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Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three‐dimensional vegetation structure at high resolution, but the scalability of using these data for large‐scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer‐level graphics processing units, provide new opportunities. Here, we apply a deep learning‐based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre‐processing such as leaf‐wood separation or quantitative reconstructions. Using modern network architectures and data augmentation techniques, and without extensive pre‐processing, we are able to achieve high overall and per‐species accuracy that is comparable or higher than in existing work while using data from a water‐limited ecosystem complicated by structural convergence and multi‐stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.

Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space‐for‐time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water‐limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water‐limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro‐climatic change.

Two major forms of vegetation patterns have been observed in drylands: nearly periodic patterns with characteristic length scales, and amorphous, scale-free patterns with wide patch-size distributions. The emergence of scale-free patterns has been attributed to global competition over a limiting resource, but the physical and ecological origin of this phenomenon is not understood. Using a spatially explicit mathematical model for vegetation dynamics in water-limited systems, we unravel a general mechanism for global competition: fast spatial distribution of the water resource relative to processes that exploit or absorb it. We study two possible realizations of this mechanism and identify physical and ecological conditions for scale-free patterns. We conclude by discussing the implications of this study for interpreting signals of imminent desertification.

In the headwater catchments of the Rocky Mountains, plant productivity and its dynamics are largely dependent upon water availability, which is influenced by changing snowmelt dynamics associated with climate change. Understanding and quantifying the interactions between snow, plants and soil moisture is challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. Recent advances in satellite remote sensing have created an opportunity for monitoring snow and plant dynamics at high spatiotemporal resolutions that can capture microtopographic effects. In this study, we investigate the relationships among topography, snowmelt, soil moisture and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope normalized difference vegetation index (NDVI) images. To make use of a large volume of high-resolution time-lapse images (17 images total), we use unsupervised machine learning methods to reduce the dimensionality of the time lapse images by identifying spatial zones that have characteristic NDVI time series. We hypothesize that each zone represents a set of similar snowmelt and plant dynamics that differ from other identified zones and that these zones are associated with key topographic features, plant species and soil moisture. We compare different distance measures (Ward and complete linkage) to understand the effects of their influence on the zonation map. Results show that the identified zones are associated with particular microtopographic features; highly productive zones are associated with low slopes and high topographic wetness index, in contrast with zones of low productivity, which are associated with high slopes and low topographic wetness index. The zones also correspond to particular plant species distributions; higher forb coverage is associated with zones characterized by higher peak productivity combined with rapid senescence in low moisture conditions, while higher sagebrush coverage is associated with low productivity and similar senescence patterns between high and low moisture conditions. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements and identify areas likely vulnerable to ecological change in the future.

Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth’s climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems.

Over the past century, increases in indigenous woody plant species, also known as woody encroachment (WE), has occurred in grasslands and savannas across the globe. While the impact on grassland and savanna composition and productivity has been well studied, little is known of the impacts on the hydrological cycle. WE may increase evapotranspiration (ET) losses, leading to reduced infiltration and ultimately reduced freshwater availability, which is of particular concern in arid and semi-arid areas. The aim of this study was to determine the effect of Colophospermum mopane (mopane) encroachment on ET in a semi-arid savanna located in South Africa. Mopane is widely distributed across southern Africa, and is one of the main encroaching species of the region. Following an assessment of the validity of two surface renewal approaches, SR1 and SRDT, against short eddy covariance campaigns for sensible heat flux estimation, the SR1 approach was used to estimate ET at an experimental woody plant clearing trial from November 2019 to July 2022. For the two drier years of the study, the removal of mopane trees had little effect on ET. However, for the wettest year of the study, the removal of mopane trees decreased ET by 12%, supporting the hypothesis that the conversion from grass dominance to woody dominance can increase ET. Annual ET exceeded annual rainfall in all 3 years, indicating that the vegetation supplements its water use with soil water that has accumulated during previous wet seasons, or that tree roots facilitate hydraulic lift of deep soil water, or groundwater, to depths within the rooting depth of both trees and grasses. Further research is needed to confirm the exact mechanism involved, and the consequences of this for groundwater and streamflow at landscape scales.

Arid and semi-arid ecosystems represent a crucial but poorly understood component of the global water cycle. Taking a desert ecosystem as a case study, we measured sap flow in three dominant shrub species and concurrent environmental variables over two mean growing seasons. Commercially available gauges (Flow32 meters) based on the constant power stem heat balance (SHB) method were used. Stem-level sap flow rates were scaled up to stand level to estimate stand transpiration using the species-specific frequency distribution of stem diameter. We found that variations in stand transpiration were closely related to changes in solar radiation (Rs), air temperature (T), and vapor pressure deficit (VPD) at the hourly scale. Three factors together explained 84% and 77% variations in hourly stand transpiration in 2014 and 2015, respectively, with Rs being the primary driving force. We observed a threshold control of VPD (~2 kPa) on stand transpiration in two-year study periods, suggesting a strong stomatal regulation of transpiration under high evaporative demand conditions. Clockwise hysteresis loops between diurnal transpiration and T and VPD were observed and exhibited seasonal variations. Both the time lags and refill and release of stem water storage from nocturnal sap flow were possible causes for the hysteresis. These findings improve the understanding of environmental control on water flux of the arid and semi-arid ecosystems and have important implications for diurnal hydrology modelling.

