The accurate information derived from high accuracy of remote sensing imagery analyses coupled with field observation data are required to develop a sound forest management. The study is mainly emphasized on assessment of the capabilities of remote sensing imageries to identify ecosystem types within the transitional ecosystem. Since, the predominant transition ecosystems found within the study area were secondary forest, rubber jungle, rubber, oil palm plantation, and also other land cover such as mixed plantation and shrubs, therefore, the models developed were focused for those ecosystem types. Prior to any further analysis, this study was initiated to develop the biomass estimation model using 50 m resolution of ALOS PALSAR image in transition ecosystem, Jambi Province. Biomass models were developed by analyzing the relationship between backscatter magnitude and field biomass. Backscatter magnitude from 1 polarization images, namely HH, HV, and one additional band of ratio of HH/HV were analyzed simultaneously with field biomass. The best models established are AGB = 42,069 exp (0.510 HV) and AGB = 1,610 exp (-0.02 HV²) with R² of 52.3% and 50,8%, respectively. The models are then used to map out the biomass distribution within the transition ecosystem and to identify the factors affecting the magnitude of biomass content for all transition ecosystem types.

Logged forests cover four million square kilometers of the tropics, capturing carbon more rapidly than temperate forests and harboring rich biodiversity. Restoring these forests is essential to help avoid the worst impacts of climate change. Yet monitoring tropical forest recovery is challenging. We track the abundance of early-successional species in a forest restoration concession in Indonesia. If the species are carefully chosen, they can be used as an indicator of restoration progress. We present SLIC-UAV, a new pipeline for processing Unoccupied Aerial Vehicle (UAV) imagery using simple linear iterative clustering (SLIC)to map early-successional species in tropical forests. The pipeline comprises: (a) a field verified approach for manually labeling species; (b) automatic segmentation of imagery into “superpixels” and (c) machine learning classification of species based on both spectral and textural features. Creating superpixels massively reduces the dataset's dimensionality and enables the use of textural features, which improve classification accuracy. In addition, this approach is flexible with regards to the spatial distribution of training data. This allowed us to be flexible in the field and collect high-quality training data with the help of local experts. The accuracy ranged from 74.3% for a four-species classification task to 91.7% when focusing only on the key early-succesional species. We then extended these models across 100 hectares of forest, mapping species dominance and forest condition across the entire restoration project.

Critical land occured as a result of land cover changes from vegetated into non vegetated land or the composition of the vegetation has changed. This study aimed to analyze the distribution of land critical at KPHP Unit XII Batanghari. Critical land analysis was based on the Perdirjen PDASHL Number P.3/PDASHL/SET/KUM.1/7/2018. Land is classified into 5 levels of criticality, namely: non-critical, critical potential, somewhat critical, critical and very critical. The parameters used in determining the level of criticality of the land are: land cover, erosion-prone class, slope class and the presence of land inside or outside the forest function. Spatial analysis used by Geographic Information System (GIS) and remote sensing technology. GIS is able to analyze and represent geographic phenomenon. Landsat 8 imagery was analyzed to obtain land cover clasification. The results of the analysis showed that critical land level of KPHP Unit XI Batanghari consisted of 3,609 ha (4.45%) that classified as very critical and 3,599 ha (4,43%) as critical land. Then, land with a somewhat critical level had the largest area, namely 26,024 ha or 32.07% of the total area of KPHP Unit XII Batanghari. The landcover was the main parameter to determine the level of criticality of the land. The openland cover type had the maximum score 60.

Citronella (Cymbopogon nardus L) is a kind of plant producing essential oil. The cultivation does not require special requirements, can be grown on less fertile soil, and able to rehabilitate degraded and critical lands. In this study, 5 levels of drying duration (0, 1, 2, 3, and 4 days) were observed for their effect on the yield of citronella oil grown at 3 critical levels of land (quite critical, critical, and very critical). The experiment was designed in a completely randomized block design with 3 replications. The distillation of whole citronella leaves was carried out for 4 hours using steam distillation method. The result showed that the air drying significantly affected the yield of citronella oil. An increase in drying duration decreased the yield of oil in a linear trend. The highest yield of oil was produced from fresh raw material (0 days drying duration). The decrease of the oil yield due to the increase of drying duration at three levels of critical land (quite critical, critical, and very critical) followed the equation Y1 = - 0.024x + 0.711; Y2 = -0.017x + 0.704; and Y3 = -0.012x + 0.704 (for y = yield [%] and x = drying duration [day]).

Bukit Tiga Puluh National Park is one of the important Nature Conservation Areasin central Sumatra because it has a variety of protected flora and fauna. The aim of this research was to observe the rate of change of land cover in the period 2002-2016 in Bukit Tiga Puluh National Park. The research used remote sensing methods by utilizing satellite imagery data to generate land cover data. This study used the classification of supervised images, where the image classes are self-defined based on field data in the form of coordinate points marked with GPS. The study found that land cover has changed from 2002-2016, where thearea of primary forest has decreased 5.422,80 hectares or with average rate 387,34 hectares/year, secondary forest had an increase of 103,00 hectares or with average rate of 7.35 hectares / year, open land increased 2,243.13 hectares or at an average rate of 160.22 hectares / year, dryland agriculture increased 1,929.69 hectares with an average rate of 137, 83 hectares / year, dryland farming mixed with shrubs increased 641.32 hectares or with an average rate of 45.80 hectares / year, and shrubs increased 505.66 hectares or with an average rate of 36.11 hectares / year. The results of the classification in the management zone, the core zone is dominated by primary and secondary forests while in the jungle zone there is a closure other than forests such as agriculture, shrubs and open land as much as 0.05%.