Forest fire management using machine learning techniques

As per the latest survey produced by the Forest Survey, the forest cover is 19.27% of the geographic area. According to this report every country can meet the human needs of 16% of the world’s population from the 1% of the world’s forest resource. The Forest Survey said that 90% of the forest fires created by humans. They pose a threat not only to the forest wealth but also this leads to the main threat to biodiversity, a change in the ecosystem. The environment gets dry and twinges, which leads to produce flames in the forest. There are two types of forest fire i) Surface Fire and ii) Crown Fire iii) Ground Fire. Surface Fire: The forest fire starts its flame primarily as a surface fire, spreading along the ground with the help of dry grasses and so on. Crown Fire: It starts flame on the crown of the shrubs, bushes and trees and sustained on the surface. This type of fire is very dangerous because resinous material given off burning logs burn furiously. If there is a slope with fire then the fire spread from the top of the slope to the down. Ground fire occurs in the humus and peaty layers beneath the litter of under composed portion of forest floor with intense heat but practically no flame. Such fires are relatively rare and have been recorded occasionally at high altitudes in Himalayan fir and spruce forests. In Remote sensing field, the knowledge of surface temperature plays a vital role. By using brightness and emissivity feature, temperature mapping and analysis can be done. The surface temperature values are measured to detect the forest fire from the ASTER image. ASTER stands for Advanced Space borne Thermal Emission and Reflection Radiometer. ASTER image contains 5 thermal bands (wave length ranges from 8.125 μm to 11.65 μm) and these are used in comparison. To convert digital numbers to exoatmospheric radiance, ASTER thermal bands are used. The converted exoatmospheric radiance is then mapped into surface radiance using the Emissivity Normalization method

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented

L-Band Multi-Polarization Radar Scatterometry over Global Forests: Modelling, Analysis, and Applications

Spaceborne L-band radars have the ability to penetrate vegetation canopies over forested areas, suggesting a potential for regular and frequent global monitoring of both the vegetation state and the subcanopy soil moisture. However, L-band radar’s sensitivity to both vegetation and ground also complicates the relationship between the radar observations and the ecological and geophysical parameters. Accurate yet parsimonious forward models of the radar backscatter are valuable to building an understanding of these relationships. In the first part of this thesis, a model of L-band multi-polarization radar backscatter from forests, intended for use at regional to global spatial scales, is presented. Novel developments in the model include the consideration of multiple scattering within the dense vegetation canopy, and the application of a general model of plant allometry to mitigate the need for much intensive field data for training or over-tuning towards specific sites and tree species. Aided by our model, in the remainder and majority of the thesis, a detailed analysis and interpretation of L-band backscatter over global forests is performed, using data from the Aquarius and SMAP missions. Quantitative differences in backscatter predicted by our model due to freeze/thaw states, branch orientation, and flooding are partially verified against the data, and fitted values of aboveground-biomass and microwave vegetation optical depths are comparable to independent estimates in the literature. Polarization information is used to help distinguish vegetation and ground effects on spatial and temporal variations. We show that neither vegetation nor ground effects alone can explain spatial variations within the same land cover class. For temporal variations during unfrozen periods, soil moisture is found to often be an important factor at timescales of a week to several months, although vegetation changes remain a non-negligible factor. We report the observation of significant differences in backscatter depending on beam azimuthal angle, possibly due to plant phototropism. We also investigated diurnal variations, which have the potential to reveal signals related to plant transpiration. SMAP data from May-July 2015 showed that globally, co-polarized backscatter was generally higher at 6PM compared to 6AM over boreal forests, which is not what one might expect based on previous studies. Based on our modelling, increased canopy extinction at 6AM is a possible cause, but this is unproven and its true underlying physical cause undetermined. Finally, by making simplifying approximations on our forward model, we propose and explore algorithms for soil moisture retrieval under forest canopies using L-band scatterometry, with preliminary evaluations suggesting improved performance over existing algorithms.</p