Water distributed in deep soil reservoirs is an important factor determining the ecosystem structure of water-limited environments, such as the seasonal tropical savannas of South America. In this study a two-dimensional (2D) geoelectrical profiling technique was employed to estimate seasonal dynamics of soil water content to 10-m depth along transects of 275 m in savanna vegetation during the period between 2002 and 2006. Methods were developed to convert resistivity values along these 2D resistivity profiles into volumetric water content (VWC) by soil depth. The 2D resistivity profiles revealed the following soil and aquifer structure characterizing the underground environment: 0–4 m of permanently unsaturated and seasonally droughty soil, less severely dry unsaturated soil at about 4–7 m, nearly permanently saturated soil between 7 and 10 m, mostly impermeable saprolite interspaced with fresh bedrock of parent material at about 10–30 m, and a region of highly conductive water-saturated material at 30 m and below. Considerable spatial variation of these relative depths is clearly demonstrated along the transects. Temporal dynamics in VWC indicate that the active zone of water uptake is predominantly at 0–7 m, and follows the seasonal cycles of precipitation and evapotranspiration. Uptake from below 7 m may have been critical for a short period near the beginning of the rainy season, although the seasonal variations in VWC in the 7–10-m layer are relatively small and lag the surface water recharge for about 6 months. Calculations using a simple 1-box water balance model indicate that average total runoff was 15–25 mm month−1 in the wet season and about 6–9 mm month−1 in the dry season. Modeled ET was about 75–85 mm month−1 in the wet season and 20–25 mm month−1 in the dry season. Variation in basal area and tree density along one transect was positively correlated with VWC of the 0–3-m and 0–7-m soil depths, respectively, during the wettest months. These multitemporal measurements demonstrate that the along-transect spatial differences in soil moisture are quasi-permanent and influence vegetation structure at the scale of tens to hundreds of meters.

Purpose Many efforts of restoring vegetation have ignored the feedbacks between biotic and abiotic factors that have developed in water-limited ecosystem. Dried soil layers (DSLs) have formed extensively on the Chinese Loess Plateau (CLP). The objective of this study was to identify the primary factors controlling spatial pattern of DSLs on the CLP. Materials and methods Two DSL indices (DSL thickness (DSLT) and soil water content in a DSL (DSL-SWC)) were estimated by measuring SWC to a depth of 5 m at 86 sites along a south-north transect on the CLP in 2013. The correlation between the spatial pattern of DSLs and environmental factors was determined with redundancy analysis (RDA). Results and discussion DSLs had formed at most of the sites (66 of the 86 sites) along the transect. The sites without DSLs were primarily in an irrigated agricultural zone. DSLT was >400 cm and generally increased from south to north, and DSL-SWC was 2.54% ( v / v ) in the semi-arid zone of the transect. The connected features of DSLs between connected neighboring sampling units exhibited a much wider extent. A total of nine environmental variables were the primary contributors to the spatial pattern of the DSLs, explaining approximately 47.3% of the variability. Local conditions were responsible for the higher proportion of explained variability than climatic factors. In addition, field capacity was the most important factor in all environmental factors, which may have influenced water-holding capacity. Conclusions This study concludes that spatial continuity and local conditions determine the spatial pattern of DSLs at a regional scale. Understanding the characteristic of DSLs is useful for efficiency of vegetation restoration and soil water management.

The present study demonstrated the methodology to assess agro-climatic suitability of the soybean crop through integration of crop suitability based on FAO framework of land evaluation and biophysical (water limited) yield potential in the rainfed agro-ecosystem. A long term climatic database (1980–2003) was prepared to compute decadal rainfall and temperature variations of 13 IMD stations in part of Madhya Pradesh state. The climatic database was used in soil water balance software–BUDGET to compute crop specific length of growing period (LGP) and biophysical production potential such as water limited crop yield potential of each soil types for soybean crop. Water limited crop yield potential of soils were found to be varied from 33 to 100 and LGP ranged from 65 to 180 days in the area. FAO based land suitability was analyzed in association with the water limited yield potential for better appraisal of land potential and assess their suitability in rainfed area. FAO based land suitability indicated 2.45 % area as highly suitable and 57.49 % area as moderately suitable. However, integration of water limited crop yield potential with FAO based land suitability lead to agro-climatic suitability analysis indicated 17.60 % and 40.03 % area, respectively as highly suitable and moderately suitable. FAO based land evaluation showed 88.13 % of plains as moderately suitable whereas agro-climatic suitability indicated only 47.79 %. Agro-climatic suitability analysis revealed undulating plateau and undulating plains as most suitable for soybean crop.