Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST max , LST min ), indicate that LST max and LST min delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI NDWI-LST , was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions.
To evaluate the state of ecosystems in Mauritania, rainfall time series and a GIMMS-NDVI (global inventory modeling and mapping study-normalized difference vegetation index) data set were used for analysis of rainfall and NDVI trends and their relationships in different ecological zones. Linear regression analysis and the non-parametric Mann-Kendall test were applied to detect NDVI and rainfall trends. In addition, the interannual NDVI coefficient of variation (CV) was used to demarcate the borders of the Mauritanian Sahel, and used as an index for land degradation. The results of both parametric and non-parametric methods confirmed the presence of increasing rainfall trends in different ecological zones, except for the Saharan and coastal zones. However, NDVI time series were positive at east Sahel and southeast Senegal River zone. As concluded from trends of rainfall and NDVI, and from CV analysis, the west Mauritanian Sahel and zones west of the Senegal River were characterized by low performance and presence of degradation, while the east Sahel zone, the zone to the southeast of the Senegal River, and patchy areas in the west Sahel exhibited very good land performance and greenness during 1983-2003. The actual borders of Mauritanian Sahel rangeland approximate to the 200 mm isohyet and 0.20 mean NDVI. Land degradation in the Mauritania Sahel can be attributed principally to human activities, and the recent greenness to the increase in rainfall. KEY WORDS: Ecosystems · NDVI · Tendency · Rainfall records · MauritaniaResale or republication not permitted without written consent of the publisher
In recent years, unmanned aerial vehicles (UAVs) have been actively applied in the agricultural sector. Several UAVs equipped with multispectral cameras have become available on the consumer market. Multispectral data are informative and practical for evaluating the greenness and growth status of vegetation as well as agricultural crops. The precise monitoring of rice paddy, especially in the Asian region, is crucial for optimizing profitability, sustainability, and protection of agro-ecological services. This paper reports and discusses our findings from experiments conducted to test four different commercially available multispectral cameras (Micesense RedEdge-M, Sentera Single NDVI, Mapir Survey3, and Bizworks Yubaflex), which can be mounted on a UAV in monitoring rice paddy. The survey has conducted in the typical paddy field area located in the alluvial plain in Tottori Prefecture, Japan. Six different vegetation indices (NDVI, BNDVI, GNDVI, VARI, NDRE and MCARI) captured by UAVs were also compared and evaluated monitoring contribution at three different rice cropping phases. The results showed that the spatial distribution of NDVI collected by each camera is almost similar in paddy fields, but the absolute values of NDVI differed significantly from each other. Among them, the Sentera camera showed the most reasonable NDVI values of each growing phase, indicating 0.49 in the early reproductive phase, 0.62 in the late reproductive stage, and 0.38 in the ripening phase. On the other hand, compared to the most commonly used NDVI, VARI which can be calculated from only visible RGB bands, can be used as an easy and effective index for rice paddy monitoring.
The heterogeneity of savanna ecosystems is guaranteed by disturbance events like fires, droughts, floods and browsing and grazing by herbivores. For conservation areas with limited space to preserve biodiversity, fire monitoring is crucial. Long periods of satellite remotely sensed data provide an alternative solution to estimate the distribution of different vegetation types and fire-affected patches over time. This study focusses on the application of MODIS data to detect, identify and delineate fire-affected areas in Kruger National Park (KNP), South Africa, for the period 2001-2003. Fire scars on KNP's savanna were identified using threshold and supervised classification methods on moderate-resolution imaging spectroradiometer (MODIS) with 500-m resolution and 32-day global composites using a combination of band 1 (red), 2 (NIR, near infrared), 4 (green) and 6 (SWIR, short wave infrared). On identified fire scars, the spectral indexes of albedo, normalised difference infrared index (NDII) and normalised difference vegetation index (NDVI) were extracted. The following four broad habitat types were used for this analysis: riparian woodland, dense woodland, mixed woodland and open-tree savanna. The values of albedo, NDII and NDVI during the dry season (June to October) for different years are lower on fire-affected patches. Mixed woodland is the largest habitat burned with 21%, 43% and 2% of the KNP area affected by fire in 2001, 2002 and 2003, respectively. Riparian woodland is the least affected by fire. The supervised classification method has a greater accuracy for fire scars detection in KNP savannas during the dry season. We conclude that MODIS data can be used successfully for fire monitoring in savanna ecosystems.
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