Detailed Land Research Articles

Impacts of land cover heterogeneity and land surface parameterizations on turbulent characteristics and mesoscale simulations

The impacts of surface heterogeneity and land surface parameterization on the mesoscale processes were studied. Experiments were conducted using the Weather Research and Forecasting (WRF) model coupled with a simple (slab) land surface model (LSM), a relatively complex Noah LSM, and a land data assimilation system (LDAS) with detailed surface fields. Three heterogeneity length scales: 1, 3, and 9 km, were employed to alter land cover and land use. A series of simulations were performed over the U.S. Southern Great Plains during the summer when the soil moisture was abundant. Results indicate that both the land surface parameterizations and fine-scale surface heterogeneity affect the model simulations; and the impact of land surface parameterization is found to be more important, particularly for low frequency ( $$f<{10}^{-4} \mathrm{hz}$$ ) eddies and mesoscale circulations. Substantial variations in turbulent spectra were also noted, and the energy spectra respond nonlinearly to the changes in the heterogeneous length scales. Three important conclusions emerge: (i) more detailed land surface representation reduces uncertainty in simulations of surface fluxes via improved turbulence characterization over a heterogeneous land surface, which helps improve simulations of land–atmosphere feedbacks; (ii) the impact of land surface heterogeneity on atmospheric feedbacks can be detected in the mesoscale circulations that are roughly four times of the spatial heterogeneity scale; and (iii) the land surface heterogeneity that can influence mesoscale circulations would be a function of grid spacing in the model.

Geophysical study of subsurface structures of the area surrounding the new port of Jarjob in El-Negila-Marsa Matruh using magnetic data

ABSTRACT The purpose of the current paper is to investigate the subsurface geological active structures, through the analysis of the magnetic data, to conduct a scientific evaluation of the infrastructure of the area surrounding the new port of Jarjob that located on the northern coast about 60 km from the city of Marsa Matruh towards the city of Salloum and sited in the vicinity of the proposed area. The Magnetic data comprise the RTP aeromagnetic map as well as the resulting map from the geomagnetic survey for the part that was chosen in the proposed area. Two Overhauser Magnetometers are used to make a detailed comprehensive land magnetic survey, while the aeromagnetic map is obtained with authorisation from the General Petroleum Company. The necessary reduction-filtering phases like the daily variation, regional gradient, and the time variation are applied to obtain the total intensity anomaly map. Resulting total land magnetic intensity map is then reduced to the north magnetic pole and followed by the application of filtering linear wavelength technique to RTP aeromagnetic map and RTP land magnetic map to isolated the shallower residual components using different grid intervals. The obtained major tectonic trends of the study area show that the regional structures are controlled by the regional deeper structure taken NE-SW and E-W directions. The average calculated depth to the basement surface ranges from 2.6 to 3.2 km. So, the strength and solidity of the infrastructure of the port area, especially land magnetic survey area being studied that occupies a privileged place, which overlooks directly on the port coast and close to the main building, makes it a candidate for the establishment of industrial and economic projects and logistical services in the port.

Quality assurance and assessment framework for land cover maps validation in the Copernicus Hot Spot Monitoring activity

ABSTRACT The Copernicus High-Resolution Hot Spot Monitoring activity (C-HSM) delivers a global dataset of Key Landscapes for Conservation (KLC), which are characterized by pronounced anthropogenic pressures that require high mapping accuracy. Detailed land cover and land cover change map products are freely available through the activity and include extensive map production accuracy assessments. Without a complete understanding of the map products’ spatial, temporal and logical consistencies, quality or quantified confidence levels, usability is reduced and can affect stakeholder decision-making and the implementation of sustainable solutions. For the quantitative accuracy assessment, a stratified random sampling approach was implemented where special emphasis was placed on (i) allocation of sampling units for rare land cover change categories; (ii) effective and accurate labelling of large numbers of sampling units; (iii) accuracy and area estimation in one consistent approach; and (iv) derivation of confidence intervals for all accuracy measures and area estimates. To handle correlations, large uncertainties, and complex probability density functions, bootstrapping was applied instead of analytical equations, which are based on normality assumptions. This paper presents the Quality Assurance and Quality Control framework applied to validate all the C-HSM thematic map products. The Kundelungu-Upemba KLC product results are presented as our use case.

