Land Mapping Research Articles

Gridded soil surface nitrogen surplus on grazing and agricultural land: Impact of land use maps

Excess N application on agricultural land greatly impacts the environment in multiple ways, driven by population growth and improving quality of human diets. Therefore, it is essential to quantify the sources of the emissions of N compounds and their determinants (e.g. biological N fixation (BNF), mineral fertilizer, manure N and N deposition) to develop adequate mitigation measures. Here we aim at comprehensively mapping and quantifying N fluxes on agricultural land to analyze these sources on different scales. As underlying grazing land maps used for such calculations are fairly different in terms of methodology and definition and thus spatial extent and pattern, we investigate how this diversity in grazing land maps affects quantification of N indicators. We compared three different global grazing land maps and analyzed the propagation of differences to discrepancies in N indicators calculated from them. We discovered that (i) area differences propagated to high discrepancies in N surplus mostly in Asia, and to a minor extent also in Europe and Northern Africa. (ii) BNF constitutes an important translator for differences on grazing land to N indicators, while also being a source of further uncertainty, which warrants further scrutiny. (iii) A more inclusive definition of grazing land results in overall less N surplus given the larger areas included but allows to provide a more comprehensive estimate of the influence of human activity on the N cycle. This study is the first to provide an in-depth analysis of the effect of grazing land and agricultural land area differences on various N budget terms and N indicator calculation, highlighting opportunities for further research, and the importance of a comprehensive accounting of N surplus when using an inclusive definition of grazing land.

The Impact of Tidal Flooding on Decreasing Land Values in the Areas of Tugu District, Semarang City

Semarang City as one of the areas on the north coast has a serious problem related to tidal flooding. The impact of this disaster has implications for changes in land use, a decrease in environmental quality and health, the emergence of slum settlements, a decrease in income and also a decrease in land value. This study aims to map the impact of tidal flooding on changes in land values based on the Land Value Zone Map (ZNT) and map land prices based on spatial data analysis. The study was carried out through spatial analysis by overlaying (join intersection) the 2014, 2016, 2018 and 2019 ZNT maps to determine changes in land value, while mapping land prices, especially in Mangunharjo Village, was based on land use maps, positive accessibility (road network) and negative accessibility (prone flood rob). The results of the study show that land which is permanently affected by tidal flooding and cannot be used anymore makes it a lost / destroyed land, while periodically inundated land has experienced a price decline in the range of Rp 100.000 – 200.000, -/m2. Meanwhile, the results of the study from the ZNT map for 2014 to 2019 show a very significant difference in price between zone 1 and a price increase of ± Rp 3.500.000; zone 2 price increase ± Rp 575.000, -, zone 3 at a price range of Rp 385.000, and zone 4 as the tidal flood prone zone only experienced an increase of Rp 250.000,-. In this context, the variable of tidal flooding vulnerability greatly affects the stagnation of land prices and even decreases in land prices, while the positive accessibility variable is the location of land on national and local roads that has experienced a very high price increase.

Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

Land use and land cover (LULC) classification mapping is important for evaluating, monitoring, protecting and planning for land resources. A key factor in extracting desired information from satellite images is choosing the right the spatial resolution. The scale of a pixel on the ground is known as spatial resolution. A pixel is the smallest ‘dot' that makes up an optical satellite image which defines the level of detail as in image. In this paper estimation of the areal extent of water, built up, barren land, vegetation land and fallow land classes with its classification accuracy were reviewed particularly for January 2013 and November 2016 in Karmala tehsil of Solapur district, India. LULC is implied by different spatial resolution images of Advanced Wide Field Sensor (AWiFS), Linear Imaging Self Scanning Sensor (LISS-III), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A imageries in QGIS environment while the classification was carried out using the maximum likelihood algorithm (MLA). The classified maps obtained from AWiFS and LISS-III sensors, as well as Sentinel-2A and Landsat-8 OLI data sets, were compared separately. Spatial analysis depicts that the Kappa coefficient of Sentinal-2A, Landsat-8, LISS III and AWiFS was found 96.96%, 91.64%, 87.30% and 89.36%. Furthermore, overall accuracy of was found to be 99.07%, 94.49%, 89.84% and 94.08% respectively. The accuracy of the classified image with higher spatial resolution (Sentinal-2A) proved more informative than that of lower resolution (AWiFS) sensor. On the response, the finer spatial resolution of Sentinal-2A (10 m) delivered more precise details and enhanced LULC classification accuracy most reliably than the coarser spatial resolution of Landsat-8 (30m), LISS III (23m) and AWiFS (56m) image. A perusal of data revealed that the overall accuracy and Kappa coefficient was found proportionate to spatial resolution of satellite imageries. The higher resolution spatial data also greatly reduces the mixed-pixel problem. The study revealed that the spatial resolution plays an important role and affects classification details and accuracy of LULC level.

