Inter-row Vegetation Increases Abundances of Ground- and Canopy-dwelling Arthropods in Mediterranean Vineyards

The decline of arthropod populations observed in many parts of the world is a major component of the sixth mass extinction with intensive agriculture being one of its main drivers. Biodiversity-friendly farming practices are taking centre stage in the recovery process. In vineyards, vegetation cover is commonly used for production purposes, to reduce soil compaction by machinery use and soil erosion. Here we examined the effects of vegetation cover and soil management on the abundance of ground- (spiders, beetles, Hemiptera and harvestmen) and canopy-dwelling (wild bees, green lacewings, beetles and Hemiptera) arthropods in three categories of vineyards: (i) vineyards with no vegetation, (ii) partially vegetated (every second inter-row is vegetated) and (iii) all inter-rows are vegetated. We recorded a general positive effect of a decrease in soil perturbation intensity and corresponding higher vegetation cover on arthropod abundance. Plant species richness was the most important vegetation parameter, with a positive effect on spiders, harvestmen, hemipterans and beetles (ground and canopy) abundances. Using a path analysis, we also highlighted the central role of inter-row vegetation management in trophic and non-trophic relationships between vegetation and arthropods, and between arthropod groups. Our results demonstrate the benefits of a softer soil management preserving a diverse vegetation cover for the conservation of arthropods in Mediterranean vineyards.

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

Optimization of Scanning Scheme for Low-height Vegetation Survey Based on Terrestrial Laser Scanning - A Case Study on the Restored Sand Land, Southern Qinghai-Tibetan Plateau

Background: In recent decades, vegetation surveys based on terrestrial laser scanning (TLS) have developed rapidly, especially on the forest inventory, but few studies have been conducted to the low-height vegetation. Because of the high investigation cost and subjectivity, it is impending to provide a scientific scanning scheme based on the TLS for the low-height vegetation survey (e.g. shrub, grassland, and meadow) in eco-fragile region (e.g. Qinghai-Tibetan Plateau). Method: In this study, we extracted the vegetation parameter i.e., number, height (H), and crown width (CW) of the two sample plots to evaluate the integrity of the data collected by TLS, on the restored sand land in southern Qinghai-Tibetan Plateau. We assessed the difference between the scanning mode of single-scan (SS) and multiple-scan (MS), and evaluated the influence of terrain fluctuation (windward slope, leeward slope, and the peak of slope) on the determination of scanning spots.Results: The results showed that: (1) the accuracy of vegetation parameter extracted by the mode of SS was mainly affected by the occlusion and the distance from central scanning spot, the RMSE of vegetation parameters is the smallest (RMSEH = 0.186 m; RMSECW = 0.208 m) within 20 m from the central scanning spot. (2) For the MS mode, in addition to the central scanning spot, the scanning spot located at the peak of the slope is the most important, which was the connection of combining the data of windward slope and leeward slope.Conclusion: To sum up, the scientific layout of scanning spot is the key to collecting data by TLS efficiently, and topography is the main factor affecting the layout of scanning spot. Since occlusion effect cannot be avoided, it can only be compensated by setting up more scanning points. Secondly, the accuracy of different sensors will has influence on the distance between adjacent scanning spots.

Exploration and Utilization of Plant Species Based on Social Culture (Hindu Religion Ceremony) in Tenganan Pegringingan Village, Karangasem Regency, Bali Province, Indonesia

