more complex areas, supervised classification methods may be required. This would strongly reduce the need for detailed reference data over large areas, while maintaining a high mapping accuracy.