A new dataset of global irrigation areas from 2001 to 2015

About 40% of global crop production takes place on irrigated land, which accounts for approximately 20% of the global farmland. The great majority of freshwater consumption by human societies is associated with irrigation, which contributes to a major modification of the global water cycle by enhancing evapotranspiration and reducing surface and groundwater runoff. In many regions of the world irrigation contributes to streamflow and groundwater depletion, soil salinization, cooler microclimate conditions, and altered land-atmosphere interactions. Despite the important role played by irrigation in food security, water cycle, soil productivity, and near-surface atmospheric conditions, its global extent remains poorly quantified. To date global maps of irrigated land are often based on estimates from circa year 2000. Here we apply artificial intelligence methods based on machine learning algorithms to satellite remote sensing and monthly climate data to map the spatial extent of irrigated areas between 2001 and 2015. We provide global annual maps of irrigated land at ≈9km resolution for the 2001-2015 and we make this dataset available online.

Mapping Land Suitability to Guide Landscape Restoration in the Amazon

Beyond reducing deforestation, the control of forest degradation, the promotion of forest restoration, and the improvement of agricultural practices in the Brazilian Amazon are becoming increasingly important for sustainable development. To enable farmers and authorities to organize their landscapes and optimize both agricultural practices and the provision of ecosystem services, mapping land suitability is essential, but it is lacking in the region. In this paper, we present a method for mapping land suitability at a fine scale (30-m pixels), adapted to the needs of farmers and municipalities, to not only optimize agricultural production but also the ecosystem services provided by forests. We used topographic data from the Brazilian municipality of Paragominas to produce four maps, one each of soil texture, slope, floodplains, and hydrography, that we then combined into a single land suitability map. This map has been incorporated into a spatial database, which also contains information on land use, remoteness, and land tenure. We performed spatial analyses to measure the process of land use change, and to define indicators that enable local stakeholders to organize landscape restoration. We highlight an organic link between agricultural intensification and forest restoration, and provide a spatial tool for landscape design, assessment, and monitoring.

Mapping and monitoring land dynamic due to urban expansion using geospatial techniques on South Kolkata

Mapping and monitoring land dynamic due to urban expansion using geospatial techniques on South Kolkata

Tiga Dihaji Dam Access Route Plan Alternative using Geographic Information System (Cost Distance Method)

The Tiga Dihaji dam access route function is destined for heavy equipment during the dam construction period. It is located on Tiga Dihaji sub-district, Ogan Komering Ulu Selatan district, South Sumatera, Indonesia. The Tiga Dihaji access route supervisor stated that the planning justification was done using expert judgement based on contour map and local land cover. This method is considered less measurable. Therefore, the Geographic Information System method is offered in this paper to determine the most suitable Tiga Dihaji dam access route. The parameters considered are landslide vulnerability, land cover and construction factors. These parameters are overlaid based on weights determined by the Public Works Department then validated by expert judgement. The new route is proposed using the cost distance method. Based on the analysis, it can be concluded that the cost distance method is more measurable than the planned method, but this method produces a less suitable route in terms of civil engineering.

Pemetaan Status pH Tanah dan C-Organik Tanah Sawah Tadah Hujan di Kecamatan Panai Tengah Kabupaten Labuhanbatu

<p>The production center for lowland rice in Labuhanbatu Regency in the last three years, precisely in Panai Tengah District, North Sumatra Province, has decreased production since 2018. One of the causes is a decrease in production because the nutrient content in the soil has decreased and added by fertilizer application by farmers. Lowland rice is still common due to limited data on land resources. This study aims to evaluate the land through the identification of classes, distribution and status of soil pH and C-Organic nutrients as well as organic matter in agricultural land in Central Panai District, and to determine the level of soil damage in each Land Mapping Unit. This research was carried out in Central Panai District with elevation. place 11 meters above sea level. Soil analysis was carried out in the integrated science laboratory of the Faculty of Science and Technology, Labuhanbatu University from January 2021 to March 2021, while the method in this study was a semi-detailed grid survey with the density of observation of 1 sample per 250 meters. Soil pH measurements used the pH-H<sub>2</sub>O and C-organic methods of soil using the Walkley and Black method. Furthermore, the results of the analysis of nutrient rainfed lowland soils are interpreted into a nutrient status map. The results showed that the pH content of rainfed lowland soil in Panai Tengah District, Labuhanbatu Regency was classified as very acidic and slightly acidic, while based on the soil nutrient status, namely C-organic and organic matter in the low category, so as to increase the productivity of rainfed lowland soil with soil organic matter content.up to 3% required organic fertilizer in the Central Panai District, Labuhanbatu Regency.</p>

Urban Land Mapping Based on Remote Sensing Time Series in the Google Earth Engine Platform: A Case Study of the Teresina-Timon Conurbation Area in Brazil

