Identification of human interference and its impact on forest canopy density in the forested areas of Odisha, India

Monitoring the condition of the forest in Indonesia’s New Capital City, Nusantara (IKN), and the surrounding area as a buffer is crucial to realizing the Forest City concept. Forest canopy density can be used to understand and measure forest conditions using satellite imagery efficiently. The main objective of this study was to investigate the spatial-temporal dynamic alterations of forest canopy density across IKN and its buffer. Forest canopy density is based on Landsat 8 imagery for 2015-2020, processed in the cloud using the Google Earth Engine (GEE) and compared using a hemispherical photograph and LiDAR. Google Earth Engine is powerful for creating forest canopy density maps, although Canopy density from Landsat 8 tends to be lower than hemispherical and Lidar, so a correction factor is needed. The correlation between forest canopy density and hemispherical photography can provide valuable insights into the structure and composition of a forest ecosystem. For land types covered with forest, consistently in 2015 and 2020, it dominates dense forest canopy density (>60%). The forest area in the IKN and its surroundings experienced a decrease in forest canopy density by logging. In contrast, some areas experienced increased forest canopy density representing disturbed forest growth.

Forest is an imperative part of environment but in the recent years, forest areas are being transformed due to population expansion, unscientific urbanization and a rising trend of industrialization in some countries. Dense forests habitats have been fragmented into patch forest region. This paper attempts to find out the forest canopy or crown density and forest fragmentation areas as well as to identify the spatiotemporal changing paradigms of forest within the Silabati river basin. Forest Canopy Density and fragmentation models are an important craftsmanship to examine the health of the forest or vegetation in a given area. Various indices such as Normalize Difference Vegetation Index, Advanced Vegetation Index, Shadow Index, Bareness Index and ultimately weightage overlay analysis methods have been adopted to determine forest health or anthropogenic stress on forest habitats. Higher weight has been assigned to dense forest areas and open forest area has been given lower weight. The result shows that forest canopy or crown cover as well as forest density are radically reduced in between the time period 1998 and 2009. It is also stated that the total 116.549 km2 areas have been degraded during 11 years period (1998–2009) with a rate of 10.59 km2/year. Meanwhile, 180.02 km2 forest areas have been regained in between 2019 and 2009 with a rate of 18 km2/year that is possible only due to implementation of forest policies exclusively execution of participatory or joint forest management techniques.

Forest cover is a crucial part of the environment. It makes an essential contribution to the socio-economic and environmental welfare of the Nation. However, these forests are seriously threatened by deforestation, increased mining activity, population growth, uncontrolled urbanisation, a developing tendency of industrialisation, agricultural land purpose, shifting cultivation, effects on soil, water, and biodiversity, unsustainable forms of human activities and others. As a result, developing strategies to promote sustainable forest management, prevent desertification, prevent soil erosion, and halt environmental degradation is essential. Remote Sensing has enabled humans to observe and obtain information about the earth's surface with spatiotemporal changes. The Indian state of Maharashtra's Gadchiroli district is used as a study region. This study investigates forest canopy density and the spatiotemporal changes in forests. The geographical pattern of forest canopy density is displayed by several indices using data from Landsat 5 and Landsat 8 at 30 m spatial resolution. Try to make the study more relevant in the contemporary world. The research area's forest cover has changed through time, as shown by several multi-temporal data sets (1989 and 2019). The results revealed that between 1989 and 2019, forest canopy cover and forest density decreased. It indicated that over 30 years, 1045.51 sq. km of land had degraded. The amount of highly dense forest has decreased significantly over the research period, whereas the non-forest area has been gradually growing for the past 30 years.

Traditional forest inventory methods for obtaining tree stand data in the Batu Serampok Protected Forest Management Unit (KPHL) require significant time and resources. Therefore, remote sensing technology was employed to estimate the potential tree stand density. This study utilized the Forest Canopy Density (FCD) model and the Normalized Difference Vegetation Index (NDVI) from SPOT-6 satellite imagery to assess forest density. Field surveys were conducted to validate the image processing results. Statistical analysis, including correlation and linear regression tests, was performed. Forest density classes were converted into the number of trees per unit area using regression equations. Accuracy tests compared field data with estimated tree stand counts based on vegetation indices. The FCD correlation score was 0.85, higher than NDVI's 0.78, with linear regression results of 0.73 for FCD and 0.62 for NDVI. FCD demonstrated higher maximum accuracy (90.52%) compared to NDVI (84.08%), making it the preferred method for estimating tree stand potential. Overall, FCD reconstruction proved more accurate than NDVI, with the Batu Serampok KPHL predominantly characterized by moderate-density stands.