Time series monitoring of impervious surfaces and runoff impacts in Metro Vancouver

Urban areas are increasing rapidly worldwide, leading to widespread changes in land surfaces over time. Urbanized land cover is heterogeneous, and is characterized by a large areal proportion of manufactured impervious surfaces which are linked to ecological degradation, habitat loss, and increase in precipitation runoff leading to pollution and safety risks. Data from the Landsat series of satellites present an opportunity to characterize urban land cover and impervious surfaces, over a large spatial and temporal scale. In this study, land cover changes from 1990 to 2015 are characterized in the large metropolitan area of Metro Vancouver, Western Canada. An ordinal regression is used to link Landsat spectral data with a detailed land classification containing classes of impervious surface used by municipal planners in the region (Spearman's Rho = 0.76). The regression is then used to classify a time series of imagery where static land classifications are not available, providing a 25-year time-series of change in impervious surface area. A trend in increasing impervious surface was detected across the municipalities in the region, with an overall areal increase of 31.96%. Precipitation events were then simulated at each time step, using precipitation rates adjusted for expected changes in climate by 2050. Both runoff depths and inundated area increased over time, with a 51% increase in area inundated by at least 5 cm. Runoff depths were evaluated for each municipality in the region, and compared to projected populations for 2050 to highlight communities that may face elevated levels of runoff risk. Results show a steady increase in impervious surfaces in the region. Impacts of future extreme precipitation events vary across the region, with flat and low-lying topographies appearing to be more severely affected, along with areas containing extensive impervious development.

Accounting for land in the United States: Integrating physical land cover, land use, and monetary valuation

Land plays a critical role in both economic and environmental accounting. As an asset, it occupies a unique position at the intersection of the System of National Accounts (SNA), the System of Environmental-Economic Accounting Central Framework (SEEA-CF), and (as a spatial unit) SEEA Experimental Ecosystem Accounting (SEEA-EEA), making land a natural starting point for developing natural capital accounts more generally. We develop a pilot set of national and subnational land accounts for the United States that are consistent with the SEEA-CF and SNA principles, quantified in both physical and monetary terms. The physical accounts utilize detailed land use (National Land Use Database) and land cover (National Land Cover Database) datasets, which provide insights into how land cover in the U.S. is changing over time. To provide aggregate estimates of land values, we use a hedonic approach that exploits fine-grain microdata (“big data” from Zillow) that contains detailed information from hundreds of millions of property transactions and their corresponding physical characteristics covering much of the U.S. Methodologically, we show that it is feasible to produce monetary accounts for land that can be directly linked to and integrated with physical land cover/use. Overall, U.S. land cover has shown declines in forests, cropland, and pasture with increases in barren, scrub/shrub, and developed classes, which are particularly concentrated in the U.S. Southeast. Nominal land values in the U.S. fell about 28% ($7 trillion) from the boom to bust periods in the prior decade, albeit with substantial regional variation, and have subsequently experienced a nearly full recovery in recent years. We estimate private land in the contiguous 48 states to be worth approximately $25.1 trillion in 2016.

