scholarly journals Mapping and characterization of intensity in land use by pasture using remote sensing

2019 ◽  
Vol 23 (5) ◽  
pp. 352-358
Author(s):  
Arthur T. Calegario ◽  
Luis F. Pereira ◽  
Silvio B. Pereira ◽  
Laksme N. O. da Silva ◽  
Uriel L. de Araújo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Weighted Kappa ◽  
Classification Systems ◽  
Land Use Classification ◽  
Livestock Farming ◽  
Climatic Characteristics ◽  
Degree Of Degradation ◽  
Degraded Pastures

ABSTRACT The current demand for food has been met through the exploitation of natural reserves. Brazil has 26% of its extension occupied by agricultural uses, 62% of which are pastures. Degraded pastures have greater land use intensity than well-managed pastures, leading to greater degradation of the environment. Land use classification systems consider that pastures are well managed, a misconception for the Brazilian reality. Based on this approach, it was aimed to develop a methodology for mapping the intensity of land use by pasture via remote sensing. The method of mapping was developed and validated in basins with different soil and climatic characteristics. Three calibrations were performed based on NDVI values to ascertain the influence on the results, being evaluated from the field campaigns and the kappa and weighted kappa indices. The kappa and weighted kappa indices presented reasonable and moderate agreement, respectively. The results were considered as satisfactory for the three calibrations, evidencing that the degree of degradation of the pastures can be estimated in a simple way by remote sensing. The Limoeiro River Basin has around 46.9% of pastures, at least, heavily degraded and 96.6% with some degree of degradation, which contributes to degradation of the natural resources and reduction of livestock farming and economic potential of the basin.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

scholarly journals A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

2021 ◽  
Vol 13 (6) ◽  
pp. 3070
Author(s):  
Patrycja Szarek-Iwaniuk
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
Land Use Classification ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

Combining remote sensing and hydrological information for improving hydrological characterization of dehesas in Mediterranean mountain areas: a study case in Cardeña-Montoro Natural Park (Spain) 

2021 ◽  
Author(s):  
Rafael Pimentel ◽  
Pedro Torralbo ◽  
Javier Aparicio ◽  
María José Pérez-Palazón ◽  
Ana Andreu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Waves ◽  
A Priori ◽  
Regional Government ◽  
Livestock Farming ◽  
Spatiotemporal Resolution ◽  
Mountain Areas ◽  
Natural Park ◽  
Mediterranean Shrubs

<p>Mediterranean mountain areas are especially vulnerable to changes. Climatic trends observed in the last decades point out to an increasing number of extreme events (i.e., number of heat waves and droughts) and consequently, a direct alteration of the hydrological states of their associated ecosystems. The savanna type ecosystem called <em>dehesa</em> is one of them. This system is the result of a long-term co-evolution of indigenous ecosystems and human settlement in a sustainable balance, with high relevance from both the environmental (biodiversity) and socioeconomic (livestock farming, including Iberian pork food industry) point of view. <em>Dehesa </em>systems have a complex vegetation cover structure, where isolated trees, mainly holm oak, cork oak and oak, Mediterranean shrubs, and pastures coexist. Different problems have arisen in <em>dehesa</em> during last years, an example of them are seca episodes, a disease of oak trees that results in drying and final death. This condition is caused by a fungus, but very likely triggered by external hydrological related conditions like air temperature and soil water content.  Remote sensing techniques have been widely used as the best alternative to monitor vegetation patterns over these areas. However, the presence of clouds and the fixed spatiotemporal resolution of these sensors constitute a limitation in more local studies.</p><p>This work proposes the combined use of remote sensing by both terrestrial photography and satelital sensors, and hydrometeorological information as data sources for improving the hydrological characterization of vegetation in <em>dehesa</em> areas. The study was carried out in the Santa Clotilde experimental area, within the Cardeña-Montoro Natural Park (southern Spain). Three years of local sub-daily terrestrial photography and hydrometeorological information allowed us to define different hydrometeorological/ecohydrological indicators that are representative of key vegetation states. This local information is linked with vegetation indexes derived from high spatial resolution satellite information (i.e., Landsat TM, ETM+ and OLI (30 m x 30 m) and Sentinel-2 (10 m x 10 m) and distributed meteorological variables to extend the results from the local to the watershed scale. The promising results will be used in a short future as the basis of an advanced monitoring service where meteorological seasonal forecast information could be used to derive key indicators and help in a priori diagnosis of the system facilitating decisions making.</p><p>This work has been funded by project SIERRA Seguimiento hIdrológico de la vEgetación en montaña mediteRránea mediante fusión de sensores Remotos en Andalucía), with the economic collaboration of the European Funding for Rural Development (FEDER) and the Office for Economy, Knowledge, Enterprises and University of the Andalusian Regional Government.</p>

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Joint Learning of the Center Points and Deep Metrics for Land-Use Classification in Remote Sensing

2019 ◽  
Vol 11 (1) ◽  
pp. 76 ◽  
Author(s):  
Zhiqiang Gong ◽  
Ping Zhong ◽  
Weidong Hu ◽  
Yuming Hua
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Metric Learning ◽  
Land Use Classification ◽  
Training Process ◽  
Pairwise Correlation ◽  
Deep Model ◽  
Training Samples ◽  
Deep Metric Learning ◽  
Learned Features

Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the training samples into consideration, which usually makes the learned CNNs sub-optimal especially for remote sensing scenes with large intra-class variance and low inter-class variance. To address this problem, deep metric learning, which incorporates the metric learning into the deep model, is used to maximize the inter-class variance and minimize the intra-class variance for better representation. This work introduces structured metric learning for remote sensing scene representation, a special deep metric learning which can take full advantage of the training batch. However, the deep metrics only consider the pairwise correlation between the training samples, and ignores the classwise correlation from the class view. To take the classwise penalization into consideration, this work defines the center points of the learned features of each class in the training process to represent the class. Through increasing the variance between different center points and decreasing the variance between the learned features from each class and the corresponding center point, the representational ability can be further improved. Therefore, this work develops a novel center-based structured metric learning to take advantage of both the deep metrics and the center points. Finally, joint supervision of the cross-entropy loss and the center-based structured metric learning is developed for the land-use classification in remote sensing. It can joint learn the center points and the deep metrics to take advantage of the point-wise, the pairwise, and the classwise correlation. Experiments are conducted over three real-world remote sensing scene datasets, namely UC Merced Land-Use dataset, Brazilian Coffee Scene dataset, and Google dataset. The classification performance can achieve 97.30%, 91.24%, and 92.04% with the proposed method over the three datasets which are better than other state-of-the-art methods under the same experimental setups. The results demonstrate that the proposed method can improve the representational ability for the remote sensing scenes.

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