Integrating Survey and Remote Sensing Data to Analyze Land Use at a Fine Scale: Insights from Agricultural Households in the Brazilian Amazon

2008 ◽  
Vol 31 (2) ◽  
pp. 115-137 ◽  
Author(s):  
Jill L. Caviglia-Harris ◽  
Daniel W. Harris
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Fine Scale ◽  
Sensing Data ◽  
Agricultural Households

scholarly journals Land use/cover classification in the Brazilian Amazon using satellite images

2012 ◽  
Vol 47 (9) ◽  
pp. 1185-1208 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Guiying Li ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Data Fusion ◽  
Spatial Information ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Brazilian Amazon ◽  
Classification Performance ◽  
Sensor Data ◽  
Sensing Data

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

scholarly journals Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

2015 ◽  
Vol 19 (1) ◽  
pp. 507-532 ◽  
Author(s):  
P. Karimi ◽  
W. G. M. Bastiaanssen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Water Resource Management ◽  
Hydrological Modeling ◽  
Remote Sensing Data ◽  
Actual Evapotranspiration ◽  
Primary Concern ◽  
Suitable Alternative ◽  
Sensing Data ◽  
Water Accounting

Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95% (SD 5%) and rainfall with an overall absolute accuracy of 82% (SD 15%). Land use can be identified with an overall accuracy of 85% (SD 7%). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

scholarly journals Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

2018 ◽  
Vol 153 ◽  
pp. 062050
Author(s):  
Hua Ding ◽  
Ru Ren Li ◽  
Li Shuang Sun ◽  
Xin Wang ◽  
Yu Mei Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

scholarly journals Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

2019 ◽  
pp. 60-65
Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Groundwater Potential ◽  
Groundwater Potential Zone ◽  
Potential Zone ◽  
Ground Truth Data ◽  
Groundwater Availability ◽  
Sensing Data

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

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