Describe the method for and produce figure showing NDVI for a bushfire- affected area in Victoria.
To obtain the NDVI for the bushfire around the Victoria area we used the classification methodology through which iso-data clustering was done with the NDVI composites of area of Maryville, where significant bushfires occurred in 2009. By identifying the non-crop clusters through signatures, total Maryville, area in the state was estimated at 34 L ha. Signatures of wheat crop in different parts of the state were analyzed. Decision rules for wheat classification, using multiple NDVI composite images, were developed. Rules were defined based on NDVI progression from November to February to delineate the affected area. These decision rules were applied; Maryville, area was delineated in the Victoria area. Remote sensing images are useful to monitor the development of the vegetation around a burn. We focused on the normalized difference vegetation index (NDVI) defined as the ratio of difference of thered and the near infrared band and the sum of those two bands. It is closely related to the vigor of vegetation.
Figure 1: Landsat image covering the area of bushfire
Describe the method and create time-series graph identifying disturbance and subsequent recovery (after fire) for location in Victoria.
First, we analyzed the number of holes in relation to other aspects of the fire. For analyzed the size of the fires, the sizes of the holes, and the position of the holes with respect to the center. The center serves as one particular point without any physical meaning – in case of stationarity, any other point could have taken as well, but in absence of other data, it seems a logical choice. For this, we used d = the distance between hole and fire center and R = radius of circle with same center and area as fire extent
Provide commentary on the benefits and limitations of Landsat time-series to perform forest monitoring and reporting
Benefits
The main advantages of Landsat are the appropriate localization to capture anthropogenic effects and the short-term size that lasts more than 40 years and that makes for retrospective analysis and long-term interpretation of change. LTS data can capture and reveal sudden, stochastic events as well as subtle changes, while revealing new patterns or processes occurring in forested areas.
The advantage of these classification methods is that a wide range of related information including Landsat bands, their exits, and future data from other data sources can be included as classification variables.
The advantage of this method is that straight-line segments allow for the detection of sudden events such as disruptions, as well as long-term processes such as returns. In addition, no predetermined change model is required because the data itself determines the composition of the traffic. It captures sudden disruptions such as clear or better cuts than methods to detect a two-day change and undergoes subtle changes such as insect-related disturbances and strong growth.
Limitations:
The main limitation of this approach is that the composition of the track must be pre-defined, and the method will only work well if the visual spectral trajectory is similar to another hypothesized trajectory.
The composition is created by the legal process of selecting the “good” pixel recognition among available available candidate images. The rules for choosing the best pixel view can be associated with approaching a target day of the year and avoiding atmospheric disturbances (either cloud, haze, or shadowy shadows), or prioritizing a particular sensor. Those compounds that produce at higher altitudes may experience phenology-related problems, while those that form compounds in near-equatorial areas may encounter problems related to persistent cloud cover (Chatfield, & Reddick, 2016). In addition, due to increased image resolution and additional latitude opportunities for free, cloud or ice free, pixels are improved.
Abstract
This paper focuses on the presence of vegetation patches, called holes remaining after forest fires. Holes are of interest to explore because their vegetation is affected by severe temperature stress nearby, although they can serve as an agent to regenerate a forest after the burn. A statistical analysis of their presence and abundance and a spatial statistical analysis to analyze their positions was done within four forest fire footprints. Fractal dimension of the holes was compared to that of the forest fire foot-print, whereas remote sensing imagery was used to identify the normalized difference vegetation index (NDVI) of the patches before and after the fire
- Introduction
The environment, economic growth and development of an area are highly influenced by Land use with its evaluation and the quality of surface. Land use Plan is a vast sector for Sustainable Development and economical growth of state and also of a city. The main purpose of the study is to examine the possibilities of using Remote Sensing to identify the current ‘state’ of the area and then to suggest a plan for its further sustainable development, alongside an evaluation of the role of remote sensing. This study looks at how a GIS and Remote Sensing is effective to identify the Land use state of the study area.
