PEMETAAN BIOMASSA TEGAKAN HUTAN HUJAN TROPIS DI BUKIT MANDIANGIN MENGGUNAKAN CITRA SENTINEL-2 MSI

The preservation of standing biomass is one of the most vital elements for environmental sustainability and the sustainability of the forest itself. One of the actions that can be taken in an effort to maintain the sustainability of forest stand biomass is to map the distribution of biomass, and monitor changes or dynamics of stand biomass from time to time in a sustainable manner. This study aims to build a model based on remote sensing imagery to estimate the total biomass of tropical rainforest stands in Mandiangin Hill, South Kalimantan. The models developed in this study are based on vegetation indices extracted from Sentinel-2 MSI Imagery. A total of ten vegetation indices were tested in this study. For the construction process and validation of stand biomass estimation models, biomass information was measured directly in the field using a number of measuring plots. Stand biomass estimation models were made by correlating stand biomass information from the field with vegetation indices from Sentinel-2 MSI Imagery. The results showed that the most accurate model for estimating the biomass of tropical rainforest stands was 9.5806.exp (0.1454.PSSRa). Where PSSRa is Pigment Specific Simple Ratio. This model has a correlation coefficient (R2) of 0.876, a Mean Absolute Percentage Error (MAPE) of 16.8%, and a Root Mean Square Error (RMSE) of 32.6. The estimation results show that the total biomass of the Bukit Mandiangin tropical rainforest stands is between 11.7 to 998.5 Mg/ha, with an average biomass of 135.8 Mg/ha. Furthermore, the estimation of stand biomass in this study is limited to woody vegetation with a DBH of 10 cm and above. The PSSRa model with various improvements can be used to accurately estimate stand biomass

Spectral Diffractive Lenses for Measuring a Modified Red Edge Simple Ratio Index and a Water Band Index

We propose a novel type of spectral diffractive lenses that operate in the ±1-st diffraction orders. Such spectral lenses generate a sharp image of the wavelengths of interest in the +1-st and –1-st diffraction orders. The spectral lenses are convenient to use for obtaining remotely sensed vegetation index images instead of full-fledged hyperspectral images. We discuss the design and fabrication of spectral diffractive lenses for measuring vegetation indices, which include a Modified Red Edge Simple Ratio Index and a Water Band Index. We report synthesizing diffractive lenses with a microrelief thickness of 4 µm using the direct laser writing in a photoresist. The use of the fabricated spectral lenses in a prototype scheme of an imaging sensor for index measurements is discussed. Distributions of the aforesaid spectral indices are obtained by the linear scanning of vegetation specimens. Using a linear scanning of vegetation samples, distributions of the above-said water band index were experimentally measured.

Leaf reflectance and functional traits as environmental indicators of urban dust deposition

Background How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits. Results The results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually. Conclusion In the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices

Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.

Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation

Monitoring and management of plant water status over the critical period between flowering and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.

Tolerance of Wheat to Soil Sodicity Can Be Better Detected through an Incremental Crop Tolerance Approach and Ascertained through Multiple Sowing Times

Soil sodicity is a significant crop production constraint around the world. Inherited tolerance is a precursor to pre-breeding and breeding tolerant cultivars. However, high yield per se and seasonal variability are potential limitations to identify real tolerance rather than escape correctly. To minimise this risk, we generated yield, yield components and supporting data at two times of sowing (TOS) of 15 lines representing four quadrants of a biplot from a sodic- vs. non-sodic yield dataset of 112 wheat lines trialled in the previous year. Data from sodic and non-sodic sites were investigated using three analytical approaches namely, simple ratio of yield (REI), ratio of genotypic effects (TI) after excluding site effects, and the incremental crop tolerance (ICT) reflected as deviation from regression. REI and TI produced similar results showing ninelines to be tolerant, but only four lines namely, Scepter, Condo, WA345, and WA134 passed the ICT test. The tolerance comparison at the two TOSs differentiated lines tolerant at either or both TOSs. Association of Yield-ICT with leaf tissue mineral analysis and ICT for morphological traits was genotype specific, thus not usable invariably for detection of tolerant germplasm. Hence, we conclude that (i) focussing on yield rather than yield components or tissue tests, (ii) following the ICT approach, and (iii) evaluation at multiple sowing times will provide an accurate and rigorous test for identifying inherited tolerance that breeders and physiologists can reliably use. We anticipate our suggested approach to be applicable globally across crops.

Companions and Casual Acquaintances: The Nature of Associations Among Bull Sharks at a Shark Feeding Site in Fiji

Provisioning activities in wildlife tourism often lead to short-term animal aggregations during the feeding events. However, the presence of groups does not necessarily mean that individuals interact among each other and form social networks. At the Shark Reef Marine Reserve in Fiji, several dozen bull sharks (Carcharhinus leucas) regularly visit a site, where direct feeding is conducted during tourism driven shark dives. On 3,063 shark feeding dives between 2003 and 2016, we visually confirmed the presence of 91 individual bull sharks based on external and long-lasting identification markings. We measured the intensity of associations between pairs of individuals by calculating the Simple Ratio Index (SRI) and calculated Generalized Affiliation Indices (GAIs) to distinguish true associations between dyads from structural predictor factors. Although the resulting mean SRIs were low, ranging from 0.01 to 0.12 (SRImean = 0.06; mean SRImax = 0.21), preferred long-term companionships were observed between individuals. Avoidances were also observed within pairs of individuals during the second half of the study. The best fitting model describing the temporal association patterns of bull sharks revealed a social structure which is characterized by preferred companionships and casual acquaintances. Our results suggest that the aggregation resulting from direct feeding has served to facilitate the development of social associations.

Energy Benchmarking And Energy Saving Assessment In High-Rise Multi-Unit Residential Buildings

The purpose of energy benchmarking is to promote efficient use of energy. Knowing that the energy used by a building is excessive is the first step in making positive changes. Based on an energy benchmark, one can estimate the potential in energy and cost savings when pursuing better performance. This thesis developed weather normalized energy benchmarking of 45 gas-heated high-rise multi-unit residential buildings (MURBs) in Toronto. The weather normalized annual energy consumption (NAC) was calculated by the PRInceton Scorekeeping Method (PRISM). The NACs are in the range from 242 to 453 kWh/m The NACs, calculated by the simple ratio weather normalization (SRWN) method and ENERGY STAR® Portfolio Management (PM) method were comparable to PRISM results. However, the SRWN method tends to overestimate the energy saving by 23% while PM underestimates it by 21%.

Energy Benchmarking And Energy Saving Assessment In High-Rise Multi-Unit Residential Buildings

The purpose of energy benchmarking is to promote efficient use of energy. Knowing that the energy used by a building is excessive is the first step in making positive changes. Based on an energy benchmark, one can estimate the potential in energy and cost savings when pursuing better performance. This thesis developed weather normalized energy benchmarking of 45 gas-heated high-rise multi-unit residential buildings (MURBs) in Toronto. The weather normalized annual energy consumption (NAC) was calculated by the PRInceton Scorekeeping Method (PRISM). The NACs are in the range from 242 to 453 kWh/m The NACs, calculated by the simple ratio weather normalization (SRWN) method and ENERGY STAR® Portfolio Management (PM) method were comparable to PRISM results. However, the SRWN method tends to overestimate the energy saving by 23% while PM underestimates it by 21%.

Energy constraints on brain network formation

Energy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.