This research aims to find out (1) the plants that were used in religious ceremonies (Hinduism) in accordance with the Bali Aga Tenganan Pegringsingan culture. 2) the making process of the various means needed in religious ceremonies (Hinduism) related to the utilization of useful plant species in Bukit Kangin Forest, Tenganan Pegringsingan Village. The Research was explorative (vegetation) and socio-system (community) research. The populations of this research were ecosystem aspects and sociosystem aspects. The ecosystem aspects included all of the useful plant species in Bukit Kangin Forest of Tenganan Pegringsingan village. Meawhile, the sociosystem aspects included the village officials, the village public figures and the community of Tenganan Pegringsingan village. The ecosystem sample (the vegetation) used in this research included the plant species in the forest of Tenganan Pegringsingan Village covered by the 1x1m2 sized seedling square, 10x10m2 sized sapling square and 20x20m2 sized square for trees (mature plants). There were 65 squares in total. The sociosystem samples in this research were the village officials, public figures, shamans, offerers, craftsmen, and the public in Tenganan Pegringsingan village. The methods applied in this research were (1) square method for ecosystem (vegetation) parameter. (2) Interview, questionnaire and observation for sociosystem parameter. The collected data were further analyzed descriptively. The results of the research showed that (1) of 46 useful plant species found in Bukit Kangin forest of Tenganan Pegringsingan, 29 of them were plant species that were utilized for religious ceremonies (Hinduism), meanwhile there were 17 plant species utilized for clothing, food, shelter, industry, medicine, and other household purposes. (2) The utilization of plant species for religious purposes was still in traditional method, in accordance with the socio-cultural of the local community.

Study of a High Spectral Resolution Hyperspectral LiDAR in Vegetation Red Edge Parameters Extraction

Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500–1000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. “Red Edge” parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the “Red Edge” position (REP), “Red Edge” slope (RES) and “Red Edge” area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC© HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves’ REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for “Red Edge” parameters extraction.

Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset.

Inventarisasi Jenis Pohon di Kawasan Pusat Pembinaan, Pendidikan, dan Pelatihan (Pusbindiklat) Peneliti - LIPI untuk Menunjang Faktor Keselamatan

The area within Pusbindiklat Peneliti-LIPI (The National Training and Education Center for Researchers Development-Indonesian Institute of Sciences) has been planted with many trees as an effort for reforestration and increasing aesthetic value. Unfortunately, the management of Pusbindiklat Peneliti-LIPI paid less attention to the trees potency and its characteristic as well as inventorizing all trees. This study aim to inventorize tree species based on their morphological characteristic in order to know the potency concerned with safety. Research was conducted through inventorizing and scoring the suitability factors of all trees, determining the tree plantation points, which is converted to the map of Pusbindiklat Peneliti-LIPI. In addition, direct interviews were also conducted to the civitas of Pusbindiklat Peneliti-LIPI. Trees that have been found in the area of Pusbindiklat Peneliti-LIPI consist of 20 family, 42 species, and 217 individuals. From the inventory of the trees, there were 11 species which were unsuitable with the criteria of urban forest vegetation parameter. Moreover, based on the map of trees species at Pusbindiklat Peneliti-LIPI area, there are 85 individual from 20 species of trees that are not suitable to be planted in Pusbindiklat Peneliti, since they were planted near the parapet wall and buildings of Pusbindiklat Peneliti-LIPI.

INFLUENCE OF EMERGENT VEGETATION ON SEDIMENT YIELD AT CROSS-SHORE COASTAL ENVIRONMENTS

This study aimed to conceptually analyze the change in the magnitude of offshore sediment yield, wave energy, and offshore dislocation of sediment particles on coastal regions in the presence of coastal vegetation. This was achieved by comparing the simultaneous physical changes at coastal zones that were partly covered with vegetation while the remaining part had no vegetation. Series of experiments were conducted, and the interactions between the vegetation parameter, ratio of sediment yields, and offshore sediment dislocation distances were analyzed and determined to define the relationship between the parameters. The resultant empirical equations mostly followed a power relationship and fit the experimental data. The energy decay coefficient, reflecting the energy used in the presence of the vegetation, had strong protection ability and approached 80% energy decay as the vegetation parameter increased. The performance of natural vegetation cover was adequate, simulating a 50–80% decrease in offshore sediment yield, depending on the magnitude of the vegetation parameter.

INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. <br><br> This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. <br><br> The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.