Object-based island hierarchical land cover classification using unmanned aerial vehicle multitype data

The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island mapping. Nine classification models were established through a fusion method of visible, multispectral, and light detection and ranging (LIDAR) data acquired by UAVs. A two-level hierarchical land cover classification (level 1 and level 2) of the Donkey Island in China was performed using geographic object-based image analysis with random forest classifier. We investigated the performance of land cover classification models containing different sets of features (spectral, height, intensity, and shape features extracted from UAV data) and evaluated the importance of various features. The results demonstrate that the overall accuracy (OA) of the models generally increase with decreasing detail and the amount of information entering the classification process. The OA achieved range from 82.08% to 92.54% and 74.12% to 85.08% across the nine models for level 1 and level 2, respectively. The best result was achieved with a model combining all features based on multispectral and LIDAR data. Height and intensity information input significantly affect the quality of classification models positively, with height apparently more significant than LIDAR information. When comparing different features, spectral features prominently assist in discriminating land cover classes. The importance of height and intensity features to classification accuracy varies for the classification models, showing greater importance in models based on visible data.

Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine

Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE.&#x0D; The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin.&#x0D; The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data.

The Agroforestry Heritage System of Sabana De Morro in El Salvador

Traditional agroforestry systems are recognized as having great importance for providing multiple benefits for local communities all over the world, especially in tropical countries. Thanks to their multifunctional role, they can support small farmers, contribute to hydrogeological risk reduction, water regulation, preservation of soil, agrobiodiversity and landscape, as well as being examples of mitigation and adaptation towards climate change. The Globally Important Agricultural Heritage Systems (GIAHS) programme of the Food and Agriculture Organization (FAO) aims to identify agricultural systems of global importance, preserving landscape, agrobiodiversity and traditional knowledge, through dynamic conservation principles. The Sabana de Morro is a traditional agroforestry system located in El Salvador based on cattle grazing in pastures with the presence of Crescentia alata and Crescentia cujete trees, locally called Morro or Jícaro. We documented the main characteristics of this system, that has never been deeply studied, in the Municipality of Dolores, in accordance with the five GIAHS criteria, and through detailed land use mapping, to assess the relations between landscape structure, agrobiodiversity and traditional silvopastoral practices. Sabana de Morro proved to be based on strong interactions between trees, cattle and farmers. The pulp of the Morro fruits is eaten by grazing cattle, completing their feeding and giving a peculiar taste to the locally produced cheese. Morro trees provide shade for the animals while cattle contribute by spreading their seeds that also take advantage of the manure. Results show that this agroforestry system contributes to the preservation of a rich agrobiodiversity and of the traditional landscape. At the same time, it supports local farmers’ livelihood and is consistent with the aim of the GIAHS programme, even if further surveys and research are needed to assess the real possibility of the inclusion in this FAO programme.

Türkiye'deki Büyükşehir Belediyeli Şehirlerde Kentsel Yayılma

Hızlı nüfus artışı ve teknoloji ile birlikte dünyada kentlerde yaşayan kişi sayısı gün geçtikçe atmaktadır. Artan şehirleşme hareketleri doğal kaynaklar üzerindeki baskıyı giderek artırmakta, kentlerin mekânda kontrolsüz, plansız bir şekilde yayılmasına neden olmaktadır. Kentsel yayılma pek çok çevresel, sosyal ve ekonomik problemlere yol açmaktadır. Bu nedenle bu çalışmada Türkiye'deki büyükşehir belediye örgütlü şehirler arasında kentsel yayılmanın düşük ve yüksek olduğu alanları tespit etmek amaçlanmıştır. Belirtilen amaç doğrultusunda Avrupa Çevre Ajansı'nın geliştirdiği şehir atlası (urban atlas) veri tabanının 2012 yılı verileri kullanılmıştır. Şehir atlası Avrupa Birliği'ne üye ve aday ülkelerdeki nüfusu 100 binin üzerindeki şehirlere ait ayrıntılı arazi örtüsü/kullanım veri seti sağlamaktadır. Kentsel alanlar için 20 farklı sınıfın tanımlandığı veri tabanında kentsel dokuyu tanımlayan 6 sınıf kentsel yayılma yoğunluğunu tespit etmede ana değişken olarak kullanılmıştır. Şehirlerin nüfus büyüklükleri bir diğer değişkeni oluşturmuş, ayrıca mekânsal metriklerden biri olan kentsel doku sınıflarının yama büyüklükleri de incelenerek yayılmanın yoğunluğu, nerelerde fazla olduğu ortaya konulmuştur. Çalışma sonucunda kentsel yayılma ile nüfus büyüklüğü arasında önemli bir ilişki olduğu saptanmıştır. Büyükşehir belediye örgütünün kuruluşunun daha eskiye dayandığı şehirlerde de kentsel yayılmanın daha yoğun olduğu görülmüştür. Kentsel yoğunluğun yüksek olduğu arazi sınıfları genellikle kent merkezinde veya merkeze yakın yerlerde bulunmakta, bu araziler büyük metropollerde daha fazla alan kaplamaktadır.

Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou

Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.

A spatiotemporal structural graph for characterizing land cover changes

ABSTRACT Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena.

An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses

Land use change (LUC) modelling has been widely used to inform landscape planning and adaptive management practices. The validation of LUC modelling results is critical for justifying the usability of LUC models. Along this line, global-level accuracy assessments with the kappa and error matrix approach are accepted as the common method for the validation of LUC models. However, high global-level accuracy does not always guarantee good model performance and high accuracy in characterizing the local LUC. It is necessary to develop indicators to exactly assess the accuracy of land use modelling in characterizing the detailed land use change. In this study, both hypothetical and real landscapes are used to analyze the differences between global-level and local accuracy assessments, and all possible simulation scenarios are considered in the hypothetical landscape by exhaustive methods. The results derived from the hypothetical landscape show that the local accuracy tends to increase with the increase in the proportion of the regional area showing land use transitions throughout the landscape. A real landscape simulation by the Dyna-CLUE model in the middle reaches of the Heihe River Basin (M-HRB) in China also showed a similar trend, where the land use transition from 2000 to 2015 accounted for approximately 10% of the total area. The simulation results showed high global-level accuracies of 97.17% (2010) and 85.01% (2015). The local accuracies for regions with little land use transition were 98.45% (2010) and 96.56% (2015), but the local accuracies for regions with significant land use transitions were only 0.99% and 6.08% in 2010 and 2015, respectively. According to the global-level accuracy, LUC simulations are reliable, but they do not correctly reflect the local changes in regions with significant land use transitions. Therefore, both global-level accuracy and local accuracy should be used to avoid possible misleading LUC modelling results. An effective simulation and accuracy assessment procedure was proposed in this study to increase the credibility of LUC modelling.

Characterising urban green space density and footpath-accessibility in models of BMI

BackgroundWhile exposure to urban green spaces has been associated with various physical health benefits, the evidence linking these spaces to lower BMI, particularly among older people, is mixed. We ask whether footpath availability, generally unobserved in the existing literature, may mediate exposure to urban green space and help explain this volatility in results. The aim of this study is to add to the literature on the association between urban green space and BMI by considering alternative measures of urban green space that incorporate measures of footpath availability.MethodsWe conduct a cross-sectional study combining data from The Irish Longitudinal Study on Ageing and detailed land use information. We proxy respondents’ exposure to urban green spaces at their residential addresses using street-side and area buffers that take account of the presence of footpaths. Generalised linear models are used to test the association between exposure to several measures of urban green space and BMI.ResultsRelative to the third quintile, exposure to the lowest quintile of urban green space, as measured within a 1600 m footpath-accessible network buffer, is associated with slightly higher BMI (marginal effect: 0.80; 95% CI: 0.16–1.44). The results, however, are not robust to small changes in how green space is measured and no statistically significant association between urban green spaces and BMI is found under other variants of our regression model.ConclusionThe relationship between urban green spaces and BMI among older adults is highly sensitive to the characterisation of local green space. Our results suggest that there are some unobserved factors other than footpath availability that mediate the relationship between urban green spaces and weight status.

