Three-dimensional digital mapping of ecosystems: a new era in spatial ecology

Ecological processes occur over multiple spatial, temporal and thematic scales in three-dimensional (3D) ecosystems. Characterizing and monitoring change in 3D structure at multiple scales is challenging within the practical constraints of conventional ecological tools. Remote sensing from satellites and crewed aircraft has revolutionized broad-scale spatial ecology, but fine-scale patterns and processes operating at sub-metre resolution have remained understudied over continuous extents. We introduce two high-resolution remote sensing tools for rapid and accurate 3D mapping in ecology—terrestrial laser scanning and structure-from-motion photogrammetry. These technologies are likely to become standard sampling tools for mapping and monitoring 3D ecosystem structure across currently under-sampled scales. We present practical guidance in the use of the tools and address barriers to widespread adoption, including testing the accuracy of structure-from-motion models for ecologists. We aim to highlight a new era in spatial ecology that uses high-resolution remote sensing to interrogate 3D digital ecosystems

On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Net soil CO2 emissions are not independent of topography but tend to decline with increasing slope gradients. Such decline has been attributed to increased runoff and greater soil loss on steep slopes, leaving the soil less habitable for microorganisms. However, the specific variations of slope gradients and thus the associated soil properties relevant for CO2 emissions, especially from terraced slopes, are often disguised by the coarse resolution of digital terrain models (DTMs) based on commonly available open-source data. Such misrepresentation of the relationship between topography and soil CO2 emissions carries the risk of a wrong assessment of soil-atmosphere interaction. By applying a slope dependent soil CO2 emission model developed from erosion plots to nearby sloping and partially terraced cropland using two DTMs of different spatial resolutions, this study tested the significance of these resolution-induced errors on CO2 emission estimates. The results show that the coarser-resolution Shuttle Radar Topography Mission (SRTM) underestimated CO2-C emission by 27% compared to the higher-resolution DTM derived from Unmanned Aerial Vehicles (UAV) imagery. Such difference can be mostly attributed to a better representation of the proportion of flat slopes in the high-resolution DTM. Although the observations from erosion plots cannot be directly extrapolated to a larger scale, the 27% underestimation using the coarser-resolution SRTM DTM emphasizes that it is essential to represent microreliefs and their impact on runoff and erosion-induced soil heterogeneity at an appropriate scale. The widespread impact of topography on erosion and deposition on cropland, and the associated slope-dependent heterogeneity of soil properties, may lead to even greater differences than those observed in this study. The greatly improved estimation on CO2 emissions by the UAV-derived DTM also demonstrates that UAVs have a great potential to fill the gap between conventional field investigations and commonly applied coarse-resolution remote sensing when assessing the impact of soil erosion on global soil-atmosphere interaction

Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland

Spatial soil information in forests is crucial to assess ecosystem services such as carbon storage, water purification or biodiversity. However, spatially continuous information on soil properties at adequate resolution is rare in forested areas, especially in mountain regions. Therefore, we aimed to build high-resolution soil property maps for pH, soil organic carbon, clay, sand, gravel and soil density for six depth intervals as well as for soil thickness for the entire forested area of Switzerland. We used legacy data from 2071 soil profiles and evaluated six different modelling approaches of digital soil mapping, namely lasso, robust external-drift kriging, geoadditive modelling, quantile regression forest (QRF), cubist and support vector machines. Moreover, we combined the predictions of the individual models by applying a weighted model averaging approach. All models were built from a large set of potential covariates which included e.g. multi-scale terrain attributes and remote sensing data characterizing vegetation cover. Model performances, evaluated against an independent dataset were similar for all methods. However, QRF achieved the best prediction performance in most cases (18 out of 37 models), while model averaging outperformed the individual models in five cases. For the final soil property maps we therefore used the QRF predictions. Prediction performance showed large differences for the individual soil properties. While for fine earth density the R2 of QRF varied between 0.51 and 0.64 across all depth intervals, soil organic carbon content was more difficult to predict (R2 = 0.19–0.32). Since QRF was used for map prediction, we assessed the 90% prediction intervals from which we derived uncertainty maps. The latter are valuable to better interpret the predictions and provide guidance for future mapping campaigns to improve the soil maps

An Assessment Of The Impact Of Dem Interpolation Technique, Resolution, And Terrain Type On The Extraction Of Drainage Network

