Studies on Land Resource Inventory for Agricultural Land Use Planning in Northern Transition Zone of India through Remote Sensing and GIS Techniques

Land suitability analysis is a prerequisite to achieving optimum utilization of available land resources. Hence, a study on land resource inventory for agricultural land use planning was conducted in the Northern Transition Zone of India to determine land capability and develop a suitability map for wheat and sorghum-based on physical and climatic factors of production using remote sensing and GIS techniques. Detailed soil survey information was used for this exercise. Four series (Singhanhalli, Mugli, Bogur and Venkatapur series) were identified and mapped into seventeen mapping units. Land capability classification showed that a greater portion of the study area belonged to class III followed by class IV with limitations of erosion, wetness and varying soil properties. Four land capability classes viz., II, III, IV, and VI, and seven subclasses viz., IIsf, IIItsf, IVs, IVt, IVts, IVtsf and VIt were identified. Major limitations of these subclasses were slope, erosion, depth, texture, coarse fragments, pH, organic carbon and base saturation. Soil suitability assessment revealed that the soils are moderately suitable to permanently not suitable. About 234 ha (31.6%) is moderately suitable, 494 ha (65.0%) marginally suitable and 10.2 ha (1.3%) permanently not suitable for wheat;while 78.5 ha (10.3%) is moderately suitable, 633.4 ha (82.3%) marginally suitable and 32.6 ha (4.3%) permanently not suitable for sorghum respectively. The moderate, marginal and permanent non-suitability was due to moderate, severe and very severe limitations respectively. However, it is possible to achieve potential yield of the crops in the study area if these limitations are addressed.

Development of mobile GIS system for forest resources second-class inventory

A special mobile GIS（Geographic Information System） system used for forest resources second-class inventory was developed on the basis of traditional forest resources inventory,remote sensing,GPS（Globe Positioning System） and embedded technology.Portable instrument,embedded development and the integration technology of RS（Remote Sensing）,GIS and GPS are all used in this special mobile GIS system.Further,the system composition,key techniques,and current situation of the practical application in China were analyzed in the study.The results are important for applying modern high-tech for the planning and design of digital forest resources to improve the precision and efficiency of inventory and reduce the labor cost and financial investment.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Dynamics of forest biomass carbon stocks from 1949 to 2008 in Henan Province,east-central China

We estimated forest biomass carbon storage and carbon density from 1949 to 2008 based on nine consecutive forest inventories in Henan Province,China.According to the definitions of the forest inventory,Henan forests were categorized into five groups: forest stands,economic forests,bamboo forests,open forests,and shrub forests.We estimated biomass carbon in forest stands for each inventory period by using the continuous biomass expansion factor method.We used the mean biomass density method to estimate carbon stocks in economic,bamboo,open and shrub forests.Over the 60-year period,total forest vegetation carbon storage increased from34.6 Tg(1 Tg = 1×10;g) in 1949 to 80.4 Tg in 2008,a net vegetation carbon increase of 45.8 Tg.By stand type,increases were 39.8 Tg in forest stands,5.5 Tg in economic forests,0.6 Tg in bamboo forests,and-0.1 Tg in open forests combine shrub forests.Carbon storageincreased at an average annual rate of 0.8 Tg carbon over the study period.Carbon was mainly stored in young and middle-aged forests,which together accounted for 70–88%of the total forest carbon storage in different inventory periods.Broad-leaved forest was the main contributor to forest carbon sequestration.From 1998 to 2008,during implementation of national afforestation and reforestation programs,the carbon storage of planted forest increased sharply from 3.9 to 37.9 Tg.Our results show that with the growth of young planted forest,Henan Province forests realized large gains in carbon sequestration over a 60-year period that was characterized in part by a nation-wide tree planting program.

The Research of Carbon Emission and Carbon Sequestration Potential of Forest Vegetation in China

The study of China s carbon emissions and carbon sequestration potential is of great practical significance to the formulation of carbon neutrality strategies and methods of China.Carbon emission is an inevitable outcome of the initial stage of economic development,especially the right of developing countries existence and development.The carbon emission of China has been maintained at a low level for a long time and reached the top of the world in 2005.However,per capita carbon emission was still only 46.28%of the United States in 2016.China s total CO_(2) emissions are expected to reach 17-19 Gt/a by 2030.To achieve the goal of carbon neutrality in 2060,the main problems are the low technical capacity of emission reduction and the unreasonable structure of energy consumption.Therefore,replacing coal with gas is one of the most effective ways of emission reduction.By 2060,the carbon sequestration capacity of forest vegetation will reach or exceed 759.14 Mt/a and the CO_(2) sequestration capacity will reach 2783.5 Mt/a.According to that,China s carbon intensity must reduce by 95.39%on the basis of the carbon intensity in 2017,reaching 640 t/100 million yuan.The carbon sequestration capacity of terrestrial vegetation(forest,grassland)will reach or exceed 1380.3 Mt/a and the CO_(2) sequestration capacity will reach 5069.3 Mt/a.According to that,China s carbon intensity must reduce by 91.07%on the basis of the carbon intensity in 2017,reach 1152 t C per 100 million yuan.

Natural and anthropogenic rates of soil erosion

Regions of land that are brought into crop production from native vegetation typically undergo a period of soil erosion instability,and long term erosion rates are greater than for natural lands as long as the land continues being used for crop production.Average rates of soil erosion under natural,non-cropped conditions have been documented to be less than 2 Mg ha^(-1) yr^(-1).On-site rates of erosion of lands under cultivation over large cropland areas,such as in the United States,have been documented to be on the order of6 Mg ha^(-1) yr^(-1)or more.In northeastern China,lands that were brought into production during the last century are thought to have average rates of erosion over this large area of as much as 15 Mg ha^(-1) yr^(-1) or more.Broadly applied soil conservation practices,and in particular conservation tillage and no-till cropping,have been found to be effective in reducing rates of erosion,as was seen in the United States when the average rates of erosion on cropped lands decreased from on the order of 9Mg ha^(-1) yr^(-1) to 6 or 7Mg ha^(-1) yr^(-1) between 1982 and 2002,coincident with the widespread adoption of new conservation tillage and residue management practices.Taking cropped lands out of production and restoring them to perennial plant cover,as was done in areas of the United States under the Conservation Reserve Program,is thought to reduce average erosion rates to approximately 1 Mg ha^(-1) yr^(-1) or less on those lands.

Modelling stratified forest attributes using optical/LiDAR features in a central European landscape

Improvements in the acquisition of three-dimensional(3D)information from the Airborne Laser Scanner(ALS)increase its applications for studying Earth’s surface.The use of ALS data in natural resource inventories is still in an experimental stage in central Europe.Here,a survey was completed in Germany,where plot-level features from LANDSAT Thematic Mapper and ALS data were applied.An automated process was developed for forest stratification using orthoimages.A genetic algorithm was applied for variable screening.Variable subsets of different sizes were employed for simultaneous predictions of structural forest attributes using the‘Random Forest’(RF)method.Performance was assessed by leave-one-out cross-validations on bootstrap resample data.Results indicate that the stratification of forest notably improved the results of predictions.The improvements were more obvious for the strata-related attributes.Accuracy was enhanced as the number of selected variables increased.However,parsimonious models are still essentially required for practical applications.The RF errors were slightly greater than those from least squares regression,as the non-parametric methods do not share the same mix of error components as regression.Through the combination of remote sensing and modelling,we conclude that our results are helpful for bridging the gap between regional earth observation and on-the-ground forest structure.