A Time Series Investigation to Assess Climate Change and Anthropogenic Impacts on Quantitative Land Degradation in the North Delta, Egypt

In the current study the processes of soil deterioration over the past five decades was evaluated. Land degradation risk, status, and rate were assessed in Kafr El-Sheikh Governorate, Egypt, in 2016 using OLI and ETM (2002) remote sensing data, and soil data from 1961.A quantitative deterioration was produced based on the comparative study approach in the integrated weighted sum, weighted overlay, and fuzzy model. The parameters used were soil depth, texture, pH, EC, OM, SAR, ESP, CEC, CaCO3, BD, N, P, K. The variables were based on the measurements derived from the Universal Soil Loss Equation (USLE). The results of the implemented USLE in the GIS model-builder revealed the prevalence of severe soil deterioration processes in the region, and include four main deterioration risks: water-logging, soil compaction, salinization, and alkalization. During 2002–2016, soil sealing took place on 36,297.87 ha of the study area (9.7% of the total area). Urban sprawl was one of the most noticed problems that became apparent during the fieldwork during the inventory of land resources in the investigation area. Soil sealing is one of the hidden manifestations of desertification, and it is the implicit explanation for the lost land for the agricultural production process. The study showed that the investigated soil, as a part of north Nile Delta, is a very fragile system that needs to be protected, especially under the effect of climate change in areas overloaded with population, and because of their negative effects on soil properties. According to the results of this study, it is recommended that the same approach be applied to similar agricultural semi-arid regions to help in building a database of land resources for agricultural use that will be very useful for the decision-maker to monitor changes on agricultural lands.

Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

Investigation and Development of Post-Season Modelling of Arrow Squid in the Snares and Auckland Islands

<p>Squid fisheries require a different management approach to most fish species which are much longer living. Most squid live for around one year, spawn and then die. The result of this is an entirely new stock each year with little or no relationship of stock sizes between the years. Hence, it is difficult to set appropriate catch limits prior to the season. Currently, there is nothing set up for modelling the New Zealand squid fishery in-season or post-season. In-season management would allow for adjustments of catch limits during a season. Post-season management would provide information on how much the stock was exploited during a season (described as the escapement). I have produced an integrated model using ADMB (Automatic Differentiation Model Builder) (Fournier et al., 2011) which models length frequency data, CPUE (Catch Per Unit Effort) indices and catch weights from a season. It calculates escapement which indicates how much the fishery is currently being exploited. In running the model against data from four area and year combinations, I found the escapement calculation to be stable. The results suggest this modelling approach could be used with the current data collected for post-season modelling of the fishery. I am less confident about in-season modelling with the current data collected. The integrated model fits quite poorly to the CPUE data, suggesting some discrepancy either between the data or the assumptions made of them. Sampling from a greater number of tows is recommended to improve the length frequency data and this may also improve the ability of the model to fit both to these and the CPUE.</p>

Identification of Areas for Sustainable Settlements in Highly Conflicted Protected Areas Using ArcGIS Spatial Analyst: A Case of Chobe District, Botswana

The increasing human settlements around protected areas is a significant cause of the shrinking size of conservation areas globally. This is an alarming situation, especially in countries where protected areas significantly contribute to the National economy. The use of new tools to solve Land use conflicts that exist around protected areas is needed. This study employed the weighted overlay analysis using ecological, social and economic factors to model suitability for developing settlements in Chobe District, an international tourist destination in Northern Botswana, using ArcGIS spatial analyst. The majority of the land (83.4%) in Chobe is not suitable for settlement, while suitable areas that could for settlement remain relatively small (0.3% of the land). This study suggests that more land for settling the increasing population be created out of Chobe District to avoid encroachment on the conservation areas. The study demonstrated that ArcGIS spatial analyst model builder could be integrated into the land resource planning process of protected areas, making it a handy tool for the analysis and the identification of ecological, economic and social factors, thus establishing an excellent area for further research, especially for sustainable management of protected areas.

A Tool for the Automatic Aggregation and Validation of the Results of Physically Based Distributed Slope Stability Models

Distributed physically based slope stability models usually provide outputs representing, on a pixel basis, the probability of failure of each cell. This kind of result, although scientifically sound, from an operational point of view has several limitations. First, the procedure of validation lacks standards. As instance, it is not straightforward to decide above which percentage of failure probability a pixel (or larger spatial units) should be considered unstable. Second, the validation procedure is a time-consuming task, usually requiring a long series of GIS operations to overlap landslide inventories and model outputs to extract statistically significant performance metrics. Finally, if model outputs are conceived to be used in the operational management of landslide hazard (e.g., early warning procedures), the pixeled probabilistic output is difficult to handle and a synthesis to characterize the hazard scenario over larger spatial units is usually required to issue warnings aimed at specific operational procedures. In this work, a tool is presented that automates the validation procedure for physically based distributed probabilistic slope stability models and translates the pixeled outputs in warnings released over larger spatial units like small watersheds. The tool is named DTVT (double-threshold validation tool) because it defines a warning criterion on the basis of two threshold values—the probability of failure above which a pixel should be considered stable (failure probability threshold, FPT) and the percentage of unstable pixels needed in each watershed to consider the hazard level widespread enough to justify the issuing of an alert (instability diffusion threshold, IDT). A series of GIS operations were organized in a model builder to reaggregate the raw instability maps from pixels to watershed; draw the warning maps; compare them with an existing landslide inventory; build a contingency matrix counting true positives, true negatives, false positive, and false negatives; and draw in a map the results of the validation. The DTVT tool was tested in an alert zone of the Aosta Valley (northern Italy) to investigate the high sensitivity of the results to the values selected for the two thresholds. Moreover, among 24 different configurations tested, we performed a quantitative comparison to identify which criterion (in the case of our study, there was an 85% or higher failure probability in 5% or more of the pixels of a watershed) produces the most reliable validation results, thus appearing as the most promising candidate to be used to issue alerts during civil protection warning activities.

