LandTrendr 분석 함수

Evaluates changes in pixel values over time using the Landsat-based detection of trends in disturbance and recovery (LandTrendr) method and generates a change analysis raster containing the model results.

LandTrendr 알고리즘에 대한 자세한 내용은 LandTrendr를 사용한 변경 분석 작동 방식을 참고하세요.

비고:

이 래스터 함수는 변경 분석을 사용한 변경 감지 함수와 함께 사용하는 경우에만 지원됩니다. LandTrendr 분석 함수의 결과 레이어를 변경 분석을 사용한 변경 감지 함수에 대한 입력으로 사용합니다. 래스터 데이터셋 결과를 생성하려면 함수 편집기를 사용하여 LandTrendr 분석 함수를 변경 분석을 사용한 변경 감지 함수와 연결하고 이를 래스터 함수 템플릿으로 저장한 다음 래스터 함수에서 래스터 생성 지오프로세싱 도구에 대한 입력으로 사용합니다.

참고

이 래스터 함수는 변경 분석을 사용한 변경 감지 래스터 함수에 대한 입력으로만 사용할 수 있습니다. 래스터 결과를 생성하려면 래스터 함수 템플릿에서 변경 분석을 사용한 변경 감지 함수에 LandTrendr 분석 함수를 연결하고, 이 템플릿을 래스터 함수에서 래스터 생성 지오프로세싱 도구의 입력으로 사용합니다. 결과는 픽셀값이 변경된 시간에 관한 정보가 들어 있는 래스터입니다.

This tool extracts changes in an observed feature, so the ideal input multidimensional imagery should capture a consistent observation throughout time and should not include atmospheric or sensor interference, clouds, or cloud shadow. The best practice is to use data that has been normalized and can be masked using a QA band—for example, Landsat Collection 1 Surface Reflectance products with a cloud mask.

The tool performs analysis on one image per year, and the number of yearly slices must be equal to or greater than the value specified in the Minimum Number of Observations parameter. It is recommended that you have at least six years of data.

If you have monthly, weekly, or daily data, it is recommended that you select several images from each year (preferably from the same season), remove clouds and cloud shadow, and combine the images to generate a single image that captures the observation well. If monthly, weekly, or daily data is provided as the input multidimensional raster, the tool will identify one slice for analysis based on the date closest to that provided in the Snapping Date parameter.

A feature in a landscape will often take time to recover from a nonpermanent change such as a forest fire or insect infestation. To control the rate of recovery recognized by the model, set the Recovery Threshold parameter. A distinct segment cannot have a recovery rate that is faster than 1/recovery threshold.

The recovery from a change in landscape can occur in the positive or negative direction. For example, when a landscape experiences forest loss, a time series of vegetation index values shows a drop in index values, and the recovery shows a gradual increase in vegetation index values, or a positive recovery trend. Specify the direction of recovery trend with the Recovery Has Increasing Trend parameter.

매개변수

매개변수	설명
래스터	입력 Landsat 다차원 래스터 레이어입니다.
처리 밴드 이름	The image band name to use for segmenting the pixel value trajectories over time. Choose the band name that will best capture the changes in the feature you want to observe.
스내핑 날짜	The date used to identify a slice for each year in the input multidimensional dataset. The slice with the date closest to the snapping date will be used. This parameter is required if the input dataset contains sub-yearly data.
최대 세그먼트 수	The maximum number of segments to be fitted to the time series for each pixel. The default is 5.
버텍스 개수 기준선 초과 오류	The number of additional vertices beyond max_num_segments + 1 that can be used to fit the model during the initial stage of identifying vertices. Later in the modeling process, the number of additional vertices will be reduced to max_num_segments + 1. The default is 2.
스파이크 임계치	The threshold to use for dampening spikes or anomalies in the pixel value trajectory. The value must range between 0 and 1 in which 1 means no dampening. The default is 0.9.
복구 임계치	The recovery threshold value in years. If a segment has a recovery rate that is faster than 1/recovery threshold, the segment is discarded and not included in the time series model. The value must range between 0 and 1. The default is 0.25.
최소 관찰 수	The minimum number of valid observations required to perform fitting. The number of years in the input multidimensional dataset must be equal to or greater than this value. The default is 6.
P 값 임계치	The p-value threshold for a model to be selected. After the vertices are detected in the initial stage of the model fitting, the tool will fit each segment and calculate the p-value to determine the significance of the model. On the next iteration, the model will decrease the number of segments by one and recalculate the p-value. This will continue and, if the p-value is smaller than the value specified in this parameter, the model will be selected and the tool will stop searching for a better model. If no such model is selected, the tool will select a model with a p-value smaller than the lowest p-value × best model proportion value. The default is 0.01.
최고 모델 비율	The best model proportion value. During the model selection process, the tool will calculate the p-value for each model and identify a model that has the most vertices while maintaining the smallest (most significant) p-value based on this proportion value. A value of 1 means the model has the lowest p-value but may not have a high number of vertices. The default is 1.25.
1년 복구 방지	Specifies whether segments that exhibit a one year recovery will be excluded. Checked—Segments that exhibit a one year recovery will be excluded. This is the default. Unchecked—Segments that exhibit a one year recovery will be not be excluded.
복구에 증가 추세가 있음	Specifies whether the recovery has an increasing (positive) trend. Checked—The recovery has an increasing trend. This is the default. Unchecked—The recovery has a decreasing trend.
다른 밴드 출력	Specifies whether other bands will be included in the results. Checked—Other bands will be included in the results. The segmentation and vertices information from the initial segmentation band specified in the Processing Band parameter will also be fitted to the remaining bands in the multiband images. The model results will include the segmentation band first, then the remaining bands. Unchecked—Other bands will not be included in the results. This is the default.