Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis

2021 ◽  
pp. 146900
Author(s):  
Rita Marcia da Silva Pinto Vieira ◽  
Javier Tomasella ◽  
Alexandre Augusto Barbosa ◽  
Silvia Palotti Polizel ◽  
Jean Pierre Henry Balbaud Ometto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Sensing Data

scholarly journals Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1426
Author(s):  
Ahmed S. Abuzaid ◽  
Mohamed A. E. AbdelRahman ◽  
Mohamed E. Fadl ◽  
Antonio Scopa
Keyword(s):  
Remote Sensing ◽  
Soil Quality ◽  
Land Degradation ◽  
Quality Index ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Western Desert ◽  
Soil Quality Index ◽  
Climate Conditions ◽  
Sensing Data

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

scholarly journals The Design of a Land Suitability Model on Rice Production Estimation using Remote Sensing Method in Merauke District Papua

2020 ◽  
Vol 10 (1) ◽  
pp. 291-295
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Rice Field ◽  
Rice Production ◽  
Land Suitability ◽  
Decision Tree Analysis ◽  
Milled Rice ◽  
Tree Analysis ◽  
Local Residents ◽  
Remote Sensing Methods

The area of agricultural land in Merauke Regency according to data from Bappeda (Agency for Regional Development) of Merauke Regency is for about 4.6 million hectares in 2015 and within the next 5 years will be cultivated as much as 1.2 hectares, especially for rice field and secondary crops plants [1].This study has purpose to estimate the value of rice production by using dry-milled rice with a land suitability approach using decision tree analysis and remote sensing methods in Merauke Regency. Remote sensing that has been corrected geometrically and radio-metrically is analyzed by using decision tree analysis to derive information on paddy/rice and non-rice field land use and which is reclassified by using field unit information based on field observations which will result in accuracy of use and producer. The rice field data is then processed to derive information on the rotation pattern of rice by using a decision tree analysis that is using input data on land characteristics which will produce total accuracy. Information on rice field area and rotation patterns are complemented by land productivity (tons / ha) sourced from BPS (Central Statistics Agency) data and interviews with land cultivators and local residents (farmers) were used to calculate the total rice production value on dry-milled rice The results of calculations by using this method are expected to have a fairly large surplus of calculations, both data from BPS (Central Bureau of Statistics) and data from interviews with land cultivators and local residents (farmers). Thus, these results are expected to show that the land suitability approach by using remote sensing methods for estimating rice production can be used to produce information on rice field area and rotation patterns with moderate to high accuracy.

scholarly journals Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

2010 ◽  
Vol 105 (4) ◽  
pp. 541-548 ◽  
Author(s):  
Flávia T Martins-Bedê ◽  
Luciano V Dutra ◽  
Corina C Freitas ◽  
Ricardo JPS Guimarães ◽  
Ronaldo S Amaral ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Remote Sensing Data ◽  
Minas Gerais ◽  
The State ◽  
Risk Mapping ◽  
Sensing Data ◽  
Tree Approach

scholarly journals Land Degradation Model Based on Vegetation and Erosion Aspects Using Remote Sensing Data

2012 ◽  
Vol 44 (1) ◽  
pp. 19-34
Author(s):  
Adhi Wibowo ◽  
Ishak H. Ismullah ◽  
Bobby S. Dipokusumo ◽  
Ketut Wikantika
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Degradation Model ◽  
Sensing Data ◽  
Model Based

Decision tree regression for soft classification of remote sensing data

2005 ◽  
Vol 97 (3) ◽  
pp. 322-336 ◽  
Author(s):  
M XU ◽  
P WATANACHATURAPORN ◽  
P VARSHNEY ◽  
M ARORA
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Soft Classification

scholarly journals Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011)

2015 ◽  
Vol 7 (4) ◽  
pp. 4391-4423 ◽  
Author(s):  
Sergio Vicente-Serrano ◽  
Daniel Cabello ◽  
Miquel Tomás-Burguera ◽  
Natalia Martín-Hernández ◽  
Santiago Beguería ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Remote Sensing Data ◽  
Drought Indices ◽  
Sensing Data ◽  
Drought Variability ◽  
Semiarid Regions

scholarly journals AUTOMATIC BUILDING EXTRACTION USING A DECISION TREE OBJECT-BASED CLASSIFICATION ON JOINT USE OF AERIAL AND LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 429-434
Author(s):  
R. Ghasemi Nejad ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Spatial Databases ◽  
Remote Sensing Data ◽  
Predictive Performance ◽  
Single Step ◽  
Building Extraction ◽  
Digital Maps ◽  
Sensing Data ◽  
Object Based

Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.

Sign in / Sign up

Export Citation Format

Share Document