Impact of Lithofacies and Structures on the Hydrogeochemistry of the Lower Miocene Aquifer at Moghra Oasis, North Western Desert, Egypt

The Moghra Oasis is one of the major Egyptian desert-reclamation projects. Understanding the hydrochemistry of the Moghra aquifer is an onus to properly plan and manage the agricultural activities in the North Western desert of Egypt. The basic geologic structure of the area under study was deduced from a detailed land magnetic survey. The resulted magnetic field map was analyzed using different filtering techniques and tomographic inversion. Field observation, stratigraphic setting, sedimentological studies, microfacies analyses and lithologic log of the studied wells were integrated to detect the lithofacies characteristics of the Moghra aquifer. Strong vertical and lateral variations of channel-fill and flat-laminated lithofacies beside several types of diagenetic processes caused the heterogeneity of the Moghra aquifer. The direction of groundwater flow in the heterogeneous Moghra aquifer is controlled by a fault system deduced from the magnetic study. Twenty groundwater samples from around Moghra Lake were collected and were analyzed hydrochemically for major ions and trace elements. The microfacies analysis (primary components and diagenetic processes), spatial variance in pH and ions concentrations, statistical and hydrogeochemical classifications, ionic ratios and relationships and saturation indices were helpful to understand the groundwater hydrogeochemical processes. The main hydrochemical type was Na–Cl. Ion-exchange due to seawater intrusion, sulfate ion reduction, iron-oxides reduction and mineral dissolution were the detected water–rock interaction processes. Dolomite precipitation was also proven. Iron oxides, quartz and carbonate minerals precipitation in the upper saline groundwater were expected. More mineral dissolution for evaporate minerals, silicate minerals and carbonate minerals in the lower saline groundwater is expected to occur.

Influences of the Transaction Intention of Farmland Transfer under Information Asymmetry: An Empirical Study of 1100 Questionnaires from China

The original intention of the tripartite rural land entitlement system was to activate farmland management rights and improve the efficiency of land element allocation. However, information asymmetry is prevalent in a market with imperfect competition. We analyzed the key factors affecting farmland transfer transactions with the structural equation model using survey data from farmers in five major grain-producing counties in Henan province, China. The purpose of our study was to investigate how these factors contribute to the avoidance of a “lemon market” emerging in farmland transfer and can promote the market regulation of farmland transfer operations. This study was conducted under the framework of asymmetric theory analysis with the purpose of discussing the fundamental forces driving farmland transfer transactions. The results show that (1) the economic characteristics of farmers play an important role in the formation of farmland transfer transactions, and their impacts on the willingness to transfer farmland in and out were 0.69 and 0.97, respectively; and (2) among the observable variables, the satisfaction degree of agricultural technology training and the proportion of non-agricultural income had a strong ability to explain the choice of farmland transfer behavior, and the factors that strongly impacted farmland transfer out behavior were physical condition and farmland quality. Thus, we recommend establishing a detailed land transfer pricing system based on the background quality of farmland and increasing the agricultural science and technology education, focusing on dredging information transmission channels to fulfill the new requirements of the three-right separation system on the government’s supervision of farmland transfer to promote effective links between small-scale peasant economies and modern agriculture.

Modeling the urban heat island mitigation effect of cool coatings in realistic urban morphology

There are currently more than 400 cities that are subject to the urban heat island (UHI) effect, whose summer temperature can be over 15 °C above the human thermal comfort zone. As the scope of urbanization expands, more people will feel the influence of the UHI effect. Since cool coatings can serve as a mitigation measure against the UHI effect, this research proposes a method that can estimate its mitigation effect in any given region. The main idea of this method is to simulate the function of cool coatings via increasing the albedo values in the Weather Research and Forecasting (WRF) model. The main novelty of this method is that it incorporates detailed land categorization data to simulate realistic urban morphology for the purpose of improving model performance. To demonstrate the feasibility of the proposed method, the UHI mitigation effect of cool coatings was estimated in the city of Sydney during two consecutive sweltering days (7–8 January 2018) via the WRF model. The results showed that the proposed method fulfilled its purpose. To be specific, as the consequence of a 0.35 albedo increase in urban Sydney, the whole urban area will be subject to an average temperature decrease of 0.76 °C, while some regions will experience a temperature decrease as great as 5.71 °C during the hottest hour. However, this value for a given zone, such as the downtown coast area, was closely related to the local wind directions. The results also showed that the values of different urban canopy parameters could be treated as auxiliary information of WRF modeling results and used to identify the locations that suffered the most from the UHI effect. Therefore, the proposed method can help decision-makers and stakeholders to better analyze the UHI mitigation potential of cool coatings. Additionally, it indicates incorporating detailed land categorization data is an effective way of improving UHI numerical simulations.