EKSTRAKSI DATA SUHU UDARA MENGGUNAKAN CITRA SATELIT MULTISPEKTRAL DAN MODEL BUILDER (Studi Kasus di Provinsi Jawa Timur)

Suhu udara merupakan salah satu parameter iklim yang digunakan pada berbagai analisis, diantaranya penetapan suatu kawasan menjadi lahan pertanian. Terbatasnya stasiun iklim serta penyebarannya yang tidak merata mengakibatkan sebagian wilayah di Indonesia tidak memiliki data iklim khususnya data suhu udara. Penelitian ini bertujuan untuk mengestimasi suhu udara di Jawa Timur menggunakan citra satelit multispektral dan Model Builder. Penelitian ini menggunakan citra Moderate Resolution Imaging Spectroradiometer (MODIS) perekaman tahun 2018 sampai 2020 sebanyak 121 scene dan data suhu udara hasil pengamatan pada 8 (delapan) stasiun iklim di Jawa Timur. Proses analisis diotomatisasi menggunakan tool yang dirancang pada Model Builder. Hasil penelitian menunjukkan bahwa tool yang dirancang menggunakan Model Builder dapat membangkitkan data suhu udara dari citra MODIS dengan baik. Disamping itu, suhu udara yang diestimasi dari citra MODIS memiliki akurasi yang sangat baik dibanding data hasil pengamatan pada stasiun iklim dengan tingkat penyimpangan (RB) sebesar 0,07 atau 7% dan memiliki nilai kesalahan rata-rata ME sebesar 1,85oC dan RMSE sebesar 4,25oC. Suhu udara yang diestimasi dari citra MODIS memiliki tingkat kebenaran (MBF) sebesar 0,94 atau 94% serta memiliki korelasi yang moderate dengan suhu udara hasil pengamatan pada stasiun iklim yang ditandai dengan nilai koefisien korelasi (CC) sebesar 0,43. Oleh sebab itu citra satelit MODIS dapat digunakan sebagai solusi alternatif dalam penyediaan data suhu udara di Jawa Timur khususnya pada wilayah yang tidak tersedia stasiun iklim.

Mathematical modelling of evapotranspiration by using remote sensing and data mining

Precise evaluation of evapotranspiration in an extended area is crucial for water requirement. By using remote sensing evapotranspiration algorithms, many climatological variables are needed. In case of using climatological variable measurements, many climatic stations must be established in that specific area. By using data mining method integrated with remote sensing, evapotranspiration can be calculated with high accuracy. A physical-based SEBAL evapotranspiration algorithm was modeled by GIS model builder for ET calculations. Albedo, emissivity, and Normalized Difference Water Index (NDWI) were considered as M5 decision tree model inputs. Evapotranspiration was evaluated for 3 April 2020 to 17 September 2020 and the equations were extracted in the M5 decision tree model and these equations were modeled in GIS by using python scripts for 3 April 2020 to 17 September 2020. The results make clear that the mathematical decision tree model can estimate the evapotranspiration gained by physical-based SEBAL algorithm in high accurately.

The Art and Science of Building a Computational Model to Understand Hemostasis

Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a “good” model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.

Automatically Building Diagrams for Olympiad Geometry Problems

We present a method for automatically building diagrams for olympiad-level geometry problems and implement our approach in a new open-source software tool, the Geometry Model Builder (GMB). Central to our method is a new domain-specific language, the Geometry Model-Building Language (GMBL), for specifying geometry problems along with additional metadata useful for building diagrams. A GMBL program specifies (1) how to parameterize geometric objects (or sets of geometric objects) and initialize these parameterized quantities, (2) which quantities to compute directly from other quantities, and (3) additional constraints to accumulate into a (differentiable) loss function. A GMBL program induces a (usually) tractable numerical optimization problem whose solutions correspond to diagrams of the original problem statement, and that we can solve reliably using gradient descent. Of the 39 geometry problems since 2000 appearing in the International Mathematical Olympiad, 36 can be expressed in our logic and our system can produce diagrams for 94% of them on average. To the best of our knowledge, our method is the first in automated geometry diagram construction to generate models for such complex problems.