This research used points extracted from high-resolution DEMs (1m) to investigate the impact of resolution, interpolation method and topography on the accuracy of drainage network extraction. The investigation was conducted by evaluating the accuracy of the estimations of streams length, streams number, drainage density, and the Longitudinal Root Mean Square Error (LRMSE) of the extracted drainage networks from different DEMs interpolated using Topo to raster, Natural Neighbor (NN), kriging and IDW interpolation methods at 5, 10, 15 and 20m resolutions over moderate, steep, and gentle slope terrain. Each evaluation conducted yielded a different result, but the accuracy of the streams length estimation for most of the DEMs at all the sites increases with an increase in streams order. The total lengths of all the streams of each of the extracted networks at gentle and steep slope sites are shorter than those of the corresponding reference networks though, 15 and 20m kriging and IDW DEMs created longer streams at the moderate slope site. IDW DEMs have proven reliable for streams length estimation while Topo to raster 5, 10, and 15m for streams number estimation. In general, N.N. extracted networks are the only networks that show consistency in the streams length and number estimations, drainage density estimation as well as in LRMSE and DEM RMSE computation at all the resolutions and for all the sites. Therefore, the accuracy of N.N. DEMs and their derivatives do not rapidly change with change in resolution, especially between 5 and 20m at all (steep, gentle and moderate) terrain types

Benefits and trade-offs of legume-led crop rotations on crop performance and soil erosion at various scales in SW Kenya