Teresina-Timon conurbation (TTC) area is an example of urban agglomeration, situated in the semiarid environment of the northeast region of Brazil, which has shown an accelerated process of urban development over the last four decades (1985–2019). In this study, we developed a semi-automatic urban land mapping framework at the Google Earth Engine (GEE) platform to (a) evaluate spatiotemporal sprawl of the TTC area (1985–2018); and (b) quantify current urban fabric structures of TTC area (2019). The main empirical results demonstrate that the use of the Landsat historical dataset is a suitable option for generating consistent urban land maps across the years in semiarid environments. Teresina and Timon expanded, respectively, from 70.34 km2 and 12.20 km2 in 1985 to 159.02 km2 and 30.68 km2 in 2018, increasing annually at 3.05% and 3.69% averaged rate, showing an underlying tendency of continuous growth, and magnitude similar to Asian cities. The results of the urban fabric (UF) structures mapping demonstrates a high complexity of the urbanized surfaces, characterized by irregular shapes and variability of urban coverage. In 2019, the TTC metropolitan area was covered by urban land use classes as ceramic roofs, other types of roofs, and impervious surface, in the proportions of 28.02%, 11.97%, and 5.67%, respectively.

Mapping Land Use/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine

Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System

With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at <0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m−2 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.

Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding

Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

Лексико-семантические группы чувашских гидронимов

The article is devoted to the lexical and semantic analysis of Chuvash hydronyms. The purpose of the study is to classify the names of water bodies by lexical and semantic groups, to identify the main principles of the nomination of hydro-names. The article deals with the names of rivers and streams, lakes and ponds, swamps and swampy places, the names of springs and wells. The main material for the analysis was collected by the author during field research, data from the toponymic file of the Scientific Archive of the CSUH and various cartographic sources, maps of land stored in rural administrations of the Chuvash Republic were used. The main methods are descriptive, comparative, typological, and statistical ones. The results of the study showed that according to the lexical and semantic classification, Chuvash hydronyms are divided into two groups: 1) hydronyms that reflect the physical and geographical properties of water bodies and their environment; 2) hydronyms that have arisen as a result of practical human activity. Most of the names of water bodies are based on a specific feature of the geographical realities themselves. Their Chuvash language can be grouped into the following subgroups: 1) names containing a significant feature of the object; 2) names indicating the ground, soil; 3) names related to the plant world; 4) names associated with the animal world; 5) names indicating the location of the water body; 6) names expressed by numerical indicators; 7) figurative and metaphorical names. Hydronyms that have arisen as a result of the practical activity of a person are divided into the following subgroups: 1) hydronyms related to the life and economic life of the population; 2) hydronyms whose meanings are related to the social life and spiritual culture of the population; 3) hydronyms derived from anthroponyms; 4) hydronyms derived from ethnonyms; 5) hydronyms derived from toponyms. It is concluded that as a result of lexical and semantic analysis, it is possible to establish the principles of the nomination of hydronymes, to identify the physical and geographical characteristics of the area, to obtain new information about the material and spiritual culture of the Chuvash people.

Spatial and Temporal Pattern Analysis of Land Use in Yangtze River Delta Based on Remote Sensing and GIS in Intelligent Environment

Land use change plays an important role in regional socio-economic development and global environmental change. Whether the land is effectively and efficiently used is not only related to the income level of the people in the surrounding cities but also closely related to the local economy and national economy. Intelligent environment refers to the indoor environment with a variety of data acquisition equipment. Combined with related technologies, the reasoning and analysis of the data can be used to realize the functions of activity identification, data perception, and control. In addition, the Yangtze River Delta is an economically developed area in China, and its land use situation is related to the economic development in the next ten or even decades. Therefore, it is necessary to analyze the spatial and temporal pattern of land use in Yangtze River Delta region by remote sensing image technology and GIS in intelligent environment. Based on intelligent environment, this paper uses RS and GIS technology to interpret remote sensing image and map land use in multitemporal coastal zone. The land use dynamic degree model and spatial interpolation method were used to analyze and evaluate the spatial and temporal evolution characteristics of land use in the Yangtze River Delta region, and the landscape pattern changes in the Yangtze River Delta region were analyzed and evaluated. This study found that the land use types in the Yangtze River Delta have transformed each other, and the land use change speed is fast, which is inseparable from the rapid economic development. In the future, in addition to maintaining the rapid and stable development of industry, the rational use of limited land resources, the improvement of agricultural development short board, and the improvement of tourism economic benefits will make the economy of the Yangtze River Delta region to a new level.