Girit’in “Hakk ve Adl ile Cedîden Tahrîri”: 1705 Yılında Girit’te Yapılan Tahrirler ve Düzenlemeler

Osmanlı topraklarına geç bir tarihte katılan Girit Eyaleti'nde 1650 ve 1670 tarihlerinde iki kez genel tahrirler yapılmıştır. Fakat 18. yüzyılın başından itibaren İmparatorluk genelinde yaşanan sorunlar, adada yaşanan ekonomik problemler ve yeni düzenlemeler bu tarihte adada üçüncü kez yeni bir tahriri zorunlu kılmıştır. 1704 yılının sonlarında başlayan tahrir 1706 yılının Ocak ayında tamamlanmıştır. Bunun Girit'te yapılan son tahrir olduğu anlaşılmaktadır. Ankara Tapu Kadastro Arşivi'nde bulunan üç defterin ilki Kandiye ve Estiya, ikincisi de Hanya ve Resmo sancakları mufassal defterleridir. Üçüncü defter ise kale ve müstahfızan defteridir. 1705 yılına tarihleyeceğimiz bu düzenlemelerle vergi organizasyonu bazı konularda 1670 düzenlemeleri gibi devam ettirilmiş bazı konularda 1650 düzenlemelerine geri dönülmüştür. Toprağın, servetin kaynağı olduğu esasına dayalı organizasyonu esas alınmış, geniş özel mülkiyete müsaade eden haracî toprak uygulaması devam etmiştir. 18. yüzyılın başlarından itibaren Osmanlı genelinde tımarların önemli ölçüde küçüldüğü ve tımar topraklarının padişah haslarına aktarıldığı görülmektedir. Nitekim Girit'te de böyle olmuş ve bu tahrirde çok sayıda köy "cedîd hass-ı hümayûn" olarak defterlere kaydolmuştur. Girit'e ait bu son tahrirde dönemin ruhuna uygun olarak dinsel bir üslup kullanılmıştır.

Addressing the spatial disconnect between national-scale total maximum daily loads and localized land management decisions.

Regulatory watershed mitigation programs typically emphasize widespread adoption of best management practices (BMPs) to meet total maximum daily load (TMDL) goals. To comply with the Chesapeake Bay TMDL, jurisdictions must develop watershed implementation plans (WIPs) to determine the number and type of BMPs to implement. However, the spatial resolution of the bay‐level model used to determine these load reduction goals is so coarse that the regulatory plan cannot consider heterogeneity in local conditions, which affects BMP effectiveness. Using the Topo‐SWAT modification of the Soil and Water Assessment Tool (SWAT), we simulated two BMP adoption scenarios in the Spring Creek watershed in central Pennsylvania to determine if leveraging fine‐scale spatial heterogeneity to place BMPs could achieve the same (or better) nutrient and sediment reduction at a lower cost than the state‐level WIP BMP adoption recommendations. Topo‐SWAT was initialized with detailed land use and management practice information, systematically calibrated, and validated against 12 yr of observed data. After determining individual BMP cost effectiveness, results were ranked to design a cost‐effective BMP adoption scenario that achieved equal or greater load reduction as the WIP scenario for 74% of the cost using eight management‐based BMPs: no‐till, manure injection, cover cropping, riparian buffers, land retirement, manure application timing, wetland restoration, and nitrogen management (15% less N input). Because watersheds of this size typically represent the smallest modeling unit in the Chesapeake Bay Model, results demonstrate the potential to use watershed models with finer inference scales to improve recommendations for BMP implementation under the Chesapeake Bay TMDL.

Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery

Overcoming the obstacle of frequent cloud coverage in optical remote sensing data is essential for monitoring dynamic land surface processes from space. APiC, a novel adaptable pixel-based compositing and classification approach, is especially designed to use high resolution spatio-temporal space-borne data.Here, pixel-based compositing is used separately for training data and prediction data. First, cloud-free pixels covered by reference data are used within adapted composite periods to compile a training dataset. The compiled training dataset contains samples of spectral reflectances for respective land cover classes at each composite period. For land cover prediction, pixel-based compositing is then applied region-wide. Multiple prediction models are used based on temporal subsets of the compiled training dataset to dynamically account for cloud coverage at pixel level. Thus we present a data-driven classification approach which is applicable in regions with different weather conditions, species composition and phenology.The capability of our method is demonstrated by mapping 19 land cover classes across Germany for the year 2016 based on Sentinel-2A data. Since climatic conditions and thus plant phenology change on a large scale, the classification was carried out separately in six landscape regions of different biogeographical characteristics. The study drew on extensive ground validation data provided by the federal states of Germany.For each landscape region, composite periods of different lengths have been established, which differ regionally in their temporal arrangement as well as in their total number, emphasising the advantage of a flexible regionalised classification procedure. Using a random forest classifier and evaluating outcomes with independent reference data, an overall accuracy of 88% was achieved, with particularly high classification accuracy of around 90% for the major land cover types. We found that class imbalances have significant influence on classification accuracy. Based on multiple temporal subsets of the compiled training dataset, over 10,000 random forest models were calculated and their performance varied considerably across and within landscape regions. The calculated importance of composite periods show that a high temporal resolution of the compiled training dataset is necessary to better capture the different phenology of land cover types.In this study we demonstrate that APiC, due to its data-driven nature, is a very flexible compositing and classification approach making efficient use of dense satellite time series in areas with frequent cloud coverage. Hence, regionalisation can be given greater focus in future broad-scale classifications in order to facilitate better integration of small-scale biophysical conditions and achieve even better results in detailed land cover mapping.

Transpiration from subarctic deciduous woodlands: Environmental controls and contribution to ecosystem evapotranspiration

AbstractPotential land–climate feedbacks in subarctic regions, where rapid warming is driving forest expansion into the tundra, may be mediated by differences in transpiration of different plant functional types. Here, we assess the environmental controls of overstorey transpiration and its relevance for ecosystem evapotranspiration in subarctic deciduous woodlands. We measured overstorey transpiration of mountain birch canopies and ecosystem evapotranspiration in two locations in northern Fennoscandia, having dense (Abisko) and sparse (Kevo) overstories. For Kevo, we also upscale chamber‐measured understorey evapotranspiration from shrubs and lichen using a detailed land cover map. Subdaily evaporative fluxes were not affected by soil moisture and showed similar controls by vapour pressure deficit and radiation across sites. At the daily timescale, increases in evaporative demand led to proportionally higher contributions of overstorey transpiration to ecosystem evapotranspiration. For the entire growing season, the overstorey transpired 33% of ecosystem evapotranspiration in Abisko and only 16% in Kevo. At this latter site, the understorey had a higher leaf area index and contributed more to ecosystem evapotranspiration compared with the overstorey birch canopy. In Abisko, growing season evapotranspiration was 27% higher than precipitation, consistent with a gradual soil moisture depletion over the summer. Our results show that overstorey canopy transpiration in subarctic deciduous woodlands is not the dominant evaporative flux. However, given the observed environmental sensitivity of evapotranspiration components, the role of deciduous trees in driving ecosystem evapotranspiration may increase with the predicted increases in tree cover and evaporative demand across subarctic regions.