Soil erosion and land fragmentation threaten agricultural production in large parts of the Western Kenyan Highlands. In Rongo watershed, maizecommon bean intercropping systems, which dominate the agricultural landscape, are vulnerable to soil degradation, especially on long slope lengths where ground and canopy cover provision fail to protect the soil from the disruptive impact of raindrops. The inclusion of soil conservation measures like hedgerows, cover crops or mulch can reduce soil erosion, but compete with crops for space and labour. Knowledge of critical slope length can minimise interventions and tradeoffs. Hence, we evaluated maizecommon bean intercrop (MzBn) regarding runoff, erosion and crop yield in a slope length trial on 20, 60 and 84 m plot lengths, replicated twice on three farms during one rainy season in Rongo, Migori County. Additionally, we investigated systems of MzBn (farmers practice), MzBn with 5 Mg ha-1 Calliandra calothyrsus mulch (Mul), Arachis hypogaea (Gnt), Lablab purpureus (Lab) and Mucuna pruriens (Muc), regarding their impact on infiltration, runoff, soil loss, soil C and N loss during three rainy seasons (long and short rains, LR and SR, 2016, and LR 2017). Measured field data on soil, crop, spatial maps and meteorology were used as input datasets to parameterize and calibrate the LUCIA model. The calibrated and validated model was then used to simulate agronomic management scenarios related to planting date (planting with first rain vs baseline) and vegetation cultivar (short duration crop) to mitigate water stress. Based on the measurements, groundcover was most influential over rainfall intensity (EI30) and plant canopy cover in predicting soil loss. Dense groundcover of Mul at the beginning of the rainy seasons was decisive to significantly (p 5mm) in the topsoil under Mul at the end of SR 2016 significantly (p<0.05) increased infiltration rates (420 mm hr-1) in LR 2017 compared to Lab (200 mm hr-1) and Gnt (240 mm hr-1). Average C and N concentrations in eroded sediments were significantly reduced under Mul (0.74 kg C ha1, 0.07 kg N ha1) during the LR 2016 as compared to MzBn (3.20 kg C ha1, 0.28 kg N ha1) and Gnt (2.54 kg C ha1, 0.23 kg N ha1). Likewise, in SR 2016 Mul showed significantly lowered C and N losses of 3.26 kg C ha1 and 0.27 kg N ha1, respectively, over Lab (9.82 kg C ha1, 0.89 kg N ha1). Soil loss over 84 m slope length was overall significantly higher by magnitudes of 250 and 710% than on 60 and 20 m long plots, respectively, which did not differ significantly among each other (p<0.05). For runoff, 84 m plot length differed significantly from 60 and 20 m, but in the opposite trend as for soil loss. Across all three farms, slope gradient and slope length were the variables with highest explanatory power to predict soil loss. At the individual farm level, under homogeneous slope and texture, slope length and profile curvature were most influential. Considering results of slope length experiments, plot lengths less than 50 m appear to be preferential considering soil loss, sediment load, and soil loss to yield ratio under the given rainfall, soil and slope conditions. Our results call for integrating slope length options and cropping systems for effective soil conservation. We recommend planting Mucuna and Calliandrahedgerows as buffer strips below the critical slope length, and legume cash crops and maize uphill. Such approaches are critical in the backdrop of land fragmentation and labour limitation in the region to sustainably maximise land area. In the modelling exercise, crops planted one and three weeks after the baseline planting date increased Maize and Muc grain yield over the baseline during the three cropping seasons, the three weeks treatment in particular. This could be due to more favourable weather conditions during the shifted vegetation period. Increased grain yield corresponded to high water use efficiency (WUE). The short duration crop planted three weeks after the baseline planting date (PD3WL+SDC10) showed the highest grain yield after PD3WL (three weeks late plaing with BL variety). The use of cultivars with short growth cycle offers the flexibility of planting again where crops failed due to crop water stress or where the rains delay, ensuring completion of the growth cycle before the season ends. Given that short growth duration crops produce less grain yield compared to their counterpart full season crops, due to the length of their cycles, breeding programs must prioritize traits that can enhance the size of the grain-filling sink. At the plot level, management systems that reduce evaporation and retain soil moisture, e.g. mulching, application of farmyard manure etc., must be promoted to reduce evapotranspiration.Bodenerosion und Kleinteiligkeit von Betriebsflächen bedrohen die landwirtschaftliche Produktion in weiten Teilen des westkenianischen Hochlands. Im untersuchten Wassereinzugsgebiet von Rongo sind die weit verbreiteten Mais-Bohne-Mischkkultursysteme gefährdet durch Bodendegradierung. Dies ist vor allem auf langen Hängen und dort der Fall, wo der Oberboden nicht durch entsprechende Bodenbedeckung vor Schlagregen geschützt ist. Bodenschutzmaßnahmen wie Hecken, Bodendecker oder Mulch können das Ausmaß von Bodenerosion verringern, konkurrieren aber oft mit der Hauptkultur um Raum bzw. Arbeitskraft. Der gezielte Einsatz solcher Interventionen ausschliesslich in Bereichen kritischer Hangpositionen kann solcherlei Aufwand und Konkurrenzeffekte minimieren. In diesem Zusammenhang wurden in der hier vorgestellten Studie Mais-Bohne-Mischkulturen (MzBn) während einer Anbausaison auf drei unterschiedlichen Hanglängen (20, 60 und 84 m) mit jeweils zwei Wiederholungen auf drei Betrieben in Rongo, Migori County, hinsichtlich