Chapter 18 - Detection of forest fragmented areas of Sonitpur, Lakhimpur, and Papum Reserve Forest using the FCD model

Monitoring of forest cover is an essential tool for sustainable management of natural resources. Woody green cover can better maintained and managed by identification of forest gaps and their subsequent refilling. Forest canopy density mapping is one of the tools used to identify such canopy openings and most useful parameter to consider in the planning and implementation of afforestation and reforestation program. The present study demonstrate the test‐assessment and practicability of forest canopy density mapping using satellite remote sensing data and biophysical spectral response modelling. Forest canopy density stratification through object oriented image analysis and conventional method of visual interpretation also have been compared with the Forest Canopy Density (FCD) Mapper semi expert system. In this study, forest canopy density is effectively stratified through linear multi‐parametric approach by utilizing advanced vegetation index, bare soil index, shadow index and thermal index. Isadata cluster analysis of forest canopy density map derived from FCD Mapper and conventional methods were shown similar results with respect to percent area of forest and non‐forest. The high percentage (10-30%) occurrence of bushy vegetation like Lantana Camera in ground canopy poses challenge in delineation of forest canopy density as its spectral reflectance is similar to that of the forest.

Although a number of image classification approaches are available to estimate forest canopy density (FCD) using satellite data, assessment of their relative performances with tropical mixed deciduous vegetation is lacking. This study compared three image classification approaches – maximum likelihood classification (MLC), multiple linear regression (MLR) and FCD Mapper – in estimating the FCD of mixed deciduous forest in Myanmar. The application of MLC and MLR was based on spectral reflectance of vegetation, whereas FCD Mapper was operated on integrating the biophysical indices derived from the reflectance of the vegetation. The FCD was classified into four categories: closed canopy forest (CCF; FCD ≥ 70%), medium canopy forest (MCF; 40% ≥ FCD < 70%), open canopy forest (OCF; 10% ≥ FCD < 40%) and non-forest (NF; FCD < 10%). In the three classification approaches, producer's and user's accuracies were higher for more homogeneous vegetation such as NF and CCF than for heterogeneous vegetation density (VD) such as OCF and MCF. FCD Mapper produced the best overall accuracy and kappa coefficient. This study revealed that only spectral reflectance is not enough to get good results in estimating FCD in tropical mixed deciduous vegetation. This study indicates that FCD Mapper, an inexpensive approach because it requires only validation data and thus saves time, can be applied to monitor tropical mixed deciduous vegetation over time at lower cost than alternative methods.

Summary Forest canopy density (FCD), estimated with the FCD Mapper, was correlated with basal area and predominant height (PDH) for 48 field plots, measured in highly variable native eucalypt forest at Toolara, south-eastern Queensland, Australia. The Mapper was produced for the International Tropical Timber Organisation and is available on a CD-ROM. It estimates FCD as an undefined index of canopy density using reflectance characteristics of Landsat Enhanced Thematic Mapper images. The Mapper is a ‘semi expert’ computer program which uses interactive screens to allow the operator to make decisions concerning the classification of land into bare soil, grass or forest. The results of a FCD classification are therefore dependent on the operator's decisions and were found to be highly sensitive to small changes in settings. A positive, weak (r 2 = 0.36) nonlinear relationship of FCD with basal area was observed, while a strong (r 2 = 0.68) similar relationship was observed between FCD and PDH. The strong relationship of FCD with PDH suggests that this remote sensing technique has promise for forest inventory, but that a quick and robust method of measuring FCD in the field is still required for ground truthing.

Remote sensing has the advantage in terms of temporal resolution that can be used to examine changes of the forest canopy density as occurred in Kelud Mountain after the eruption of 2014. Canopy density changes then used as a consideration for forest reclamation priority. This study aims to assess the ability of Landsat 8 multitemporal imagery and Forest Canopy Density (FCD) modeling for canopy density changes at Kelud forest before and after the eruption, as well as take advantage of the canopy density changes from FCD and biophysical condition of forest to make a forest reclamation priority. This research using a Landsat 8 imagery (26 June 2013 and 4 September 2015). The method that used is FCD modeling to obtain canopy density. Forest reclamation priority is determined based on the canopy density change after the eruption and biophysical factors such as slope, soil fertility and native vegetation. Landsat 8 can used to determine the forest canopy density of Kelud before and after eruption with an accuracy of 83.73% and 81.14%. Kelud forest reclamation priorities are divided into nine classes based on priority level. The most prioritized class is 1a with an area of 865 ha and class 1b with an area of 2.085 ha. Then class 1c (0 ha), 1d (413 ha), and 1e that most dominate (5.454 ha). Beside that, there is class 2a (1.900 ha) and 2b (243 ha), and the last is class 3a (467 ha) and 3b (1.172 Ha).ntroduction

This study aims to examine the use of Remote Sensing and Geographical Information System (GIS) technology in land use/land cover mapping to aide sustainable planning and development in the Wafi-Golpu project area. At the same time, this study examines an existing method of Forest Canopy Density (FCD) model to estimate forest canopy density of the proposed deforestation site, which is known as the Advanced Exploration Feasibility Study Activities (AEFSA) area within the Wafi-Golpu Project site. The FCD model calculates the forest canopy density using the three (3) indices of vegetation, soil and shadow from the Landsat-8 Operational Land Imager (OLI) satellite image of year 2013. In this study an attempt has been made to monitor the forest loss or degradation during deforestation in a natural forest stand of the Wafi-Golpu project area using forest FCD mapping and monitoring model and the findings of the study will assist the project planners and developers with their work on forest rehabilitation and reforestation for the purposes of sustainable forest management. The result of the work shows that a considerable amount of forest loss will be undertaken during the AEFSA deforestation exercise and also the findings show that a reliable land use/land cover map will greatly assist sustainable development in a resource project development period.