Investigation on Probabilistic Model for Corrosion Failure Level of Buried Pipelines in Kirtipur Urban Areas (Nepal)

Potable water supplies mostly through buried galvanized steel and cast-iron pipes from distribution terminals to the public, and a lot of corrosion failures occurred each year in urban cities of Nepal. It is an urgent need to know the main responsible factors for such buried metallic pipeline failures and subsequently evaluate the level of corrosion risk in soils of presently studied Kirtipur urban areas. Six factors (i.e., pH, moisture, resistivity, oxidation–reduction potential-ORP, chloride, and sulfate ions) of fifty-three soil samples were determined using American Standard for Testing and Materials (ASTM) standard. It estimates 6.4–7.9 pH, 7–45% moisture, 4.5 × 103–45.5 × 103 Ohm.cm resistivity, 317–514 mV ORP, 12–86 ppm chloride, and 40–294 ppm sulfate ions in the samples, indicating the soils of the Kirtipur urban areas could classify mostly into mildly corrosive and less corrosive groups to the buried galvanized steel and cast-iron pipes. Furthermore, a new probabilistic corrosion failure model is proposed for the study of the soil corrosivity level more precisely based on sub-corrosion groups by considering the experimental data of six soil factors. Present findings would be insightful for corrosion mapping of soil lands to study the underground pipeline works in the future.

A novel hybrid approach using SVM and spectral indices for enhanced land use land cover mapping of coastal urban plains

In present study, Landsat images of years 2001, 2009 and 2018 are used for LUC mapping of a coastal-urban-floodplain wherein built-up and coastal-barren classes have been identified to be the most confusing classes for interpretation. Otsu’s thresholding techniques have been used for mapping of waterbodies, built-up, and coastal-barren lands. The performance of most commonly used built-up indices have been assessed, among which BCI performed best for the study area. A new index, called Coastal-Barren-Index (CBI), has been developed using the spectral characteristics of SWIR1 and green spectral reflectance bands. A critical comparison of SVM and RFC classifiers are reported, and, finally, a hybrid approach is proposed as a combination MNDWI-CBI-SVM for mapping of the study area with Overall Accuracy 90.5% and Kappa value 0.87. The proposed approach is validated for an independent site, and, can be considered as generic in nature for LUC mapping of coastal urban plains.

CƏLİLABAD KADASTR RAYONUNUNDA TORPAQ TİPLƏRİNİN COĞRAFİ YAYILMASI VƏ APARILMIŞ İLKİN ÇÖL-TƏDQİQAT İŞLƏRİ

Factors influencing the formation of land cover of Jalilabad cadastral region are one of the reasons for the diversity of soil formation processes in the area. Intra-zonal soils are subject to the laws of vertical zoning due to changes in the height of the area due to its geographical distribution. Despite the fact that the Jalilabad cadastral region does not have a very large area, its separate parts are characterized by differences in bioclimatic and biogeochemical characteristics. The article shows the analysis and geographical coordinates of the main soil types formed in the Jalilabad cadastral region on the basis of a large-scale land map, depending on the soil-ecological conditions. In order to carry out comparative and ecological assessment of soils, we have made land plots in the study area. Currently, the cut samples are in the laboratory stage for physical and chemical analysis in accordance with the methodology. Key words: soil type, mechanical composition, soil structure, soil profile, GPS

Advanced GIS and RS Applications for Soil and Land Degradation Assessment and Mapping

Land refers to the planet’s surface not covered by seas, lakes or rivers, but by different types of vegetation (e [...]

Current State of Soil Fertility Level of the Northern Forest Steppe of the Republic of Bashkortostan

The research aims to study the dynamics of soil fertility of agricultural lands of the Northern forest-steppe. For this, in the process of field surveys, soil sections were laid, a description of the morphological structure with the selection of soil samples by genetic horizons. The field stage is carried out by route inspection, according to the coordinates of the previously designated points; sections are laid out with a description of all genetic horizons for the subsequent determination of the name of the soil. Based on the results of field, a digital soil map of agricultural lands was compiled.

GEOGRAPHICALLY WEIGHTED REGRESSION AND MULTIPLE LINEAR REGRESSION FOR TOPSOIL TEXTURE PREDICTION

Land resource management requires extensive land mapping. Conventional soil mapping takes a long time and is expensive; therefore, geographic information system data as a predictor in soil texture modeling can be used as an alternative solution to shorten time and reduce costs. Through digital elevation model data, topographic variability can be obtained as an independent variable in predicting soil texture. Geographically weighted regression is used to observe the effects of spatial heterogeneity. This study uses a data set of 50 observation points, each of which had soil particle-size fraction attributes and eight local morphological variables. The covariates used in this study are eastness aspects, northness aspects, slope, unsphericity curvature, vertical curvature, horizontal curvature, accumulation curvature, and elevation. Prediction using geographically weighted regression shows more results compared to multiple linear regression models. The spatial location can affect product Y, with the R2 value of 0.81 in the sand fraction, 0.57 in the silt fraction, and 0.33 in the clay fraction.