Zoning of forest health conditions based on a set of soil, topographic and vegetation parameters

2007 ◽  
Vol 248 (1-2) ◽  
pp. 43-55 ◽  
Author(s):  
Dietmar Zirlewagen ◽  
Gerhard Raben ◽  
Markus Weise
2008 ◽  
Vol 155 (3) ◽  
pp. 409-425 ◽  
Author(s):  
Borys Tkacz ◽  
Ben Moody ◽  
Jaime Villa Castillo ◽  
Mark E. Fenn

2021 ◽  
Vol 912 (1) ◽  
pp. 012070
Author(s):  
R Safe’i ◽  
F Ardiansyah ◽  
I S Banuwa ◽  
S B Yuwono ◽  
I R Maulana ◽  
...  

Abstract The surrounding community widely uses mangrove forests as a fulfillment of life. This requires an efforts to preserve the mangrove forest so that no damage occurs. This study aimed to determine the internal factors that affect the health condition of mangrove forests. The research method used to obtain internal factor data is by measuring the ecological indicators of forest health using the Forest Health Monitoring (FHM) method, then the data is processed by the Multiple Regression Analysis method using SPSS 20 through data on internal factors of mangrove forest health which are analyzed for their effect on health conditions of the mangrove forest. The results showed that the significant value of the regression was 0.008 ((α = 0.05) > 0.008), this means that simultaneously the independent variables (tree damage, crown damage, Cation Exchange Capacity-CEC, and biodiversity have an effect on the dependent variable (mangrove forest health) at the level of = 5%. Furthermore, through individual regression coefficients from internal factor data, it is found that the internal factors of biodiversity indicators in measurements 1 and 2 and crown conditions in the second measurement do not affect forest health conditions. Therefore, this research concludes that the internal factors that affect the level of forest health in the first measurement are vitality indicators (tree damage/cluster Plot Index-CLI and crown condition) and site quality indicators (CEC). Meanwhile, in the second measurement, there was a change in the crown condition parameters, which did not significantly affect forest health.


PERENNIAL ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 62
Author(s):  
Rizky Novia Sari ◽  
Rahmat Safe'i ◽  
Dian Iswandaru

Mangrove forests have a special function, namely as a green belt which is certainly very important for human life. Health of forests, especially mangrove forests, is often overlooked regarding their health conditions. The health condition of the mangrove forest is very influential on its sustainability, so to know its health, one of the indicators that can be used is fauna biodiversity. Fauna biodiversity can be known by using the FHM (Forest Health Monitoring) method to determine the diversity and condition of health status. Mangrove forest in Pasir Sakti Sub-District, East Lampung Regency has a diversity of 9 species of birds and 5 types of epifauna diversity. Based on this, the Mangrove Forest of Pasir Sakti District, East Lampung Regency has a good forest health status.


Author(s):  
H. Wang ◽  
Y. Zhao ◽  
R. Pu ◽  
Z. Zhang

In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate <i>Robina pseudoacacia</i> tree health levels. The different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.


2018 ◽  
Vol 10 (9) ◽  
pp. 3308 ◽  
Author(s):  
Fabio Recanatesi ◽  
Chiara Giuliani ◽  
Maria Ripa

Climate change and human activities in particular are important causes of the possible variations in Mediterranean basin forest health conditions. Over the last decades, deciduous oak-forest mortality has been a recurrent problem in central and southern Italy. Despite the perception of increasingly visible damage in oak forests in drought sites, the role of various environmental factors in their decline is not completely clear. Among the modern methods of monitoring terrestrial ecosystems, remote sensing is of prime importance thanks to its ability to provide synoptic information on large areas with a high frequency of acquisition. This paper reports the preliminary results regarding a replicable and low cost monitoring tool planned to quantify forest health conditions based on the application of the Normalized Difference Vegetation Index (NDVI), using the diachronic images provided by the Sentinel-2 satellite. The study area is represented by a peri-urban forest of natural Mediterranean deciduous oaks, characterized by a high variability in the composition of the species and in the silvicultural structures. In order to monitor the health conditions of a specific forest canopy cover with remote sensing data, it is necessary to classify the forest canopy cover in advance to separate it from other species and from the Mediterranean scrub. This is due to the spatial distribution of vegetation and the high rate of biodiversity in the Mediterranean natural environment. To achieve this, Light Detection and Ranging (LiDAR) data, forest management data and field sampling data were analyzed. The main results of this research show a widespread decline in oak health conditions over the observed period (2015–2017). Specifically, for the studied area, thanks to the specific localization of the oak canopy cover, we detected a high potential concerning the Sentinel-2 data application in monitoring forest health conditions by NDVI application.


2021 ◽  
Vol 5 (1) ◽  
pp. 14-27
Author(s):  
Cici Doria ◽  
Rahmat Safe’i ◽  
Dian Iswandaru ◽  
Hari Kaskoyo

Repong damar by the community around the forest is used as an economic support in order to increase income to meet their daily needs, because repong damar can create a series of other economic activities such as harvesting, transporting from gardens to villages, storing, sorting, and trasnporting to wholesalers in the Krui market. This study aims to determine the value of the indicator parameters of productivity and health status of the Repong forest in Pekon Pahmungan, Pesisir Barat. To get this goals, the stages include: determining the number of cluster plots, establishing FHM cluster plots in repong damar, collecting and analyzing data on productivity and final forest health values. The results of this study indicate that the repong damar forest in Pekon Pahmungan, Pesisir Barat Regency has a moderate forest health value based on productivity indicators. This affects the management of community forests in the future with the main function of the forest, namely production. By knowing the value of productivity and forest health conditions, managers can make appropriate forest management decisions..


2019 ◽  
Vol 7 (1) ◽  
pp. 95
Author(s):  
Rahmat Safe'i ◽  
Christine Wulandari ◽  
Hari Kaskoyo

In Lampung Province, awareness of the importance of forest health in achieving sustainable forest management in various types of forests is still low so that forest health problems have not received serious attention so far. This study aims to obtain indicators of forest health assessment and the status of forest health conditions in various types of forests in Lampung Province. This research was carried out in mangrove and community forests in East Lampung District, and protected and conservation forests in Tanggamus District in 2018. The stages of this study consisted of formulating guarantees of forest health indicators, making measuring plots, measuring forest health, processing data, and forest health assessment. The results showed that indicators for assessing the health of forests in mangrove forests are vitality and biodiversity, in community forests are productivity, vitality and site quality, in protected forests are biodiversity, vitality and productivity, and in conservation forests are biodiversity and productivity. The status of health conditions in each cluster of plots in mangrove forest is bad and good, in community forests is good and medium, in protected forests is bad and good, and in conservation forests are bad and good.Keywords: indicator, forest health status, forest types, Lampung Province


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