Oberflächenabfluss, Erosion und Ertrag verglichen. Zudem wurden MzBn, MzBn mit 5 Mg ha-1 Calliandra calothyrsus Mulch (Mul), Arachis hypogaea (Gnt), Lablab purpureus (Lab) und Mucuna pruriens (Muc) hinsichtlich Infiltration, Oberflächenabfluss, Erosion, organischem Boden-C und Gesamt-Boden-N während dreier Anbauperioden (lange und kurze Regenzeit 2016 und lange Regenzeit 2017) verglichen. Gemessene Boden- und Pflanzenparameter sowie Boden-, Landnutzungskarten und ein digitales Höhenmodell wurden nebst tagesgenauen Wetterdaten als Eingaben für das Lucia (Land Use Change Impact Assessment)-Modell verwendet. Mit dem kalibrierten und validierten Modell wurden dann Szenarien zum Wasserstressmanagement mit Fokus auf Aussaatzeitpunkten und Sortenwahl (verschiedene Vegetationsdauer) getetstet. Die Auswertung der Feldversuche zeigte, dass der Grad der Bodenbedeckung (durch Biomasse, Mulch und Streu) stärkeren Einfluss auf Bodenabtrag hatte als Regenintensität (EI30) und Bodenbedeckung des Blätterdachs allein. Die dichte Bodenbedeckung durch Calliandramulch in Mul zu Beginn der Saison war dabei entscheidend für signifikant geringeren Oberflächenabfluss (88, 87 und 84% niedriger als in MzBn, Lab und Gnt) und Bodenabtrag (66 und 65% niedriger als in Gnt und Lab). Der hohe Anteil großer Bodenaggregate > 5mm im Oberboden zum Ende der kurzen Regenzeit (SR) 2016 stand in Zusammenhang mit im Vergleich zu Lab (200 mm hr-1) and Gnt (240 mm hr-1) signifikant erhöhten Infiltrationsraten unter Mul (420mm h-1) in der langen Regenzeit (LR) 2017. Durchschnittliche C- und N-Konzentrationen in Sedimenten waren in der LR 2016 unter Mul (0.74 kg C ha1, 0.07 kg N ha1) signifikant niedriger als unter MzBn (3.20 kg C ha1, 0.28 kg N ha1) und Gnt (2.54 kg C ha1, 0.23 kg N ha1). Ebenso waren in der SR 2016 C- und N-Verluste deutlich geringer als unter Lab (3.26 kg C ha1 und 0.27 kg N ha1 im Vergleich zu 9.82 kg C ha1 und 0.89 kg N ha1). Bodenabtrag bei 84 m Hanglänge war 250 bzw. 710% höher als auf den 60 und 20 m Anlagen, wobei sich letztere statistisch (p<0.05) nicht unterschieden. Hinsichtlich Oberflächenabfluss unterschieden sich die Hanglängen ebenfalls statistisch, aber in entgegengesetzter Richtung. Im Vergleich der Flächen auf allen drei Betrieben waren Hangneigung und länge die statistisch einflussreichsten Faktoren bezüglich Bodenabtrag. Auf den einzelnen Betrieben, d.h. bei gleich Hangneigung und Bodenart, waren Hanglänge und Hangform ausschlaggebend. Als Ergebnis der Hanglängenversuche erwies sich eine Länge von 50 m unter den gegebenen Wetter-, Boden- und Geländebedingungen als kritisch bzgl. Erosion, Sedimentmengen und dem Verhältnis von Erosion zu Ertrag. Die Ergebnisse dieser Studie legen nahe, dass effektiver Bodenschutz vor allem durch die Integration von Hanglänge und Anbausystem (Pflanzenwahl) erreicht werden kann. Es wird empfohlen Calliandra-Hecken mit Mucuna-Unterpflanzung als Pufferzonen in Streifen unterhalb der kritischen Hanglänge anzulegen sowie Körnerleguminosen und Mais als cash crops oberhalb. Durch diesen Ansatz kann vor dem Hintergrund der Landfragmentierung und Knappheit an Arbeitskraft in der Untersuchungsregion die nutzbare Landfläche nachhaltig optimiert werden. Der Modellierungsteil dieser Studie zeigte, dass Erträge bei einer und besonders bei drei Wochen späterem Aussaatzeitpunkt im Vergleich zum lokal üblichen Termin während aller drei Anbauperioden zu höheren Kornerträgen führte. Grund hierfür könnten günstigere Wetterbedingungen während der somit verschobenen Vegetationsperiode sein. Die höheren Erträge gingen einher mit effizienterer Wassernutzung der Pflanzen. Eine Sorte mit verkürzter Vegetationsperiode, drei Wochen nach dem üblichen Termin gepflanzt (PD3WL+SDC10), erzielte die höchsten Erträge. Sorten kürzerer Vegetationsdauer bieten allgemein höhere Flexibilität in Fällen spät einsetzender Regenfälle oder von Pflanzenmortalität, da auch bei wiederholter Aussaat die Regenzeit noch hinreichend genutzt werden kann. Angesichts der niedrigereren Ertragbildung während verkürzter Vegetationsdauer sollte ein höherer Kornanteil prioritäres Zuchtziel für zukünftige Sorten sein. Auf der Seite der Landwirte bedeutet dies, dass vermehrt Anbausysteme, die Evaporation verringern und Bodenfeuchte konservieren (z.B. Mulchen, Mistgaben), zur Anwendung kommen sollten

Basic Properties of Calcocambisol from a Location on North Dalmatian Plain

Calcocambisol is the most dominant soil type developed on Dinaric karst. It is formed by pedogenic processes acting on carbonate rocks, which include weathering, accumulation of insoluble residue, organic matter, and allogenic material and braunification. Further development of Calcocambisol includes leaching of clay from upper soil horizons and secondary accumulation in lower horizons. Calcocambisols are exclusively developed on carbonate rocks characterised by diverse relief forms resulting in variable soil depth over short distances and consequently different phases of soil development. Thus, the goal of this study was to analyse morphological, physical, and chemical properties of Calcocambisols in different stages of development from a location within the Krka National Park. Results of soil analysis showed similarities in morphological properties, soil field and air capacity, density and SOC content. On the other hand, differences in properties included different carbonate content and pH values of topsoil and difference in particle size distribution. These differences can be attributed to irregular rocky surface which plays important role in allogenic particles distribution and water percolation. Increased leaching of clay particles to deeper horizons results in diversification of Bt (argic) horizon, indicating more advanced stage of soil development towards Luvisol formation