scholarly journals Analisis Tutupan Mangrove Taman Nasional Berbak–Sembilang melalui Citra Landsat-8 dan Pemantauan LAI

2021 ◽  
Vol 24 (3) ◽  
pp. 393-401
Author(s):  
Tengku Zia Ulqodry ◽  
Andreas Eko Aprianto ◽  
Andi Agussalim ◽  
Riris Aryawati ◽  
Afan Absori
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Area Index ◽  
Sensing Data ◽  
Canopy Coverage

Berbak Sembilang National Park of South Sumatra Region (BSNP South Sumatera) is the largest mangrove ecosystem in the western part of Indonesia. Monitoring of mangrove coverage in BSNP South Sumatera carried out using Landsat-8 imagery data based on NDVI values (Normalized Difference Vegetation Index) integrated with mangrove LAI (Leaf Area Index) data. The research purpose was to analyze the mangrove coverage and mapping the density of the mangrove vegetation canopy with the integration of remote sensing data and LAI. This research conducted field survey with LAI measurement of mangrove canopy coverage and integrated with remote sensing data to validate map. The determination and correlation coefficient of NDVI and LAI value of canopy coverage was high (R2 = 0.69 ; r = 83.07).The results of research indicated that the overall distribution of the mangrove area was 94,622.05 ha. The NDVI image integration map with LAI resulted in 4 mangrove canopy density classes consisted of rare canopy (688.80 ha ; 0.73%), moderately dense canopy (1,139.55 ha ; 1.2%), dense canopy (35,003.46 ha ; 37%), and very dense canopy (57,790.20 ha ; 61.07%). Taman Nasional Berbak Sembilang wilayah Sumatera Selatan (TNBS Sumsel) merupakan kawasan ekosistem mangrove terluas di wilayah Indonesia bagian barat. Pemantauan kerapatan kanopi vegetasi mangrove di TNBS Sumsel dilakukan menggunakan data Citra Landsat-8 berdasarkan nilai NDVI (Normalized Difference Vegetation Index) yang diintegrasikan dengan data LAI (Leaf Area Index) mangrove di lapangan. Penelitian ini bertujuan untuk menganalisis tutupan vegetasi mangrove dan memetakan sebaran kerapatan kanopi mangrove dengan integrasi data penginderaan jauh dan LAI. Penelitian ini menggunakan metode pengolahan data survei lapangan dan hasil pengolahan citra satelit. Nilai koefisien determinasi dan korelasi antara nilai NDVI dengan nilai LAI tutupan Kanopi di Lapangan dikategorikan tinggi (R2 = 0,69 ; r = 83,07). Hasil penelitian menunjukkan tutupan mangrove secara keseluruhan seluas 94.622,05 ha. Peta integrasi citra NDVI dengan LAI mangrove di lapangan menghasilkan 4 kelas kerapatan kanopi mangrove yakni kanopi jarang seluas 688,80 ha (0,73%), kanopi sedang seluas 1.139,55 ha (1,2%), kanopi lebat seluas 35.003,46 ha (37%), dan kanopi sangat lebat seluas 57.790,20 ha (61,07%).

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Estimation of Leaf Area Index Using Remote Sensing Data.

1999 ◽  
Vol 12 (3) ◽  
pp. 210-220 ◽  
Author(s):  
Takashi ISHII ◽  
Makoto NASHIMOTO ◽  
Hisashi SHIMOGAKI
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

Assessment of different methods for estimating forest leaf area index from remote sensing data

2014 ◽  
Vol 34 (16) ◽  
Author(s):  
王修信 WANG Xiuxin ◽  
孙涛 SUN Tao ◽  
朱启疆 ZHU Qijiang ◽  
刘馨 LIU Xin ◽  
高凤飞 GAO Fengfei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

2021 ◽  
Vol 13 (17) ◽  
pp. 9897
Author(s):  
Jinhui Wu ◽  
Haoxin Li ◽  
Huawei Wan ◽  
Yongcai Wang ◽  
Chenxi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Bird Diversity ◽  
Area Index ◽  
Sensing Data ◽  
Animal Diversity ◽  
The Mean ◽  
The Impact

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution.

Sign in / Sign up

Export Citation Format

Share Document