Methodology
Descriptive statistics
First, we analyzed the number of holes in relation to other aspects of the fire. For analyzed the size of
the fires, the sizes of the holes, and the position of the holes with respect to the center. The center serves
as one particular point without any physical meaning – in case of stationarity, any other point could
have taken as well, but in absence of other data, it seems a logical choice. For this, we used d = the
distance between hole and fire center and R = radius of circle with same center and area as fire extent
(Fig. 4).
Five distributions were fitted for the full database, excluding the fires with no holes:
Descriptive statistics
First, we analyzed the number of holes in relation to other aspects of the fire. For analyzed the size of
the fires, the sizes of the holes, and the position of the holes with respect to the center. The center serves
as one particular point without any physical meaning – in case of stationarity, any other point could
have taken as well, but in absence of other data, it seems a logical choice. For this, we used d = the
distance between hole and fire center and R = radius of circle with same center and area as fire extent
(Fig. 4).
Five distributions were fitted for the full database, excluding the fires with no holes:
Descriptive statistics
This is the methodology we used to analyze the data set where we analyzed the number of holes in relation to other aspects of the fire. For analyzed the size of the fires, the sizes of the holes, and the position of the holes with respect to the center. The center serves as one particular point without any physical meaning – in case of stationarity, any other point could have taken as well, but in absence of other data, it seems a logical choice. For this, we used d = the distance between hole and fire center and R = radius of circle with same center and area as fire extent above. Five distributions were fitted for the full database, excluding the fires with no holes.
the Poisson distribution with parameter ???? specifying both the expectation and the variance (a natural choice, as the variable represents counts);
The negative binomial distribution with parameters p for the probability of an event and n for the number of events, with expectation equal to n⋅p and variance equal to n⋅p⋅(1 − p) (an obvious choice for positive values);
The log-normal distribution with expectation ???? and variance ????2(being the common distribution to model skewed data);
The exponential distribution with cut-off parameter ???? and scale parameter ???? and expectation equal to???? + 1/???? and variance to ????−2(adequate for positive values);
• the Neyman-A distribution with parameters ????1and ????2, and expectations equal to ????1⋅????2and variance to ????1⋅????2⋅(1 + ????2) .
Results
The number of holes for the full database (excluding 0 holes) is given in Figure 2. Clearly, the Neyman-A distribution with parameters ????1= 1, ????2= 0.5 provided the most adequate fit well representing the large density of fires with one hole. Figure 3 shows the descriptive statistics for all the fires in the database. We first note that the log of the sizes of the holes shows a symmetric distribution, with a mode approximately equal to a mean log(hole area) of 12, corresponding to 163,000 m2. Both higher and lower outliers exist, keeping the distribution somewhat remote from a normal distribution. Next, Figure 6b shows the relation between the number of holes and the size of the fires for the full dataset. We notice that there is an increase in the number of holes with increasing area. As large fires are likely have more holes than small fires, because of problems in the detectability of small holes in small fires, we see that also for relatively small fires, the number of holes can be large. However, a strong statistical relation could not be found. Figure 3 shows that the ratio of the area of the hole and the magnitude of the outer extent peaks is relatively close to 0, which is at 0.01. Finally, Figure 6d shows that the median of d/R equals 0.792, that is, the holes are away from the middle of the footprint. We also found that the area of the hole/outer extent equals 0.00045, a very small number, which is there are many small holes. The median of the log (hole area) equals 10.0, corresponding to 22,130 m2: hence, the median size is slightly above 2 ha.
Conclusion
We conclude that holes are inherent in bushfires and burns. They deserve to be studied, as on the one hand they have been under high pressure and on the other hand are part of the new vegetation after the forest fire. The statistical analysis was informative as it showed that the number of holes does not follow a commonly used distribution: neither the lognormal nor the Poisson distribution properly coincided with the observed numbers of holes, whereas the size of holes followed a log-Gaussian distribution. The pattern of holes could have a clear inhomogeneity, with clustering occurring at small distances, possibly as an effect of fire extinction activities, and we observed regularity at larger distances. The fractal dimension of hole boundaries is generally smaller than that of the boundary of the fire. The NDVI of the holes was highly affected by the forest fires, and only very slowly (periods at least longer than six months) are needed to more fully recover.
References
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