| Full text | |
| Author(s): |
Nascimento, Claudia Maria
[1]
;
Mendes, Wanderson de Sousa
[1]
;
Quinonez Silvero, Nelida Elizabet
[1]
;
Poppiel, Raill Roberto
[1]
;
Sayao, Veridiana Maria
[1]
;
Dotto, Andre Carnieletto
[1]
;
dos Santos, Natasha Valadares
[1]
;
Accorsi Amorim, Merilyn Taynara
[1]
;
Dematte, Jose A. M.
[1]
Total Authors: 9
|
| Affiliation: | [1] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Soil Sci, Padua Dias Ave 11, CP 9, BR-13418900 Piracicaba, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | Journal of Environmental Management; v. 277, JAN 1 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Studies on soil degradation are essential for environmental preservation. Since almost 30% of the global soils are degraded, it is important to study and map them for improving their management and use. We aimed to obtain a Soil Degradation Index (SDI) based on multi-temporal satellite images associated with climate variables, land use, terrain and soil attributes. The study was conducted in a 2598 km(2) area in Sao Paulo State, Brazil, where 1562 soil samples (0-20 cm) were collected and analyzed by conventional methods. Spatial predictions of soil attributes such as clay, cation exchange capacity (CEC) and soil organic matter (OM) were performed using machine learning algorithms. A collection of 35-year Landsat images was used to obtain a multi-temporal bare soil image, whose spectral bands were used as soil attributes predictors. The maps of clay, CEC, climate variables, terrain attributes and land use were overlaid and the K-means clustering algorithm was applied to obtain five groups, which represented levels of soil degradation (classes from 1 to 5 representing very low to very high soil degradation). The SDI was validated using the predicted map of OM. The highest degradation level obtained in 15% of the area had the lowest OM content. Levels 1 and 4 of SDI were the most representative covering 24% and 23% of the area, respectively. Therefore, satellite images combined with environmental information significantly contributed to the SDI development, which supports decision-making on land use planning and management. (AU) | |
| FAPESP's process: | 16/26124-6 - Precision Pedology: Soil characterisation and mapping in real time using geotechnologies. |
| Grantee: | Wanderson de Sousa Mendes |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 18/09656-0 - Relationship between soil degradation and soil classes evaluated by a 35 years Landsat image (Vis-Nir-Swir-Tir), derived from climate and digital elevation model, as to assit on digital soil mapping |
| Grantee: | Claudia Maria Nascimento |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
| FAPESP's process: | 14/22262-0 - Geotecnologies on a Detailed Digital Soil Mapping and the Brazilian Soil Spectral Library:Development and Applications |
| Grantee: | José Alexandre Melo Demattê |
| Support Opportunities: | Research Projects - Thematic Grants |