Green Open Space and Barren Land Mapping for Flood Mitigation in Jakarta, the Capital of Indonesia

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%

Functional Traits Affecting Photosynthesis, Growth, and Mortality of Trees Inferred from a Field Study and Simulation Experiments

abstract: Functional traits research has improved our understanding of how plants respond to their environments, identifying key trade-offs among traits. These studies primarily rely on correlative methods to infer trade-offs and often overlook traits that are difficult to measure (e.g., root traits, tissue senescence rates), limiting their predictive ability under novel conditions. I aimed to address these limitations and develop a better understanding of the trait space occupied by trees by integrating data and process models, spanning leaves to whole-trees, via modern statistical and computational methods. My first research chapter (Chapter 2) simultaneously fits a photosynthesis model to measurements of fluorescence and photosynthetic response curves, improving estimates of mesophyll conductance (gm) and other photosynthetic traits. I assessed how gm varies across environmental gradients and relates to other photosynthetic traits for 4 woody species in Arizona. I found that gm was lower at high aridity sites, varied little within a site, and is an important trait for obtaining accurate estimates of photosynthesis and related traits under dry conditions. Chapter 3 evaluates the importance of functional traits for whole-tree performance by fitting an individual-based model of tree growth and mortality to millions of measurements of tree heights and diameters to assess the theoretical trait space (TTS) of “healthy” North American trees. The TTS contained complicated, multi-variate structure indicative of potential trade-offs leading to successful growth. In Chapter 4, I applied an environmental filter (light stress) to the TTS, leading to simulated stand-level mortality rates up to 50%. Tree-level mortality was explained by 6 of the 32 traits explored, with the most important being radiation-use efficiency. The multidimentional space comprising these 6 traits differed in volume and location between trees that survived and died, indicating that selective mortality alters the TTS.Dissertation/ThesisDoctoral Dissertation Biology 201

Vulnerabilidad del diámetro de ciertas familias de grafos

En este trabajo hemos realizado un estudio completo sobre la vulnerabilidad del diámetro de dos familias de grafos:Los grafos impares y los n-cubo plegados. En el caso de los grafos impares, hemos probado que la eliminación de cualquier conjunto de vértices o ramas de cardinalidad k menor que el grado incrementa el diámetro de los subgrafos resultantes a lo sumo en dos unidades.Asimismo, hemos estudiado como varían los parámetros d'k y d'k' cuando eliminamos k vértices o ramas del grafo.Análogamente, para los grafos cubo plegado hemos estudiado como varían estos parámetros cuando eliminamos k vértices o ramas del grafo, para valores de k inferiores al grado del grafo. Por los resultados obtenidos podemos afirmar que ambas familias de grafos son adecuadas para la implementación de redes de interconexión tolerantes a fallos.Otro estudio que hemos realizado en esta tesis trata sobre el diseño de redes densas fiables. Y hemos obtenido cuatro grafos (A,D,D,1) que mejoran cinco cotas presentadas en la tabla de grandes grafos (A,D,D,1)

Activities of the Jet Propulsion Laboratory

Work accomplished by the Jet Propulsion Laboratory (JPL) under contract to NASA in 1985 is described. The work took place in the areas of flight projects, space science, geodynamics, materials science, advanced technology, defense and civil programs, telecommunications systems, and institutional activities

Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data

P. 1-24Forest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also di erentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level ( = 0.85), but slightly lower accuracy when di erentiating the three burn severity classes ( = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower statistic values (0.76 and 0.63, respectively). This study revealed the e ectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managersS

Vegetation and soil fire damage analysis based on species distribution modeling trained with multispectral satellite data

Producción CientíficaForest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.Ministerio de Economía, Industria y Competitividad (project 559 AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17)Ministerio de Educación, Cultura y Deporte (grants PRX17/00234 and PRX17/00133

Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery

P. 205-217Multispectral imagery is a widely used source of information to address post-fire ecosystem management. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions, demonstrating broad transferability along the entire climatic gradient.S

Remote Sensing Identification of Black Ash (Fraxinus Nigra) in Maine Via Hyper- and Multi-Spectral Imagery

North American ash species (Fraxinus spp.) are under dire threat from the invasive pest, emerald ash borer (Agrilus planipennis, EAB). Black ash (F. nigra) has shown no resistance to EAB while its cultural and ecological importance render it irreplaceable. Traditional field forestry techniques are not suitable for the large-scale identification of individual black ash trees to facilitate conservation, thus necessitating the need for other identification and classification techniques. The objective of this research is to develop remote sensing techniques that can be used to identify ash trees, in particular black ash, at the individual tree level using both hyperspectral and multispectral data. Both general ash species identification and black ash tree identification in a low-density mixed forest using hyperspectral data have not been reported in the literature. Specifically, this study aims to use optical remote sensing data to: 1) create a pixel-based classification model for ash tree identification, 2) develop an object-based classification model for ash tree identification, and 3) use the most accurate ash tree classification model as a basis for a black ash tree classification model. Analysis of spectral differences between classes suggests that both ash in general and black ash specifically can be successfully separated from co-occurring species. Where classification models were significantly different, object-based methods performed better than pixel-based methods and Support Vector Machine (SVM) models generally outperformed Random Forest (RF). The highest accuracies were achieved using object-based methods and hyperspectral data, although multispectral data were able to successfully differentiate ash as well. Using object-based, SVM methods, black ash was successfully differentiated from co-occurring hardwood species using both hyperspectral and multispectral data, with hyperspectral data achieving 70% Producer’s and 70% User’s Accuracy for black ash and multispectral data achieving 57% and 50%, respectively. Despite relatively low sample sizes, this research presents a viable path forward with respects to black ash mapping. As this study shows, black ash can be successfully differentiated from closely related species using remotely sensed optical data. While capturing hyperspectral data is likely cost prohibitive for large-scale mapping efforts, multispectral sensors are more viable and can achieve similar results. At a minimum, the techniques presented in this research can be used to assist and guide field conservation work to locate areas of high likelihood of black ash presence so that they can be identified and informed decisions made about their preservation