Mapping the above-ground carbon potential by using a non-destructive method has been a serious challenge for researchers in the effort to improve the performance of natural forest management in Indonesia, particularly in the ex-Mega Rice Project (MRP) area in Central Kalimantan Province. Nevertheless, the rapid and dynamic changes in secondary peat swamp forests are currently mapped effectively with the remote sensing technology using the Forest Canopy Density (FCD) model. FCD analysis as done by integrating vegetation index, soil index, temperature index and shadow index of Landsat 8 OLI images. The result was an FCD class map. In each class, parameter measurements were established for seedling, sapling, poles and tree stages. Above-ground carbon stock was calculated using three allometric equations. The results revealed that the values of carbon stock in ±16,147.26 ha dense secondary peat swamp forest, ±1,509.66 ha moderately dense scrub swamp forest, and ±632.07 ha sparse scrub swamp forest were, respectively, 79.28-122.96; 74.06-113.06; and 40.48-63.60 ton/ha. These results show that FCD application could be used to classify forest density effectively and in line with the variety of their attributes such us aboveground biomass and carbon stock potential.

Coal mining plays a vital role in Indonesia's economic growth. However, these activities negatively impact the environment. To minimize this, the Indonesian government requires ex-mining land to be reclaimed, with one of the success criteria being canopy cover. Until now, there has been no measurable method that can determine the success rate of canopy cover on reclaimed land. This research was conducted to develop a measurement method based on remote sensing data using the Forest Canopy Density (FCD) Model, which is applied in Company X, Kutai Kertanegara. The FCD Model consisted of four biophysical indices, including AVI, BSI, SI, and TI, obtained from Landsat 8 OLI TIRS imagery from 2013–2021. The Kolmogorov-Smirnov normality test was performed before testing the relationship between FCD values and canopy cover using linear regression to obtain the canopy cover success value based on the FCD value. The FCD showed an increasing trend yearly, especially in the first two years after planting. Regression analysis showed a strong relationship between FCD values and canopy cover values, with R2=0.775, and revealed that 75.35 is the FCD value threshold for a successful canopy cover in the reclamation area. This study shows that the FCD approach can be applied to determine the success rate of reclamation in post-mining areas.

Forest canopy density is an important attribute of forest. The correlation of forest canopy density with LANDSAT TM and its derivative data was discussed here. The forest resource field inventory data and simultaneous LANDSAT TM data were used here in Shimian county, Sichuan province, P.R.of China. A lot of derivative data were created from LANDSAT TM data. 1204 forest sub-compartments with inner homogeneity were used as samples for correlation analysis. It was found that both TM7 and the third principal component (P3) of LANDSAT TM data were negatively and significantly (95%) related to forest canopy density on forest sub-compartment. It was also found that TM1, TM2, TM3, TM4, TM5 and TM7 were positively and significantly (99%) related to the forest canopy density when the forest canopy density was not less than 50 percent.

The study examined detection method of forest degradation using forest canopy density (FCD), maximum likelihood, fuzzy and belief dempster shafer classification method. Accuracy evaluation of classification and detection were based on overall accuracy which obtained from 51 ground sample plot. Canopy density, LAI, crown indicator, trees density and basal area (Lbds) were conducted as field indicators. Accuracy of classification among forest density (trees/Ha) with four classification methods were FCD 61%, maximum likelihood 57%, fuzzy 51% and belief dempster shafer 49%. Based on temporal detection accuracy from 2003 until 2008, FCD had overall accuracy 68 %. The result of research, FCD is the best method to detect of forest degradation.

Investigation of forest canopy density has become an important tool for proper management of natural resources. Vegetation cover density can identify the exact forest gaps within a particular area which in turn will provide the appropriate management strategies for future. Forest canopy density has become an essential tool for identifying the exact areas where the afforestation or reforestation programmes needs to be implemented. The aim and objective of this article is to show up the existing density of forest cover using remote sensing and geographic information system tools. Weighted overlay analysis method has been adopted for investigating forest canopy density of Sali river basin, Bankura district, West Bengal. Several indices likewise normalized difference vegetation index, bareness index, shadow index and perpendicular vegetation index etc. have been used for this study. Higher the weight was assigned for greater concentration of vegetation and lower the weight was assigned for lesser concentration of vegetation. Southern part of the region has very high density of forest coverage in comparison with the northern part of the region. It has been observed that 7.48% of the area is at very low density, 12.63% of low density, 24.84% of medium density, 23.92% of high density and 31.13% of very high forest canopy density.