Transformasi Forest Canopy Density dan Second Modified Soil Adjusted Vegetation Index untuk Monitoring Degradasi Hutan Lindung dan Taman Nasional di Sarolangun Jambi

Tujuan dilakukan penelitian ini adalah (1) menganalisis transformasi FCD dan MSAVI2 untuk memetakan kerapatan kanopi dengan akurasi yang layak diterima, dan (2) mengkaji Perubahan degradasi hutan tersebut secara spasiotemporal di Kabupaten Sarolangun.Metode yang digunakan adalah analisis secara digital data penginderaan jauh yang terdiri dari tiga tahapan. Pertama, koreksi radiometrik citra. Kedua, penerapan transformasi FCD dan MSAVI2. Ketiga, analisis regresi dan uji akurasi.Hasil dari penelitian ini adalah transformasi FCD merupakan transformasi yang lebih efektif untuk memetakan kerapatan kanopi dibandingkan dengan MSAVI2. FCD memiliki akurasi 84,93% sedangkan MSAVI2 sangat rendah yaitu 17,65%. Degradasi hutan terjadi di Kecamatan Batang Asai, Muaro Limun, dan Air Hitam dengan total luasan 435,97 ha atau sekitar 0,67% dari luas hutan, yang terjadi antara tahun 2004-2014

COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE)

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly

Comparing canopy density measurement from UAV and hemispherical photography: an evaluation for medium resolution of remote sensing-based mapping

UAV and hemispherical photography are common methods used in canopy density measurement. These two methods have opposite viewing angles where hemispherical photography measures canopy density upwardly, while UAV captures images downwardly. This study aims to analyze and compare both methods to be used as the input data for canopy density estimation when linked with a lower spatial resolution of remote sensing data i.e. Landsat image. We correlated the field data of canopy density with vegetation indices (NDVI, MSAVI, and AFRI) from Landsat-8. The canopy density values measured from UAV and hemispherical photography displayed a strong relationship with 0.706 coefficient of correlation. Further results showed that both measurements can be used in canopy density estimation using satellite imagery based on their high correlations with Landsat-based vegetation indices. The highest correlation from downward and upward measurement appeared when linked with NDVI with a correlation of 0.962 and 0.652, respectively. Downward measurement using UAV exhibited a higher relationship compared to hemispherical photography. The strong correlation between UAV data and Landsat data is because both are captured from the vertical direction, and 30 m pixel of Landsat is a downscaled image of the aerial photograph. Moreover, field data collection can be easily conducted by deploying drone to cover inaccessible sample plots

Inference of effective soil properties from observed vegetal canopy density

There are no author-identified significant results in this report

Determining density of maize canopy. 2: Airborne multispectral scanner data

Multispectral scanner data were collected in two flights over a light colored soil background cover plot at an altitude of 305 m. Energy in eleven reflective wavelength band from 0.45 to 2.6 microns was recorded. Four growth stages of maize (Zea mays L.) gave a wide range of canopy densities for each flight date. Leaf area index measurements were taken from the twelve subplots and were used as a measure of canopy density. Ratio techniques were used to relate uncalibrated scanner response to leaf area index. The ratios of scanner data values for the 0.72 to 0.92 micron wavelength band over the 0.61 to 0.70 micron wavelength band were calculated for each plot. The ratios related very well to leaf area index for a given flight date. The results indicated that spectral data from maize canopies could be of value in determining canopy density

Forest canopy density analysis of Sokpomba Forest Reserve, Edo State

Forest is a dynamic landscape especially in the tropics as a result of high anthropogenic activities. This study therefore, attempts to evaluate the changes in forest canopy density sequel to the interaction between man and forest ecosystem in Sokpomba Forest Reserve from 1990 to 2020. Relevant Remote Sensing and GIS algorithms were used at different levels of this study. Landsat images formed the major input data for the analysis. In addition to the satellite images, ground control points (GCP) picked with the aid of Global Positioning System (GPS) were used to calculate the accuracy assessment of the Forest Canopy Density (FCD) analysis. The high canopy density (HD) decreased from 320.82km2 in 1990 to 292.82km2 in 2020. Conversely, the low canopy density (LD) increased from 171.12km2 in 1990 to 282.82km2 in 2020. The transitioning of the different Forest Canopy Densities from one category to another was also captured in this study. For instance between 2005 and 2020, about 37 km² changed from low density (LD) to no forest (NF). The accuracy assessment shows that the image classification is good in the sense that the Overall Accuracy figures are 69% (1990), 84% (2005) and 85% (2020). This forest modeling technique is very apt when it comes to the monitoring of forest cover dynamics, forest disturbance and ways of mitigating them. Key words: Geographic Information System, Remote sensing, Forest changes, Landsat, FCD, classification, anthropogenic and  urbanization

Estimation of effective hydrologic properties of soils from observations of vegetation density

A one-dimensional model of the annual water balance is reviewed. Improvements are made in the method of calculating the bare soil component of evaporation, and in the way surface retention is handled. A natural selection hypothesis, which specifies the equilibrium vegetation density for a given, water limited, climate soil system, is verified through comparisons with observed data. Comparison of CDF's of annual basin yield derived using these soil properties with observed CDF's provides verification of the soil-selection procedure. This method of parameterization of the land surface is useful with global circulation models, enabling them to account for both the nonlinearity in the relationship between soil moisture flux and soil moisture concentration, and the variability of soil properties from place to place over the Earth's surface

Estimation of biomass density and carbon storage in the forests of Andhra Pradesh, India, with emphasis on their deforestation and degradation conditions

The current study evaluates the growing stock, biomass and carbon content of Andhra Pradesh state’s forest (India) along with its current status of forest degradation and loss. For this purpose, the study used the growing stock data collected by state forest department in 2010 for the calculation of biomass and carbon storage using the standard conversion and expansion factors given by IPCC. The analysis shows low biomass and carbon values for the state’s forest in comparison to the mean values recorded in different studies made for Andhra Pradesh. It is also observed to be lower when compared with the average carbon and biomass for Indian forests. Overall, the analysis showed degradation and loss of forest in the state, coupled with reduction in biomass and carbon sink

Multiple QTL for horticultural traits and quantitative resistance to Phytophthora infestans linked on Solanum habrochaites chromosome 11.

Previously, a Phytophthora infestans resistance QTL from Solanum habrochaites chromosome 11 was introgressed into cultivated tomato (S. lycopersicum). Fine mapping of this resistance QTL using near-isogenic lines (NILs) revealed some co-located QTL with undesirable effects on plant size, canopy density, and fruit size traits. Subsequently, higher-resolution mapping with sub-NILs detected multiple P. infestans resistance QTL within this 9.4-cM region of chromosome 11. In our present study, these same sub-NILs were also evaluated for 17 horticultural traits, including yield, maturity, fruit size and shape, fruit quality, and plant architecture traits in replicated field experiments over 2 years. The horticultural trait QTL originally detected by fine mapping each fractionated into two or more QTL at higher resolution. A total of 34 QTL were detected across all traits, with 14% exhibiting significant QTL × environment interactions (QTL × E). QTL for many traits were co-located, suggesting either pleiotropic effects or tight linkage among genes controlling these traits. Recombination in the pericentromeric region of the introgression between markers TG147 and At4g10050 was suppressed to approximately 29.7 Mbp per cM, relative to the genomewide average of 750 kbp per cM. The genetic architecture of many of the horticultural and P. infestans resistance traits that mapped within this chromosome 11 S. habrochaites region is complex. Complicating factors included fractionation of QTL, pleiotropy or tight linkage of QTL for multiple traits, pericentromeric chromosomal location(s), and/or QTL × E. High-resolution mapping of QTL in this region would be needed to determine which specific target QTL could be useful in breeding cultivated tomato